esa-sceva/satcom-chunk-collection huggingface dataset:
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 3 Optimization Model
### Mathematical Expression of Optimization Model  
The optimization problem of the mobile photovoltaic-diesel-storage microgrid system can be described using symbols as follows:  
(1) Objective function:  
\[Min\ Z(N)=(LPSP(N),Weight_{total}(N)) \tag{11}\]Constraint conditions:  
\[\left(N_{pv},N_{bat1},N_{bat2},N_{dg}\right)>0 \tag{12}\] \[SOC_{min}\leq SOC\leq SOC_{max}\] (13) \[0.1P_{bat2\_min}<P_{bat2}<P_{bat2\_max} \tag{14}\]  
where \(Z\) represents the objective function; \(LPSP\) and \(Weight_{total}\) refer to the power outage rate and weight, respectively; the decision variable \([N_{PV},N_{bat1},N_{bat2},N_{dg}]\) denotes the number of photovoltaic panels, primary batteries, secondary batteries and barrel diesel carried in the scheme.  
Formulas (13) and (14) are constraints for primary and secondary batteries. The optimization goal of a battery is to discard it as much as possible after exhausting its battery capacity. Thus, its state of charge constraint should be within \([SOC_{min},SOC_{max}]\), a closed interval on both sides. For secondary batteries to optimize their service life, their charging and discharging depth should be limited. To simplify the model, its use must be stopped when the power level is reduced to 10% of the maximum output.
## 4 Major Worldwide Cyberattacks Against Energy Systems
### US Colonial Oil Pipeline  
The Colonial Pipeline, the largest fuel pipeline company that runs from the Gulf of Mexico to New York and considered to be a main fuel supply artery, was the target of a ransomware cyberattack in 2021. As a result of the attack, the supply of gasoline and jet fuel to the USA's East Coast were disrupted, where the company proactively shut down its pipeline system in response to the attack. The main reason of the shutdown was that the company wanted to ensure the ransomware did not spread to the OT (operational technology) environment. In addition, the injected malware locked up a large number of corporate systems including billing systems, which complicated the mechanism to produce bills and receive payments [75].  
Using authentic credentials belonging to a Colonial Pipeline employee, cybercriminals accessed a company's VPN on May 6 (one day before the attack). Although the VPN account was dormant and not intended for usage, the credentials were still valid and allowed the hackers to access the network. The employee probably used the same login information on different websites, and the perpetrators got hold of the password through a different data breach. It is uncertain how the VPN login came into the hands of the cybercriminals. Another measure that made the breach easier is that multifactor authentication (MFA) was not supported by the VPN. According to incident investigation, the attack was the result of a single compromised password that allowed them to inject a type of malware (ransomware) that encrypted company's computer data and kept it hostage until the ransom cryptocurrency was transferred. Additionally, the hack group (called Darkside) also exfiltrated more than 100 GB of the company's sensitive data. So, they used a double extortion tactic by stealing data and threatening to leak it while asking for a ransom [76; 77].  
To mitigate the breach consequences, Colonial Pipeline started negotiating with Darkside hack group and paid hackers a USD 4.4 million ransom, in the bitcoin cryptocurrency, a day after finding the malware on its systems. Even with this fast response and paying the ransom, Colonial Pipeline took one week to restart their entire pipeline system and commence product delivery to all markets [78].
## 2 Materials and Methods
### Test Samples
#### 2.2.6 Determination of the Angle of Repose  
The repose angle was measured using a cylindrical lift apparatus (Figure 4) [16]. The experimental setup involved loading wheat straw feed into a steel cylinder (inner diameter: 90 mm; height: 180 mm), which was vertically lifted by a CMT-6103 microcomputer-controlled electronic universal testing machine at a constant velocity of 0.6 m/s. This lifting speed, optimized through preliminary trials, ensured stable formation of the material pile on a horizontal baseplate.  
High-resolution vertical images of the pile were captured for subsequent processing in MATLAB R2022b. The workflow included the following:  
1. Grayscale Conversion: Raw images (Figure 5a) were converted to grayscale (Figure 5b) to enhance contrast.
2. Binarization: An iterative threshold segmentation algorithm was applied to generate binary images (Figure 5c), isolating the pile from the background.
