esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Evaluation Parameters  
According to the literature review in the field of PCG analysis using an ANN, a comprehensive evaluation necessitates the use of three primary metrics, namely, accuracy, sensitivity, and specificity [21].  
Accuracy serves as a pivotal metric, providing a snapshot of the model's performance by determining the proportion of correct predictions made out of the total predictions. However, with the potential for class imbalance in PCG datasets, the metric's effectiveness may be compromised, necessitating further evaluation.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{21}\]  
Sensitivity, or the true positive rate, steps in to address this, providing a measure of the model's capability in correctly identifying actual heart anomalies, thereby minimizing false negatives. This becomes imperative when dealing with critical health-related outcomes, where overlooked cases could lead to serious misdiagnoses.  
\[Sensitivity=\frac{TP}{TP+TN} \tag{22}\]  
Specificity complements sensitivity, focusing on the model's ability to accurately identify normal cases, thereby minimizing false positives. This is essential to reducing unnecessary medical interventions based on inaccurate diagnoses.  
\[Specificity=\frac{TN}{TN+FP} \tag{23}\]  
Here, TP: true positive; TN: true negative; FP: false positive; FN: false negative. It is crucial to consider these metrics holistically. A model may exhibit high accuracy but compromise on sensitivity, thereby frequently misclassifying positive instances. Alternatively, a model demonstrating high sensitivity but low specificity may contribute to excessive false positives. Therefore, this triad of metrics fosters a nuanced understanding of the model's performance, providing valuable insights to drive refinements in subsequent iterations.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 2 Materials
### Dataset Correlation  
We can find a correlation among the solar panel's parameters by analyzing the telemetry data. Figure 5a shows the correlation between the voltage and the temperature of the +Y panel when it faces the sun for approximately 90 s at varying angles while rotating. Figure 5b shows the correlation between the current and the temperature of the same panel for the same time window. A slight delay can be observed in the current graph compared to the voltage graph. Significant current only flows for approximately 40 s because of a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensor** & **Variable** & **Unit** & **Data Size** \\ \hline LMP8640 & Current & mA & 8 bits \\ AD7490 & Voltage & mV & 12 bits \\ LMT84 & Temperature & \({}^{\circ}\)C & 12 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptions of solar panel sensors.  
Figure 4: One orbit +Y panel power generation and temperature: (**a**) beta angle 15\({}^{\circ}\); (**b**) beta angle 70\({}^{\circ}\).  
specific potential difference required in the circuit between the panel output and the input of the DC/DC converter for the current to start flowing.  
Due to the photovoltaic characteristic of the solar cell, the voltage and current readings are more sensitive to the change in light intensity. However, for the temperature reading, due to the heat transfer from the solar cell to the temperature sensor located behind the panel, a delay is observed for the change in temperature as compared to instant change in voltage and current during the transition from eclipse to sunlight. Hence, the satellite's temperature readings were not utilized in this study due to the low correlations of the trend with the current and voltage samples. From here on, the dataset comprised ten dimensions of current and voltage telemetry data from each panel.
## 6 Algorithms and Frameworks in Federated Learning
### Adaptive Learning Rates in Federated Contexts
#### 6.4.2 Federated Adagrad  
The Adaptive Gradient Algorithm (Adagrad) is an algorithm specifically designed to improve the convergence performance of machine learning models. It achieves this by adapting the learning rate for each parameter individually. Parameters that are updated infrequently receive larger updates, while frequently updated parameters receive smaller updates. This approach helps to address the issue of diminishing gradients, which can slow down or even prevent convergence during training. Federated Adagrad extends this adaptive learning rate mechanism to the decentralized realm of federated learning. In FL, training data are distributed across multiple devices or servers, and models are trained collaboratively without directly sharing the underlying data. Federated Adagrad enables efficient training in such scenarios by allowing each participating device to adapt the learning rate to its local model parameters.  
**Challenges in Federated Settings:**  
* The potential for greater communication overheads as both gradients and squared gradient accumulations might need to be communicated.
* Differences in the magnitude and direction of client updates, due to non-IID data, can cause issues in global aggregation.
## 3 Results and Discussion
### Characterization  
#### 3.2.1 FTIR Characterization of MIPs and NIPs  
The FTIR spectra of both MIPs and NIPs were obtained to investigate the backbone structure of the prepared polymers (Figure 2).  
