esa-sceva/satcom-chunk-collection huggingface dataset:
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 3 Related Work
According to the authors, future research in this area should prioritize in integrating cyber and physical data into a single system to focus on detecting, at an early stage, attacks to mitigate the potentially serious impacts of cyber-attacks on HVDC networks. In [12], the authors study the security vulnerabilities of photovoltaic (PV) inverters, paying special attention to their internal sensors, which are critical for reliable energy conversion. The authors found that both current and voltage sensors are susceptible to electromagnetic interference (EMI) at frequencies of 1 GHz or higher, regardless of the presence of electromagnetic compatibility (EMC) protections. These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. The authors propose an EMI attack that leads to three potential outcomes: denial of service (DoS), physical failure of the inverter, and reduction in output power. These effects have been observed on six commercial single-phase and three-phase PV inverters as well as in a real microgrid. The results were obtained by irradiating with EMI signals from a distance of 100-150 cm with power up to 20 W. This study by the authors highlights the increasing security risks of power electronics in renewable energy sources (RES), which represents a new target for cyber-physical attacks in future RES-dominated grids. Finally, the authors propose three methods to deal with such threats that are adaptive to different threat scenarios, and their advantages and disadvantages are discussed. In [18], the authors discussed the cybersecurity of smart microgrids in detail. In their work, they have briefly discussed the cyber physical systems in smart microgrids. Then, they focus on cyber-attacks on data availability, integrity and confidentiality. False data injection (FDI) compromises the integrity of data in the cyber/communication network and is one of the most challenging threats to smart microgrids. Therefore, this attack is discussed in detail in the work. Such FDI attacks can target voltage and frequency management as well as secure systems of smart microgrids. In their work, the authors have also considered the economic and physical-technical impacts of FDI attacks on smart microgrids. The defense strategies against these attacks, according to the authors, are classified into protection strategies, where selected instrument measurements are protected, and detection/limiting strategies, based on static or dynamic detection. Additionally, the authors have given examples of the implementation of building FDI attacks, detecting them and limiting them in smart microgrids.
## 3 Methodology
### System-Level Health Condition Prediction  
#### 3.4.1 Hybrid Prediction Model  
A single prediction method has its advantages and disadvantages, so it is more-or-less suitable for a given supervision problem. There is no universal prediction method that outperforms all other methods in all situations. Therefore, the hybrid model that takes into account the advantages of each model can predict the decomposed signals with different characteristics, realizes the complementarity between models, and then improves the prediction accuracy and decision rationality.  
For the global attenuation trend decomposed signal, the RVM model with higher computational accuracy and lower computational complexity than the SVM algorithm was selected for prediction, and it could output the uncertainty information of the prediction results. The adaptive RVM method was adapted for online prediction [28]. For the decomposed signal with obvious nonlinear characteristics, the LSTM [29] algorithm with a better nonlinear autonomous learning function was selected. The prediction results of the three models were accumulated:  
\[\hat{u}=\hat{u}_{1}+\hat{u}_{2}+\ldots+\hat{u}_{k}. \tag{12}\]  
where \(\hat{u}_{k}\) is the prediction results of \(u_{k}\) and \(\hat{u}\) is the hybrid model predicting the final result. When the fusion features changed, the training sample of the hybrid prediction model was updated, and the health condition prediction was conducted again. To highlight the performance of the created SHI and limit the impact of the prediction methods on the final results, the default hyperparameters used in the methods were used without making adjustments.