3. Boundary Extraction: The bwperim function detected the pile perimeter, followed by morphological filling (imfill function) to eliminate internal noise and obtain a continuous closed boundary (Figure 5d).
4. Slope Calculation: Boundary coordinates extracted using Origin's Digitizer tool were linearly fitted, and the repose angle was derived from the slope of the fitted line.  
Figure 4: Stacking angle physical test. Note: 1. Universal material tension and pressure testing machine; 2. Fixture; 3. Cylinder; 4. Stacking angle; 5. High-definition camera; 6. Computer.  
This method achieved high precision in quantifying the repose angle, with results validated against physical measurements, ensuring robustness for DEM parameter calibration.
## 5 Load-Bearing Mechanical Characteristics of Coal Under Sprayed Coating Conditions  
### Analysis of Laboratory Experiment Results  
A hysteresis curve of the polyurea-coated coal sample was obtained from uniaxial compression tests (see Figure 9). The axial loading and unloading process of the polyurea-coated coal specimen can be divided into three stages: the compaction, elastic, and failure stages. The results of the first two stages show no significant differences from those of the axial loading of the standard coal specimens.  
At the compaction stage, the initial pores within the coal specimen gradually close under the applied load and the elastic modulus of the coal increases rapidly with rising strain. As the strain increases (approximately 0.3%), the specimen enters the elastic stage, where stress and strain exhibit a linear relationship. Here, the specimen's uniaxial compressive strength is approximately 25 MPa. Because the polyurea coating has a thickness and elastic modulus (1.27 MPa) much lower than those of the coal body, the influence of the polyurea coating can be ignored during the compaction and elastic stages.  
At the failure stage, the coating allows for the coal sample to retain a high residual strength even after brittle failure, exhibiting significant plastic characteristics. During the experiment, after the coal specimen underwent brittle failure it continued to undergo displacement loading until the strain reached 4%, at which point the residual strength was found to be approximately 5 MPa. The specimen was unloaded using a stress control method. Although the specimen recovered partially from the deformation, approximately 3% of the deformation remained and the unloading process displayed nonlinear characteristics.  
Figure 9: Uniaxial compression loading and unloading stress–strain curve of polyurea-coated coal specimen.
## 4 Case Analysis
### Protection System State Evaluation and Fault Prediction in a Single Case
As can be seen from Figure 9, at moments 1-3, the relative closeness of the device to the normal state was one, so it was judged to be \"normal\". The proximity of the equipment to the attention state increased at moment 3, which indicated that the equipment had a certain trend of deterioration. The status level of moment 4 changed from \"normal\" to \"attention\", and the status level of moment 6 changed from \"attention\" to \"severe\". This was because during this period of time, the previous hidden problems had not been eliminated, and became more and more serious, and were very likely to evolve into a fault. After comparing with the field equipment status maintenance records, the status change trend was consistent with the actual operation of the equipment, which verified the accuracy of the status assessment model. (3) When the equipment state was \"severe\", i.e., moment 6, a fault prediction process was initiated for the equipment. According to the clustering centers of the nine typical faults described in Table 7, the relative correlation degree was calculated by calculating the  
Figure 8: Change of characteristic weight at each moment. Figure 9: Variation of the closeness of the protection system to each state level at each moment. Euclidean distance between the characteristic sequence of moment 6 and each clustering center using Equation (13), as shown in Figure 10.  
It can be seen that the relative correlation degree between the device and the fault type 1 at this moment was one. Therefore, the fault prediction result was a failure of the main DSP or sampling DSP module of the merging unit. After comparing with the actual fault at the equipment defect processing records, the fault type was correctly determined. It was verified that the algorithm could accurately predict the type of fault that would occur when the device was in poor condition. (4) The fault records were recorded in the history database, and the history database and clustering center were updated, and the fault set was updated periodically according to the actual situation.
## 5 Complexity and Latency
### Latency of the Proposed Model  
We considered the testing process and focused on three 1D detections (using the VA). We can see that three 1D detections take more than three times as long. To solve this problem, we proposed a method to reduce latency. With three data pages from three equalizers, we proposed arranging the order of detection for each track to help implement parallel detection.  