As the starting materials of the MIP and NIP, such as the functional monomer, cross-linker, initiator, etc., were the same, all FTIR spectra obtained are similar (Figure 2A). The O-H stretching peak at 3569 cm\({}^{-1}\) and the O-H bending vibration at 1388 cm\({}^{-1}\) confirm the presence of carboxylic acid groups. The absorption band at 2854 cm\({}^{-1}\) corresponds to the asymmetrical vibration of C-H bond of the -CH\({}_{2}\) polymer chain. The presence of two significant peaks at 1721 and 1146 cm\({}^{-1}\) is related to C=O and C-O-C stretching vibration, respectively, and shows the existence of EGDMA cross-linker [30]. The -CH\({}_{3}\), -CH\({}_{2}\) deformation vibrations spectra are clearly detected at 1453 cm\({}^{-1}\)[31]. The absence of the MAA and EGDMA C=C double bond stretching at 1644 cm\({}^{-1}\) in MIPs and NIPs indicates that the polymerization process was successful [31].
## 5 Computational Results
### Large VRF Instances  
Note that both \(\mathrm{IG_{ALL}}\) and VBIH algorithms employ the FRB5 heuristic for constructing initial solution whereas \(\mathrm{IG_{RS}}\) uses the traditional NEH heuristic. For the large VRF instances, Table 5 summarizes the ARPD values of heuristic methods such as NEH, NEH without speed-up, denoted as NEH*, and extended NEH heuristic with a local search on partial solutions denoted as FRB5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{\(\mathbf{15\times n\times m}\)} & \multicolumn{3}{c}{\(\mathbf{30\times n\times m}\)} & \multicolumn{3}{c}{\(\mathbf{45\times n\times m}\)} \\ \cline{3-10}
**Instance** & **CP** & \(\mathbf{IG_{RS}}\) & \(\mathbf{  
As shown in Table 5, NEH is very fast with 0.04 s on overall average CPU time. However, its overall average of ARPD is 3.33%. Although FRB5 heuristic is computationally very expensive, which is 43.76 s on overall average CPU time, its average ARPD is only 0.89% from the upper bounds. It is obvious from Table 5 that FRB5 heuristic is substantially better than NEH with a very large margin at the expense of increased CPU time. It is interesting to observe the CPU time performance of the NEH heuristic without the speed-up method of [PERSON]. Table 5 clearly indicates that the [PERSON]'s speed-up method is substantially effective since the overall average CPU time is jumped from 0.04 s to 4.95 s without the speed-up method of [PERSON]. In addition to the above, we present the interval graph of both constructive heuristics in Figure 6 in order for justification. Figure 6 indicates that differences in ARPDs are significantly meaningful on the behalf of FRB5 heuristic since their confidence intervals do not coincide.
# Enhancing Kidney Disease Diagnosis Using ACO-Based Feature Selection and Explainable AI Techniques
## 3 Background Information
### Ant Colony Optimization Algorithm  
ACO is a metaheuristic algorithm that draws inspiration from ants' foraging habits. In ACO, artificial ants replicate the behavior of actual ants in finding food by creating pathways between their colony and the food supply while leaving pheromone trails along the way. Pheromone trails diminish over time but are reinforced through frequent usage by ants, increasing the likelihood that extensively used pathways will be selected by additional ants [29]. ACO is extensively used to address complex optimization challenges across several domains.  
In the context of feature selection, ACO adapts ant behavior to identify the most relevant subset of features from a specified dataset [30]. In this scenario, features are shown as nodes in a graph (G), with ants starting their journey from random features. Each ant probabilistically selects features for inclusion in the subset based on pheromone levels and heuristic information, which indicate the quality of the features relevant to the problem being tackled.  
The probability \(\mathrm{P_{i}^{k}(t)}\) of ant k selecting feature i at time t is determined using the following Equation (1):  
\[\mathrm{P_{i}^{k}(t)}=\{\frac{[\tau_{i}][\mu_{i}]^{\beta}}{\sum_{u\in N_{i}} \left[\tau_{i}\right][\mu_{i}]^{\beta}}\} \tag{1}\]  
where \(\tau_{i}\) represents the pheromone level of feature i, \(\mu_{i}\) is the heuristic information (e.g., feature relevance), and \(\beta\) controls the relative importance of pheromone versus heuristic information. Ants move across features based on these probabilities, reinforcing successful paths as they explore the feature space.  
