esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Steganography Using LSB and Hybrid Encryption Algorithms
## 3 Method  
The cover images serve as containers for concealing classified multimedia information from potential attackers. In this approach, the hidden capacity of the cover image is divided, with 50% of the storage area allocated for the stego file. Within this allocation, 25% is utilized for inserting the secret message, while the remaining 25% is dedicated to storing decoy data, which is used to test credibility. The stego value, obtained from the image during the upload function, acts as a key for encryption/decryption and serves as a basis for a specific algorithm. The secret message is encrypted using Blowfish and AES methods and embedded in the cover image during the embedding process. The encryption key is derived from the stego value, ensuring that any alterations to the image are capable of preventing successful key extraction and message decryption. The length of the encryption and decryption keys varies depending on the size of the cover image, as it is derived from the mask value obtained from the image. To counter visual and statistical attacks, the secret message is divided into multiple parts and strategically inserted into different segments of the cover image. To minimize noise and maintain the integrity of the embedded message, it undergoes compression before insertion into the image. The process involves calculating the message and image size and determining the segment number and split used within the image. When comparing the cover image with the resulting stego image, the Peak Signal-to-Noise Ratio (PSNR) is used as a measure of similarity. If the PSNR is not identical, the secret message extraction is not performed, as it is contingent on the encryption key derived from the mask value. Additionally, the histogram and weight of the image are calculated to ensure the integrity and confidentiality of the stego image and the embedded secret message.
## 3 Material and Methods
### The EPVIGILA System  
In 2019, Chile implemented EPVIGILA, a healthcare system to monitor and manage the COVID-19 pandemic [12]. This comprehensive tool collects detailed national patient data and serves as a primary information source for government reports. The platform provides real-time disease surveillance and coordinates with a network of stakeholders responsible for monitoring the pandemic. This is crucial for decision-making and implementing actions to protect the population while ensuring compliance with security and confidentiality standards. After the pilot tests, the platform was launched in March 2020, enabling the registration and notification of patients, along with features that allowed for smoother communication among stakeholders. This version played a crucial role by not only registering infected individuals but also monitoring close contacts, aligned with the National Strategy for Testing, Traceability, and Isolation (TTI). Finally, one of the key features of the system is the registration of patients' symptoms and comorbidities, which facilitates more accurate health assessments, leading to better tracking and clinical decision-making.  
EPVIGILA serves as a crucial tool in Chile's COVID-19 surveillance, providing timely data that supports targeted public health actions, such as resource allocation and vaccination efforts. By integrating information from multiple healthcare sources, it offers a comprehensive view of the pandemic's impact. Despite challenges like data quality and class imbalance, enhancements through machine learning improve the reliability of EPVIGILA, strengthening its role in real-time monitoring and effective intervention planning.
## 1 Introduction
Additionally, various indices such as the geoaccumulation index (\(I_{gen}\)), single-factor pollution index (\(PI\)), Nemerow integrated pollution index (\(NIPI\)), potential ecological risk index (\(RI\)), the hazard index (\(HI\)), and the total lifetime cancer risk (\(TLCR\)) have been extensively used to assess the levels of heavy metal pollution in soil and the associated ecological risks [20]. This study integrates \(I_{gen}\), \(RI\), \(HI\), \(TLCR\), and GIS as comprehensive indicators, allowing for a holistic assessment of both soil heavy metal pollution levels, ecological risks and human health risks. Currently, significant attention is being paid to the heavy metal pollution status in the Poyang Lake area of Jiujiang, Jiangxi Province. However, our understanding of the extent and sources of heavy metal pollution in the soils of Jiujiang remains limited. To achieve an objective and comprehensive assessment of soil heavy metal pollution, this study employs integrated indicators such as \(I_{gen}\), the \(RI\), \(HI\), \(TLCR\) and GIS. By integrating these methods, researchers can attain a comprehensive assessment of soil heavy metal pollution levels, ecological risks, and human health risks. Furthermore, PMF, along with correlation and cluster analyses, will be utilized to trace the sources of soil heavy metal pollution while exploring the relationship between contamination and human activities. This combined approach aims to provide targeted recommendations for the prevention and remediation of soil pollution based on the results of source analysis and soil risk evaluations. In conducting an in-depth analysis of the soil pollution status in the research area, this study relies on heavy metal pollution data provided by the dataset of the first national soil pollution survey. Although the data may not be the most recent, its breadth and representativeness offer substantial value in assessing and understanding the distribution characteristics and potential risks associated with soil heavy metal pollution in the region. Additionally, the analysis results will serve as a scientific basis for monitoring, risk assessment, and management decision-making related to soil pollution, thereby contributing to regional ecological environmental protection and sustainable development.