Figure 14 presents the tables of the data pages, and tracks are assigned from 0 to 9. First, we detected tracks 0 and 2 during the detection of the 2-way GPR target. Then, we can supply the interference information from tracks 0 and 2 for the detection of the 4-way GPR target, and we can detect track 1 in the detection of the 4-way GPR target. At the 3 rd time, track 4 can be detected in the 2-way GPR target. Therefore, at the 4 th time, we can detect track 3 in the 4-way GPR target. When we have tracks 1 and 3, we can detect track 2 in the 8-way GPR target at the 5 th time.  
We assumed that one data page has \(n\) tracks, and for each track, we spend \(m\) seconds for detection. Consequently, the data in the detection for the 8-way GPR target are delayed five units of track time compared to the detection for the 2-way GPR target. Specifically, if we detect one data page in the detection of the 2-way GPR target with a time of \(nm\) seconds, we spend a time of \((n+5)m\) seconds to detect that data page for the 8-way GPR target. We can see that our proposed scheme delays by a factor of \(\{(n+5)m\}/(nm)\) compared to one detection. When the parameter \(n\) is very large, we can ignore this delay in the proposed scheme.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Task Model  
#### 3.4.1 Delay in Task Deployment  
The distance between LEO satellites is constantly changing. Therefore, the communication between satellites needs to consider the influence of distance. The link length \(D\) between the satellites can be calculated from the positions between satellites and presents periodic changes when the constellation is fixed. Assume that \(D(t)\) is the function of inter-satellite distance changing with time, and the communication delay can be expressed as:  
\[t_{d}=\frac{D(t)}{c} \tag{3}\]  
where \(D(t)\) is a function which can be generated by the constellation operation law. In (3), \(c\) is the transmission speed of the electromagnetic wave in a vacuum.  
The delay of a single transmission is \(t_{d1}\), \(t_{d2}\)... \(t_{dn}\), respectively. Therefore, the delay of a single subtask deployment is:  
\[t_{b}=t_{d1}+t_{d2}+\ldots+t_{dn} \tag{4}\]  
Tasks are composed of subtasks, and the deployment delay of a single subtask is \(t_{b1}\), \(t_{b2}\)... \(t_{bn}\), and the deployment delay of task \(Z_{n}^{1}\) is:  
\[Z_{n}^{1}=\max\{t_{b1},t_{b2}\ldots t_{bn}\} \tag{5}\]
## 4 Results
### The Effect of More than Three Factors
#### 4.4.1 The Effect of Factor Quartets  
When investigating the effect of factor quartets all possible (864) combinations are made of them. Afterward, they were compared to each other. These combinations and their Intercept variables are the following as presented in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Model I, II** & **Model I, III** & **Model II, III** & **Optimal Model** \\ \hline FoV \& ROT \& Display device & \(1.442\times 10^{-8}\) & \(7.337\times 10^{-6}\) & 0.9697 & II \\ FoV \& ROT \& CR & \(1.639\times 10^{-8}\) & 0.000325 & 0.8916 & II \\ FoV \& ROT \& Shadows & \(4.022\times 10^{-14}\) & \(4.344\times 10^{-10}\) & 0.4625 & II \\ FoV \& Display device \& CR & \(4.528\times 10^{-6}\) & 0.0001402 & 0.6667 & II \\ FoV \& Display device \& Shadows & \(0.052120\) & 0.52680 & 0.9770 & I \\ FoV \& CR \& Shadows & \(1.095\times 10^{-6}\) & \(2.962\times 10^{-5}\) & 0.2415 & II \\ ROT \& Display device \& CR & \(1.503\times 10^{-5}\) & 0.0008141 & 0.8482 & II \\ ROT \& Display device \& Shadows & \(4.469\times 10^{-5}\) & 0.0004081 & 0.7247 & II \\ ROT \& CR \& Shadows & \(0.00682\) & 0.08343 & 0.704 & II \\ Display device \& CR \& Shadows & \(0.261\) & 0.5767 & 0.7253 & I \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of models (\(p\)-values).  