Once all the ants complete their traversal, a global pheromone update occurs, which adjusts the pheromone levels of features based on their contribution to the solution. The pheromone update rule is given by Equation (2):  
\[\tau_{j}(t+1)=(1-\rho)\tau_{j}(t)+\Delta\tau_{j} \tag{2}\]  
Here, \(\rho\) is the pheromone decay parameter, and \(\Delta\tau_{j}\) represents the pheromone reinforcement for feature j, reflecting the effectiveness of features. This iterative process of probabilistic selection, pheromone update, and reinforcement allows ACO to converge on an optimal or near-optimal set of features, improving the performance of ML models while reducing dimensionality.
## 1 Introduction
Many scientists have used models and methods to retrieve vertical environmental and physiological parameters such as temperature, humidity, PAR, the fraction of absorbed PAR (FPAR), the LAI, and the light use efficiency (LUE) [57; 58; 59; 60; 61; 62]. [PERSON] et al. and [PERSON] et al. [63; 64] also determined the rules of the leaf-scale maximum carboxylation rates (Vcmax25) at different heights. All of these models or methods were developed in specific regions; therefore, it may be difficult to determine the parameters on a global scale. In this article, we introduce three GPP models based on LUE models that are based on the Monteith [65] methods. Monteith's function is calculated as:  
\[\text{GPP}=\text{LUE}\times\text{PAR}\times\text{FPAR} \tag{1}\]where LUE is the daily LUE, and FPAR is the fraction of the absorbed PAR estimated from the LAI and zenith angle. The daily LUE is calculated by different factors that are used to determine LUEmax, and the numerical exercises have specific input parameters. Different models have various parameterizations of environmental stress for LUE; for example, the vegetation photosynthesis model (VPM; [PERSON] et al. [66]) assumes that temperature is of low importance to LUE at optimal growing temperatures, but it is of high importance at high (50 \({}^{\circ}\)C) and low temperatures (0 \({}^{\circ}\)C). The MOD17 (MOD, [PERSON] et al. [4; 67]) model was designed to use remote sensing data for GPP estimation, and this model utilizes a higher stress factor (less than 10% of the LUE for optimal growth) at low daily minimal temperatures (\(-8\)\({}^{\circ}\)C) and a low stress factor (100% of the LUE for optimal growth) at high daily minimal temperatures (9 \({}^{\circ}\)C). The model developed by [PERSON] et al. [63] assumed that high temperatures were important for LUE (more detailed information can be found in the Supplementary Material). However, the models mentioned above did not consider the effect of vegetation stratification on the forest canopy. The goal of this study was to investigate the effect of vertical stratification on remotely sensed GPP estimations in selected dense forest. We raised the following two questions: (a) how do environmental drivers vary with height in multilayer dense forests; and (b) does adding vertical stratification parameters to remotely sensed GPP algorithms improve the performance of the estimations in forest canopies? To address these questions, we used eddy covariance flux data from two sites, including one EBF and one artificial Japanese larch forest, in three remotely sensed LUE models to evaluate the responses of GPP to different factors during the growing season. These sites have meteorological data measurements at multiple heights combined with vertical stratification information, such as temperature, vapor pressure deficit (VPD), and PAR.
## 5 DoS and DDoS Defense
### Attack Mitigation
(2019) propose an SOA-based tracing approach to trace the true DDoS source and filter it. The SOA is placed before the Web server and all requests for the service are marked by the SOA-Based Traceback Approach (SBTA). An algorithm for determining the reconstruction of the path makes it possible to find the true source of attack. The attack messages are then filtered by the Cloud. **Infrastructure. ** Amazon Web Services (AWS) [44] proposes an Infrastructure Layer Defense and an Application Layer Defense against DDoS. The Infrastructure Layer defense (1) uses a resizable compute capacity; (2) chooses AWS Regions for optimal latency and throughput; (3) considers Elastic Load Balancing (ELB); (4) uses a content delivery network (CDN) service and (5) uses a scalable domain name system (DNS) service. The Application Layer defense uses Web application firewalls to protect the vulnerabilities within the application, or to block the unknown source IP addresses, URI, query string, HTTP method, or header key.
# YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring
## 3 Experimental Results and Analysis
### Bionic Mechanical Fish
#### 3.3.3 Visual Recognition  
Fish recognition is achieved using an improved YOLO-PWSL model, which improves the localization accuracy of irregular fish bodies through Wise-ShapeloU, local-global attention fusion using LGFB to capture key features, and PConv to reduce the number of parameters while maintaining the feature expressiveness. The improved model can effectively detect fish in complex situations, thereby interacting with the host computer to match the corresponding environmental conditions for each fish species. This enables the robotic fish to reach the appropriate depth for water quality testing.
## 3 Temperature-Aware Routing Protocols
### LTRT (Least Total-Route Temperature) Routing  
This protocol, named LTRT, or Least Total-Route Temperature [23], is basically a hybrid between the LTR protocol and shortest hop routing algorithm. This protocol uses temperature as its metric, and then, using the shortest hop algorithm, it calculates the route with the lowest temperature metric.  
The algorithm of this protocol is briefly described as follows. First, every nodes collects the temperature information from its neighboring nodes and, then, builds all possible routes to the destination. Then, it assigns temperature as a weight to each intermediate node and builds a weight graph. On this graph, it applies the [PERSON]'s algorithm to solve the single source shortest path (SSSP) problem of the weight graph. The result is a path that has the lowest temperature metric. A sample of this graph is shown in Figure 6. That being said, since the beginning, LTRT tries to look at the end-to-end connection perspective, instead of the connection between two directly connected nodes only. It tries to select the least temperature route from every possible route.
## 5 Results and Discussions
### Signal Fusion for All Actuator-Sensor Paths  
In order to strengthen the crack-interrelated features of the individual original DIs, the environmental noise and measurement uncertainties (unwanted information in individual original DIs) need to be lessened effectively, and the signal-to-noise ratio of the captured crack signal features need to be heightened as much as possible. According to the DI weights of each actuator-sensor path, the extracted original DIs are required to decentralize individual contributions equally and amalgamate all available signal features at each specific crack. A synthetic DI related with fatigue crack is proposed, which can be used to characterize the crack propagation at all paths. Based on the analysis in the time-frequency domain, the hybrid magnitude/amplitude-based DIs (defined by Equations (6) and (7)) and energy-based DIs (defined by Equations (8) and (9)) calculated by the fusion arithmetic are separately illustrated in Figures 11 and 12.  
Firstly, the linear fitting of DIs is conducted at different crack growth regions for each actuator-sensor path. Based on the DI weights of different paths, linear fusion DIs are extracted for all the actuator-sensor paths. The first-order differential is the difference between two consecutive neighbors in a discrete DI function. Differential fusion DIs are obtained by different weights of all the paths. Based on the analysis of linear fusion DIs and differential fusion DIs (including magnitude/amplitude-based and energy-based DIs), Figure 11 severally displays the ultimate DIs in the time domain. Experimental results show that the propagation of the fatigue crack can be calibrated by the synthetic DIs quantitatively. It is found that the fitting differences between the linear magnitude and differential magnitude DIs are especially tiny, and the fitting straight lines are mostly coincident. Moreover, it can also be clearly observed that the stability of linear and differential magnitude fitting DIs is better than that of linear energy fitting and differential energy fitting DIs. Simultaneously, the results indicate that the stabilities of differential energy fitting DIs are the worst in the time domain.  
Figure 11: The magnitude and energy DIs of different fusion methods in the time domain (**a**) Linear and differential fusion magnitude DIs; (**b**) Linear and differential fusion energy DIs; (**c**) Differential fusion of magnitude-energy DIs; and (**d**) Linear fusion of magnitude-energy DIs.  
It can be concluded that the synthetic fusion DIs of different acoustic features can be used to calibrate the propagation of fatigue crack quantitatively to a certain extent. However, considering the stability, reliability, and practicability of the signals, the linear and differential amplitude fusion schemes in the frequency domain are shown to have better promising alternatives for characterizing the crack propagation.