# Conceptual Design and Preliminary Verification of Distributed Wireless System of Weigh-in-Motion
## 4 Preliminary Validation  
### Piezoelectric Sensing Unit Verification  
Four piezoelectric sensor units make up the entire system of vehicle weigh-in-motion. Therefore, each sensing unit needs to be evaluated and validated before the viability of the entire weighing system is tested. The impact load test is created to confirm the sensing unit's viability. The constructed test platform is shown in Figure 12. The main experimental test instruments used are shielding boxes, computers, 6517B electrostatic meters/high resistance meters, data acquisition cards, etc.  
Figure 11: A prototype of circuit.  
Figure 12: The platform for sensing unit test.  
Figure 10: Circuit diagram of 2.8 V–9 V to \(\pm\)5 V.  
A 10 KG weight is put on the sensor in the sensitivity measurement experiment, and the pressure is altered by pushing the hand on the weight. The relationship between the voltage and the pressure is shown in Figure 13. The acquired data interface shows that the impact load induced by applying the weight causes the sensor to swiftly develop a voltage peak. After the impact load is removed, the voltage change curve progressively becomes steady. The voltage signal that the sensor outputs varies quickly as the load changes. The voltage recovers to its value in the static load condition once the external force is withdrawn and is typically steady. As a result, it can be seen that our sensor has great sensitivity and a quick reaction time.  
The sensor and vehicle make contact for around 0.1 s; hence, it is important to confirm the sensor's properties under impact load. The voltage change curve of the sensor under impact load is shown in Figure 14.  
The obtained data interface shows that the voltage value almost tends to zero and keeps aligned in the absence of an impact load. When the impact load is applied to the sensor, a sharper voltage peak is formed. Meanwhile, it is apparent that the voltage peak varies more dramatically for impact loads of various sizes.  
Figure 14: Sensor voltage curve under impact load.  
Figure 13: Output voltage.  
In summary, it can be stated that the sensor proposed in this work fulfills the scheme's requirements since it has quick reaction times, high sensitivity, and the ability to create a relatively visible peak voltage when subjected to impact loads.
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## Appendix A Experimental Setup  
For the attribute features \(X\) of wargaming pieces, feature transformation is firstly performed using a linear (fully connected) layer with an output size of 128. Then, the transformer module uses MSA with 8 attention heads to compute attention weights across sequence elements, capturing long-distance dependencies within the sequence. For the spatial tensor data, Conv2D is used to transform 2D spatial features \(S\) into 1D feature vectors \(S^{\prime}\), with the Conv2D parameters set to 12 kernels, a kernel size of 3, a stride of 1, and a padding of 1. After concatenating the transmissions of the spatial and attribute features, the network is divided into \(N=6\) blocks. Since each side in the wargaming dataset controls 6 pieces, 6 linear layers are utilized in the model to predict the locations of the 6 opponent units, with an output size of 512 for each layer. The model further includes a GRU layer with a size of 512. Finally, a fully connected layer of size 3366, followed by a softmax layer, is used to generate the output tensor for each piece, corresponding to the 3366 hexagonal grids on the map.