According to the results, 230 significant quartets were found out of all possible 864 quartets. This means that 26.62% of the combinations are significant. However, only 6 combinations decrease the completion times significantly, while the remaining ones increase it. The greatest significant decrease is when 60\({}^{\circ}\) FoV, 45\({}^{\circ}\) camera rotation, a desktop display is used without shadows.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 3 Materials and Methods
### Key Performance Indicators
#### 3.4.1 Error Vector Magnitude (EVM)  
The EVM is defined as the measure of how far the actual transmitted signal deviates from the ideal or reference signal in a constellation diagram. In simpler terms, it measures the difference between the ideal signal points and the points where the actual received signal ends up due to various imperfections in the transmission process [8]. The equation for calculating the EVM is given by  
\[\text{EVM}(\%)=\frac{\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}\left|S_{\text{ref} }[i]-S_{\text{received}}[i]\right|^{2}}}{\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N} \left|S_{\text{ref}}[i]\right|^{2}}}\times 100 \tag{2}\]  
where \(S_{\text{ref}}[i]\) is the ideal symbol or the reference symbol at the \(i\)-th symbol and \(S_{\text{received}}[i]\) is the \(i\)-th received symbol. \(N\) is the total number of complex symbols.
## 3 Materials and Methods  
### Pest Image Dataset  
The publicly available IP102 dataset is adopted as the reference dataset [44]. The large-scale dataset is composed of more than 75,000 images belonging to 102 different pest categories with a distribution reflecting their presence in nature. The above-mentioned classes are split into 8 crop typologies according to the crop that the insect class damages more (rice, corn, wheat, beet, alfalfa, vitis, citrus, and mango). These typologies are gathered into 2 super-classes based on the kind of corrupted crops called economic and field crops. The dataset contains RGB images at varying pixel resolutions which have been collected from online resources.  
The IP102 is a challenging benchmark dataset not only because of its high degree of imbalance but also because images have illumination changes, a broad range of perspective, scale, and orientation. Moreover, several photos are characterized by occlusion and clutter. The difficulty encountered in object detection and recognition in adopting the IP102 dataset is also due to the presence of different pest life stages within the same category (such as larvae, caterpillars, and butterflies).  
The dataset provides two options of images and annotations, meant to be used for different pest recognition tasks. In particular, the first collection of 75,222 images can be exclusively used for classification tasks because each image is labeled by species alone and contains a single pest instance. The second collection consisting of 18,976 images features annotations made by specialists which define the bounding box and the class for each instance. This collection is therefore suited for detection and classification tasks.  
Given the goal of accurately localizing and classifying insect pests on images captured by in-field sensors, the second collection of the original IP102 dataset has been used in this study for framework development. From now on, the authors refer to this collection as the dataset.
## 1 Introduction
The selection criteria for the DSN in LFEER and Co-LFEER is the same. The same parameters (residual energy, number of hops, BER) are used to choose RLNs. However, in Co-LFEER, cooperation between the DSN and RLN is considered. Unlike many other cooperative routing protocols [8; 18], Co-LFEER selects only one RLN and DSN to transmit data bag towards the final DNN. By choosing a single forwarder node and the DSN, the consumption of the energy is controlled. The number of hops and BER for the selection of DSN and RLNs minimize the path loss effects on the data packets, thus the maximum information bags are transmitted to the final DNN. The highest value of the function (residual energy, number of hops, BER) is assigned to the DSN and the second highest value is assigned to the RLN. This paper contributes in the following ways:  
* LFEER: In the proposed localization free energy efficient routing protocol, the DSN is chosen based on residual energy, number of hops and bit error rate values. The source node chooses only one DSN from the neighbor nodes. That node is chosen as a DSN which has the maximum residual energy, fewest hops and minimum bit error rate. Due to these parameters and mechanism, the energy consumption of the nodes is controlled, which increases the battery lifetime. * Co-LFEER: The improved version of the proposed LFEER is called localization free energy efficient cooperative routing. Here, one RLN is considered. The DSN and the RLN are chosen on the parameters: residual energy, number of hops and BER. The DSN is chosen among the many existing nodes in the field of the source node. The node which has the maximum residual energy, fewest hops and least BER is chosen as a DSN to transmit the information bags. The RLN selection parameters are the same as for the DSN. The RLN is selected based on second maximum/least values of the same parameters as for the DSN. The fewest hops and minimum BER parameters control the channel effects on the information bags and, as a result, PDR is improved. * No Localization: The proposed protocols are localization free and do not need the exact location of the nodes which reduces the cost of the network.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 5 Results and Analysis
### Evaluations of ARNS  
In this subsection, we compare ARNS with RTAD and the 3P3B-based method in the same environment. We show the advantages of ARNS in three aspects, including end-to-end delay, maximum hops, and PDR. The simulation results show as follows.  