## 4 Discussion
### Parameters Affecting the Minimum and Maximum Microplastic Concentrations  
We hypothesized that contamination around the minimum values indicates the basic pollution of a river base, while the extremely high values are related to local factors. The minimum values increase continuously downstream of the Tisza (from section S1: 260 item/dry kg to section S5: 360 item/dry kg), simultaneously with the gradual decrease in the slope and flow velocity; thus, the MP in sediments gradually accumulated. A similar trend characterizes the in-channel natural sediment aggradation of the Tisza [31]. The tributaries also support the connection between the minimum MP content and the slope, as their minimum MP contamination is increasing from the Upper Tisza's sub-catchments to the lower sub-catchments; thus, the lowermost tributary has the highest minimum MP pollution. Therefore, we suggest analyzing the longitudinal MP concentrations considering only the minimum values of hydrologically uniform sections, as these values indicate the basic pollution. Probably, the lack of this approach had led to ambiguous results on longitudinal changes in MP concentrations, as some studies suggest that the MP pollution in sediments has increased downstream [7; 13; 26], others indicated a decrease [58] or no longitudinal downstream trend at all [24].  
The development of extremely high MP concentrations can be attributed to the sudden deterioration of the MP transport conditions. This statement is supported by the fact that, in 2019, the MP concentration was on average 6305 \(\pm\) 1434 item/dry kg at the most polluted points (\(\geq\)4000 item/dry kg), while in 2020, at the same points, only 1732 \(\pm\) 1317 item/dry kg of MP was in their sediments, so the pollution of these points decreased by 73%. At the same time, new contamination hot-spots have developed in areas where previously slightly or moderately contaminated sediments were found. Because the flow conditions at the same site on the same form at the same point hardly change in one year, the change in the MP content is explained by the river's temporally changing transported sediment flux. As the MP content of the transported sediment is temporally various, MP-contaminated fluxes and less polluted fluxes exist. The question is where the given transported material will reach a transport threshold point, so when it will be deposited, i.e., where the flow velocity fell below the transport rate during a flood wave (flux). Thus, the development of extremely high values is probably related to the MP input into a given reach and the spatial and temporal changes in sediment transport capacity. However, the former researchers explained the development of MP hot-spots by increased MP input from megacities [51], agriculture and wastewater treatment plants [58] and industry [7]. The development of hot-spots was also explained by artificial or natural impoundment [1; 15; 19; 29].
## 5 Conclusions  
Renewable energy is expected to account for a high proportion of power generation in the future. In response to sharp decreases of renewable energy output that result in power shortages, appropriate UC problems can be solved to fill short-term power gaps. To fill renewable energy gaps of 1720 and 1560 MW within 30 and 10 min, this study used MATLAB to write an algorithm. However, a large number of units may increase the complexity of the scheduling problem, leading to the curse of dimensionality and a failure to generate the optimal solution. This study proposed combining HA and APL to perform scheduling on 20 natural gas combined-cycle units, and the results were compared with those generated using IPSO and SA. This study found that the proposed method was superior to the other two algorithms in terms of the computation time and cost. Not only can this method precisely satisfy the constraints of the power system, but it can produce the lowest ramp-up cost. This study proposed a method that can increase system power within a specific time period through an optimal UC. Renewable energy may account for a large proportion of power generation in the future, even though renewable energy systems may have variable power output. Therefore, when a power shortage occurs due to a sudden drop in renewable energy output, the proposed method can be used for the scheduling and dispatch of power generating units. Power companies can use this method to dispatch units and governments can use these findings for promoting and implementing renewable energy power generation. [PERSON] conceived of the presented idea, wrote and revised the manuscript; [PERSON] and [PERSON] supervised the findings of this work, and planned the experiments; [PERSON] and [PERSON] performed the numerical simulations; all authors provided critical feedback and helped shape the research, analysis and manuscript. The authors gratefully acknowledge the support of the Ministry of Science and Technology of Taiwan, R.O.C. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Power Gap/Time**} & \multicolumn{3}{c}{**Advanced Priority List**} & \multicolumn{3}{c}{**Traditional Priority List**} \\ \cline{2-10}  & **HA (NT \$/s)** & **IPSO (NT \$/s)** & **SA (NT \$/s)** & **HA (NT \$/s)** & **IPSO (NT \$/s)** & **SA (NT \$/s)** \\ \hline
1720 MW/30 min & 45289/1.46 & 45302/3.5 & 45564/4.2 & 50270/1.38 & 50270/2.54 & 50279/3.11 \\
1560 MW/30 min & 40087/1.86 & 40265/3.84 & 40359/4.45 & 49264/1.76 & 49264/2.72 & 50032/2.98 \\
1720 MW/10 min & 45719/0.74 & 45850/2.88 & 45978/4.35 & 45940/0.74 & 46015/3.18 & 46051/3.22 \\
1560 MW/10 min & 40795/0.84 & 40986/2.98 & 40879/3.98 & 40302/0.65 & 43639/2.45 & 43630/3.12 \\ Ranking & 1 & 2 & 3 & 4 & 5 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the results of three algorithms in different renewable energy gap and time scenarios.