# Applying Machine Learning in Cloud Service Price Prediction: The Case of Amazon IaaS
## 2 Related Work
### ML-Driven Approach  
Several works focus on ML-driven cloud service characteristics prediction. Many of them focus on cloud service prices. In [24], the extreme gradient boosting (XGBoost) model was applied to forecast energy-related costs in data centers based on Canadian operator data. Several papers have also addressed resource prediction using ML [25]. In [26], computing resource demand was predicted through various ML algorithms, and tests were performed reflecting the system's yearly performance using AWS data. An innovative sliding window method was adopted to preprocess the data. Spot instance price prediction was carried out using k-nearest neighbors (KNN) in [11]. In [12; 13], the authors adopted several machine-learning approaches to forecast the future pricing of EC2 spot instances, such as linear, ridge, and lasso regressions, multilayer perceptrons, k-nearest neighbors, extra trees, and random forests. Based on the results, machine learning is an encouraging approach for predicting spot prices. In [10], the authors presented a regression random forests model to predict spot prices on an hourly, daily and weekly basis. Low-end instances had better prediction accuracy than high-end instances, indicating that this is a rapidly changing market. Table 1 summarizes existing work on price prediction for cloud services. ML algorithms have been recently utilized for spot instance price prediction, with encouraging results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topic** & **Ref** & **Method** & **Contribution** \\ \hline \multirow{3}{*}{Statistical approach} & [19] & ARIMA & Long-term CPU usage prediction \\  & [20] & Hedonic models & price prediction for laaS service \\  & [22] & Autoregressive (AR), ARIMA & \multirow{2}{*}{Graphics-processing unit (GPU) Spot instance price} \\  & & model, Exponential smoothing & \\  & & (ETS) & \\ \hline \multirow{3}{*}{Machine-learning approach} & [24] & XGBoost & Prediction of energy-related costs in \\  & [25; 26] & Artificial neural network & data centers based on Canadian \\ \cline{1-1}  & [11] & k-nearest neighbors (kNN) & \multirow{2}{*}{Amazon’s spot instance price prediction.} \\  & [12] & Linear, Ridge \& Lasso Regression, \\ \cline{1-1}  & [12] & Multilayer Perceptrons, k-nearest & \multirow{2}{*}{Amazon’s spot instance price prediction.} \\  & [10] & Regression random forest model & \\ \hline \hline \multirow{2}{*}{Current work} & \multirow{2}{*}{CatBoost} & Price prediction of Amazon’s reserved instances \\ \cline{1-1}  & & & \\ \end{tabular}
\end{table}
Table 1: Overview of the Related work.
## 2 Materials and Methods
### Services and Performance Indicators
#### 2.3.1 Service 1: Reduction in Energy Costs (\(EC\))  
The first service is defined as the ability of the SB to provide electricity to its users at a competitive cost with respect to the utility grid. It is always at the top of the list when talking about optimization objectives for EMS in MGs. The associated indicator that allows for quantifying this service (its performance indicator) would be the average cost of electricity. This indicator is computed, as shown in Equation (5), its units are \(\mathbf{\in}\) / kWh and will be hereafter represented by the symbol \(EC\).  
\[EC=\frac{1}{E_{load}^{T}}\cdot\sum_{h=1}^{H}\left(E_{grid}^{h}\cdot C_{grid}^{h }+E_{batt}^{h}\cdot C_{batt}^{h}+E_{pv}^{h}\cdot C_{pv}^{h}\right) \tag{5}\]  
where \(E_{load}^{T}\) represents the total energy consumed by the load during the test period, \(E_{grid}^{h}\) is the energy bought from the utility grid, \(E_{batt}^{h}\) is the energy that is delivered by the battery (battery discharging),\(E_{pq}^{h}\) stands for the PV output energy, while \(C_{X}^{h}\) represents the cost of the energy of the DER \(X\), at the hour \(h\). The cost of the energy coming from the grid \(C_{grid}\) is considered to be known beforehand for every hour of the test period.  
The calculations of the nominal and adjusted costs (accounting for the battery cycling-life reduction and PV curtailment) of the energy coming from the battery and PV panels are presented in Appendix A.1.
## 6 Smart Transportation
### The Improvements of Public Transportation Efficiency  
IoT systems can improve the efficiency of subways, cabs, and shared bikes.  
Regarding subways, [PERSON] et al. [93] introduced an IoT-based train management system that improves the operational efficiency of the railway in Hong Kong. This system can conduct a holistic analysis of the data from multiple sensors, such as the sensors monitoring the temperature of the rolling stock axle counter, rail integrity, and equipment rooms, GPS and RFID monitoring train speed and position, and V-sensor monitoring the vibrations of the bogie. After data analysis, the system will distribute the tasks to the subsystems. For instance, the system may trigger the train's public address system to broadcast passenger information upon arriving or departing a station. [PERSON] et al. [94] proposed a two-stage adaptive model for forecasting passenger flows on large-scale rail systems. The first stage introduces a self-focused prediction-based model that predicts the next day's traffic based on historical data from IoT devices, and the second stage develops a real-time fine-tuning model that adjusts the predicted traffic based on emergencies and short-term traffic variations from physical network devices. They used load sensors to collect data on passenger flow.  