# Thermal Evaluation of a Hybrid Dryer with Solar and Geothermal Energy for Agroindustry Application
## 1 Introduction
### Hybrid Dryer Sizing
#### 1.1.1 Drying Chamber (Solar Energy as Main Source)
\(A\) is the chamber area. \(T_{a,c}\) is the chamber operating temperature and \(T_{a}\) is the outdoor ambient air temperature. To determine the combined convection and radiation coefficient on the outside of the chamber, we applied the equation of [PERSON], where \((he+hr)=hexc\), \(r=5.7+3.8\) va, and va is the ambient air velocity. Heat needed for chamber heating \(Q_{5}\) (kcal)  
\[Q_{5}=\ \left[\left(V*d*C_{p}\right)_{polycarbonate}+\left(V*d*C_{p} \right)_{metal}+\left(V*d*C_{p}\right)_{insulation}\right]\Delta T \tag{6}\]  
where \(V\) (m\({}^{3}\)) is the volume; \(d\) (kg/m\({}^{3}\)) is the density; \(C_{p}\) (kcal/kg \({}^{\circ}\)C) is the calorific power; and \(\Delta T\) is the difference between the ambient temperature and hot air temperature. Heat needed for air renewal \(Q_{6}\) (kcal). \[Q_{6}=\ V_{prod}\ d_{prod}(X_{i}-X_{final})C_{p\ a,\ c}(T_{a,\ c}-T_{a}) \tag{7}\]  
The saturated air is constantly renewed by a fan that will pass through an electrical resistance, allowing the air temperature to be raised to the optimal drying conditions. It should be noted that the complementary electrical resistance system should only be activated in case the climatic affectation has days of low irradiation where the dryer does not reach the operating temperature. Drying velocity  
Once the total heat required to dry 7 kg of wet product is obtained, the air speed required to reach the operating temperature and pass hot air evenly through the trays is determined [10]. \[v_{entrada}\ =\ \frac{Q_{T}}{C_{p}*(T_{a,\ c}-T_{s})x\ (d_{a,\ c}*A)} \tag{8}\]  
where \(Q_{T}\) is the total heat; \(C_{p}\) is the air-specific heat; \(T_{a,c}\) is the hot air temperature in the chamber; \(T_{s}\) is the temperature on the surface of the product; \(d_{a,c}\) is the air density; and A is the hot air inlet area. Dryer thermal efficiency  
The energy efficiency of the drying process is determined from the energy consumption supplied with respect to the evaporated water. The equation commonly used to obtain the efficiency of the dryer is indicated below [11]. \[\varepsilon=\ \frac{Total\ energy\ supplied}{energy\ used\ +\ energy\ lost\ in\ the\ hot\ air\ outlet} \tag{9}\]
# Contrails and Their Dependence on Meteorological Situations
## 3 Results
### Advanced Statistical Methods for the Evaluation of Data
#### 3.2.1 Linear Regression  
The dependences of the contrail lifetime y on the individual variables x(i) were tested using linear regression and correlation. The explanatory variables should be as independent as possible, and the bond between y and x(i) should be as strong as possible. The ranks of variables must be used to test the correlation between discrete variables. The value of the Pearson correlation for y using the correlation coefficients for the individual x(i) is shown in Table 1. The variables are more correlated when the correlation coefficients take on higher values. The highest correlation between y and x(i) was found for dew point temperature, ice point temperature, and temperature. The dependence of the discrete lifetime of the contrails could only be expressed on one explanatory variable using linear regression. The one with the greatest influence on the lifetime of contrails was selected.  
The dependence of the lifetime of the measured and predicted contrails with respect to the relative humidity to ice is shown in Figure 6. The actual values of y were expressed as points and the predictions of y\({}_{\text{p}}\) as a solid line on a graph. It can be seen from the plot that the prediction captures the short-lived contrails up to 1 min very well, while the long-lived contrails above 1 min have a larger scattering in the values. In fact, this pattern is similar to the classification models used below.  
Figure 6: Dependency of measured and predicted contrail lifetime on relative humidity with respect to ice using linear regression.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Experimental Setup
#### 3.1.2 Dataset Description  
Our experimental evaluation utilized load data from the Eastern China power grid, encompassing five administrative regions: Shanghai, Anhui, Zhejiang, Jiangsu, and Fujian. The original load data underwent normalization mapping to preserve the characteristic patterns while ensuring data privacy. The dataset exhibited diverse regional characteristics, with Shanghai showing pronounced commercial and residential load patterns, while the other provinces demonstrated varying degrees of industrial and agricultural influence on their load profiles.