## 2 Study Area and Data
### Data  
Figure 1 shows shaded relief map of study area and image coverage, etc. The four interferometric pairs we selected for this study, include two pairs of L-band (radar wavelength of 23.6 cm) ALOS-2 images and two pairs of C-band (radar wavelength of 5.6 cm) Sentinel-1A images (see Figure 1). Table 1 lists the acquisition date of each SAR image and the perpendicular baseline (\(B_{\perp}\)), temporal baseline (\(B_{\rm T}\)) and orbit types of each interferometric pair. The ALOS-2 ScanSAR mode images were collected along both the ascending and descending orbit at a radar incidence angle of 39\({}^{\circ}\) in HH polarization and generated with a pixel spacing of 19.02 m in slant range and 25.9 m in azimuth (i.e., along track). Each image scene covers an area of about 350 km by 350 km in azimuth and range, respectively.  
Meanwhile, the Senitnel-1A IW mode images were collected along both the ascending and descending orbits at radar incidence angle of 39.5\({}^{\circ}\) in VV polarization and generated with a pixel spacing of 5 m in slant range and 20 m in azimuth. Each image scene covers an area of about 250 km by 170 km in range and azimuth, respectively. The heading angles of the ALOS-2 and sentinel-1A ascending orbits are 347\({}^{\circ}\) and 344\({}^{\circ}\) (measured clockwise from the north), respectively; and the heading angles of the descending orbits are 195\({}^{\circ}\) and 193\({}^{\circ}\) (measured from the north), respectively.  
Around the epicenter, the vegetation coverage is low. At the same time, the temporal and spatial baselines are small enough (see Table 1), thus the coherence is high enough. The DInSAR and MAI methods will be processed effectively to reconstruct the 3D coseismic deformation caused by the earthquake. In ALOS-2 ScanSAR mode, SAR antenna cyclically points to several subswatths with different look angles. In each cycle, the SAR system collects a group of echoes for each subswath. The group of echoes is called a burst. By repeating this cycle over several subswatths, a wide-swath image can be acquired [30]. The earthquake region was fully imaged by a burst. So, considering the efficiencies, only one burst of ALOS-2 images and whole Sentinel-1A images were used, actually.
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 3 Results and Analysis
### Influence of Porosity-Binder Index on UCS  
Figure 3 illustrates the correlation between the UCS and the corrected porosity-binder (P/B) ratio (\(\eta/\chi_{\text{iv}}\)\({}^{\alpha}\)) for various samples with varying additives, densities, and curing durations [40].  
This study shows that porosity significantly impacts blend behavior, as evidenced by the power coefficient \(\alpha\). It is consistent with prior research findings [41]. When \(\alpha\) assumes a value of less than 1, it implies that porosity has a more significant influence [42]. Within the present investigation, the optimal fit was at \(\alpha\) = 0.15. It aligns with earlier empirical studies, where the \(\alpha\) values typically ranged from 0.12 to 0.35, primarily depending on the specific soil type, as demonstrated in previous research.  
As can be seen from Figure 3a-d, by increasing the porosity index, the decrease in \(q_{u}\) has been achieved, which aligned with prior studies [43; 44; 45]. Considering the amount of glass powder, it was revealed that samples with 12% GP, compared to the other percentages of GP, have a lesser range of porosity that yields higher amounts of \(q_{u}\). This jump of \(q_{u}\) from 8% to 12% of GP is because of a higher reduction in the porosity investigated in a microstructural analysis. On the other hand, the high regressions of curvatures in Figure 3a-d represent a significant relation between \(q_{u}\) and porosity in all samples.  