For cabs, optimal rebalancing allows traffic to move passengers in uneven directions with a minimum number of vehicles. However, long waits for cab rides during peak hours can reward quality of service. Therefore, [PERSON] et al. [95] limited the waiting time in the passenger queue to a specified range in optimal rebalancing, which extends the autonomous on-demand mobility solution for fleets of connected autonomous cabs.  
Public bike-sharing systems are the most popular public transportation systems in the world [96]. [PERSON] and [PERSON] [97] proposed IoT and machine learning techniques to optimize the management of autonomous bike-sharing systems in smart cities through a regression integration approach. They found that the Random Forest Regressor, Bagging Regressor, and XGBoost Regressor are more suitable for analyzing the data in the bike-sharing system. The large number of e-fences caused high difficulty and cost in scheduling the shred bikes [98]. [PERSON] et al. [98] proposed a process planning algorithm for electric dispatching vehicles to schedule the shared bikes. This algorithm includes demand prediction (i.e., LSTM-GRU) and scheduling subarea division.
## 4 Results and Discussions
### Case Three (12 V-90 Ah HESS Capacity)
As shown in Figure 22, the proposed HESS is compared with a single 12 V-90 Ah during discharging mode for 3 s. It is clear from the results in Figure 22 that the proposed HESS delivers 653.2 A, whereas the minimum voltage is 11.43 V. While the SLAB delivers 631.7, whereas the minimum voltage is 10.64 V. Figure 23 shows that the LAB's SoC after supplying the required CCA for 3 s is improved to 99.68% and 99.2% for SLAB. Figure 23: The SoC of the LAB in a HESS versus SLAB during discharging mode for case three. Figure 22: The delivered CCC by HESS versus SLAB, and voltage response of LAB versus SLAB during discharging mode for case three. As shown in Figure 23, the LAB SoC in a HESS is maintained at 99.7%, which demonstrates that it loses capacity slowly as compared to a single applied LAB.
## 2 Identification of Abnormal Stations and Estimation of Influence Intensity
### Identification of Stations Affected by Disruptions  
The tap-in passenger flow at stations is an important index to measure whether stations are operating normally. Generally, the tap-in passenger flow at stations fluctuates within a certain range. When the tap-in passenger flow at a station exceeds this range, a disruption may have occurred. Therefore, in order to identify the stations affected by a disruption, it is essential to obtain the fluctuation range of the tap-in passenger flow at stations during normal operation.  
According to the statistical concept of normal distribution [21], if a variable meets normal distribution \(x\sim(\mu,\sigma)\), then the \(3\sigma\) principle must be satisfied as follows:\[P\{\mu-3\sigma<x<\mu+3\sigma\}=99.74\% \tag{1}\]  
where \(\mu\) is the mean value, and \(\sigma\) is the variance.  
After assuming that the tap-in passenger flow at each station in a normal situation meets normal distribution, the fluctuation range of the tap-in passenger flow during normal operation can be determined. Specifically, if the tap-in passenger flow at a station varies within the range \([\mu-3\sigma,\mu+3\sigma]\), this indicates that the station is in a normal situation; otherwise, it is in an abnormal situation.
# Angular Dependence of Photonic Crystal Coupled to Photovoltaic Solar Cell
## 3 Results and Discussion
### Irradiance Spectrum Modification Effect  
According to our results, higher incident angles induced a nondesired performance of the optical properties of the 1 DPC coupled to the solar cell: transparency was significantly reduced, Bragg position shifted to lower energies, and the reflection peak profile was the smoothest, so the main benefits of the photonic crystal dropped. These conditions were critically observed close to dawn and sunset periods, when the irradiance spectrum also changed compared to midday. Scattered light in the atmosphere at these periods of the day modifies the solar spectrum, and this might have affected the optical coupling between 1 DPC and the solar cell too. Despite incoming energy on the solar cells at these periods being low for the selected location, this effect should be considered at other locations far away from the equator region.  