Figure 4 shows the end-to-end delay obtained by each method with varying vehicle density. RTAD has the largest delay as it needs more hops to complete message broadcasting. In contrast, ARNS has the lowest delay as it costs the fewest hops by adaptively selecting the relay-nodes. Furthermore, we can see that, as vehicle density increases, end-to-end delay first decreases and then increases. The decrease is because message broadcasting can be implemented with fewer hops when vehicle density gets higher. The increase is due to the larger contention delay because of more nodes in the contention process.  
In Figure 5, the maximum hops of three methods are depicted to indicate the reliability of end-to-end delay shown in Figure 4. RTAD has the most hops as it selects corners as the optimal positions in curved road scenarios. In contrast, ARNS has the least hops since it improves the location of the optimal position. Moreover, with the increase of the vehicle density, the maximum hops of ARNS declines in a stable trend, while the maximum hops of the beacon-based method are already saturated.  
Figure 6 presents the PDR of the three methods. It can be clearly seen that, as vehicle density ascends, PDR declines. PDR of ARNS is better than that of both the 3P3B-based method and the beacon-based method. Additionally, PDR of ARNS is more stable than the other two. The reasons can be derived as follows. Firstly, for the beacon-based method, nodes in its routing table may travel out of the communication range during the beacon interval, resulting in the loss of message packets. In this case, we will re-transmit. However, if the number of re-transmissions reaches the maximum times, the message packet is still missing, then the broadcast is considered as a failure. However, the relay-node selection of ARNS is real-time, so ARNS is more stable than the beacon-based method. Secondly, compared with the 3P3B-based method, the partition phase of ARNS selects a smaller segment than the based-3P3B method. Then fewer nodes participate in the random contention phase. This results in the gain for PDR of ARNS. Therefore, PDR of ARNS is the most stable among the three methods.
# Life-Cycle Cost Analysis on Application of Asphalt and Concrete Pavement Overlay
## 1 Introduction
High-early-strength slag cement was used in the overlay concrete to minimize the traffic blocking time and the concrete overlay showed good performance for one year [19]. To achieve the effective repairs of aged and deteriorated concrete pavements, various overlay methods have been developed, and the most effective type of overlay is necessary to analyze the performance of overlay pavement sections [20]. The non-cutting asphalt overlay could reduce the LCC by 18 to 25% when applying the cutting asphalt overlay and the non-cutting asphalt overlay through economic analysis [6]. Permanent deformation is one of the major destructions which is related to load and which affect the functions of asphalt pavements [21]. Permanent deformation is one of the main reasons because of which pavement depressions happen [22]. However, concrete overlay is applied only to test pavements. Thus, it is necessary to improve the awareness of the application of concrete overlay in the context of Korean expressways. During overlay in Korea, the lane under construction is typically blocked completely. This causes a reduction in vehicle speed owing to the concentration of traffic in unblocked lanes, which leads to the dissatisfaction of road users [23; 24]. Therefore, traffic operation measures that can improve the satisfaction of road users are required during the application of overlay. This study aimed to examine the applicability of asphalt and concrete overlays through LCC analysis. The value per vehicle in roads with traffic blocking because of overlay was converted into user cost using the Construction Analysis for Pavement Rehabilitation Strategies (CA4 PRS) software after identifying the number of lanes, traffic volume, and availability of shoulder in Korean expressways. When the shoulder was used, a reduction in user cost was also confirmed. The construction costs of asphalt and concrete overlays were calculated, and the life-cycle of each overlay method was identified from the existing literature. Finally, LCC analysis was conducted by applying the user costs, construction costs, and life-cycles of asphalt and concrete overlays.