To determine the criticality of these two parameters (irradiance incident angle and solar spectrum shifting), two identical solar cells, with and without PC1D coupled, were located on a 2-axis tracking to measure again the photocurrent generated. The tracker modified its position quickly, from \(-70^{\circ}\) to \(70^{\circ}\), monitoring all measurements in a short period of time, thus allowing us to assume that the solar spectrum did not change significantly during the test.  
Results obtained from this test were compared with those obtained from the fixed structure (Figure 7). Data support the previous discussion: when the incident angle was higher than 50\({}^{\circ}\), the losses observed in the solar cell coupled with the 1 DPC were significant. At these operational conditions, the expected benefits of the crystal photonics are converted into undesired drawbacks. Effects derived from the irradiance spectrum shifting at dawn and sunset were secondary when compared with the Bragg peak position and its consequence for the optical coupling of the 1 PCD. It is  
Figure 6: Experimental dependence observed for the optical coupling losses as function of the incident angle.  
also important to point out that monitoring data from the samples installed on a fixed structure or into a 2-axis tracking system were coincident.  
Thus, we have demonstrated that, according to [PERSON]'s law, the photocurrent generated by the photovoltaic device is proportional to the cosine of the angle of incidence of the incoming radiation. However, when optical elements, such as the photonic crystal, are considered in the photovoltaic prototype design, predicted losses by [PERSON]'s law must be updated to consider the optical decoupling between both elements. This combined effect predicts a reduction in the irradiation cone to maximize the photocurrent of the solar cell under outdoor conditions.
# Energy Efficient Clustering Protocol for FANETS Using Moth Flame Optimization
## 4 Experimental Results and Analysis
### Energy Consumption  
In UAVs, energy is one of the most limited resources. Due to a small dry-cell battery, it is possible to fly micro-UAVs for just a few minutes, which is from 25 and 30 min. Due to their low battery power, these unmanned aerial vehicles (UAVs) have several challenges. To maximize the lifespan of a UAV and increase probability of success, it is essential to maximize energy efficiency. In UAVs, three processes are responsible for energy loss: actuating the motor control system, energy used by various sensors, and energy used for inter-UAV communication. UAVs uses most energy since they communicate with each other. The total amount of energy is used in communication, including transmission and reception, can be computed using Equations (6)-(9).  
\[\mathrm{Energy_{total}}=\mathrm{Energy_{com}}+\mathrm{Energy_{ flying}}+\mathrm{Energy_{sensors}} \tag{6}\]  
\[\mathrm{Energy_{com}}=\mathrm{Energy_{Tx}}+\mathrm{Energy_{Rx}} \tag{7}\]  
\[\mathrm{Energy_{Tx}}=\mathrm{Energy_{TRC}}\times\mathrm{k}+\mathrm{Energy_{ A}}\times\mathrm{k}\times\mathrm{D}^{2} \tag{8}\]  
\[\mathrm{Energy_{Rx}}=\mathrm{Energy_{TRC}}\times\mathrm{k} \tag{9}\]  
Here energy consumption refers to the sum of energy consumed during transmission and reception of data. While running transmitter and receiver circuits, \(\mathrm{Energy_{TRC}}\) is the energy dissipation. \(\mathrm{Energy_{A}}\) is the amount of energy used by the transmit amplifier to send data to the other end. The number of bits sent and received is represented by \"L,\" whereas the distance between the sending and receiving nodes is represented by \"d.\" \(\mathrm{Energy_{ flying}}\) is energy consumed for flying a node, in this scenario we are using homogenous nodes so there would be same energy consumed for flying by all nodes.  
Here whole energy consumption refers to the sum of energy consumed during transmission and reception of data. By using Equation (6), we can calculate the total energy. In our case we are using homogenous nodes so there would be same energy for flying in all nodes.  
We calculate the whole energy consumption by EECP-MFO, PSO, ACO, and GWO over the course of 120 s. As the number of UAVs and the grid size increase, the network's energy usage also rises. Figures 12-14 clearly shows that EECP MFO beats the other three methods. The lower EECP MFO energy usage is since the right CHs were chosen, and the CH number is within the optimal limit.  
Figure 12: Energy consumption vs. number of nodes (1000 \(\times\) 1000 m\({}^{2}\)).