Figure 3: The associations between the UCS and the modified P/B index for all curing durations and different amounts of sintered gypsum, with (**a**) 0% GP, (**b**) 4% GP, (**c**) 8% GP, (**d**) 12% GP for both samples’ dry densities.  
Moreover, regarding the graphs in Figure 3, curing days play a significant role in sample strengthening. As shown in Figure 3a-d, graphs of 7, 28, and 56 curing days are positioned above each other, respectively, except for samples with a GP content of 8%. In addition, graphs related to seven curing days are notably distinguished from the other two graphs in each chart regarding the distance, especially at high porosities. The growth of \(q_{u}\) by increasing the curing days is predictable; however, the case of GP content of 8% is discussed in the microstructural analysis part.
## 4 Experimental Results and Analysis  
In order to test the performance of OCA, we take the accuracy of the Doppler difference velocimetry between the solar spectra and Ceres spectra as the optimization goal. The paper undertakes the following tasks:  
1. We manually select the most suitable reflection template to provide the initial population for the swarm intelligence algorithms.
2. We use OCA applied in the inversion method of the asteroid spectra reflectance template.
3. We investigate the zone competition mechanisms using OCA.
4. We compare OCA with OCA, GA, SSA, and ARO.
5. We extend the application of OCA for the 0-1 knapsack problem and compare it with GA, PSO, SSA, ARO, and OCA.
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 2 Related Work
### Classical ML  
In classical machine learning approaches for DR and DME detection, various algorithms have been used, such as linear regression, logistic regression, support vector machine, principal component analysis, decision tree, random forest, and naive Bayes. In some cases, these algorithms have been combined with optimization techniques like moth-flame optimization to enhance their performance.  
In a study by [PERSON] et al. [7], an integrated machine learning approach was proposed for early detection of diabetic retinopathy. The approach combined the use of support vector machine, principal component analysis, and moth-flame optimization  
Figure 1: The retinal fundus can exhibit various types of lesions, each with its distinct appearance. Soft exudates appear as white, feathery or fluffy spots, while hard exudates are harder, uneven and have a white or yellowish appearance. Microaneuryss are small, round and have defined borders, measuring less than 3 mm in diameter, and are red in color. Hemorrhages are indications of bleeding in the retina and can manifest as dots, blots or flames. Additionally, the macula and optic disc are also indicated.  
techniques. The individual performance of decision tree, support vector machine, random forest, and naive Bayes algorithms was initially evaluated on a diabetic retinopathy dataset. The integration of PCA improved the performance of the decision tree algorithm, but reduced the performance of the other algorithms. The integration of moth-flame optimization with SVM and PCA resulted in improved performance with an average of 85.61%.  
A study [8] focuses on early detection of diabetic retinopathy using an ensemble machine learning model composed of Random Forest, Decision Tree, Adaboost, K-Nearest Neighbor, and Logistic Regression algorithms. The diabetic retinopathy dataset was normalized using min-max normalization before training the ensemble model, and the results showed that the ensemble model outperformed the individual machine learning algorithms.  
According to the study by [PERSON] et al. [9], a machine learning framework was developed for DME using optical coherence tomography (OCT) volumes. The dataset used in the study consisted of 32 OCT volumes obtained from the Singapore Eye Research Institute using the CIRRUS SD-OCT device. The study employed pre-processing, feature detection, feature representation, and classification steps to classify the images as normal or DME. The best results were achieved using LBP16-ri vectors and linear-support vector machine in combination with PCA and bag of words representations, with a sensitivity and specificity of 87.5% each.  
The studies have shown that classical ML algorithms can be useful in DR and DME detection, however, they have limitations such as difficulty in handling large and complex data, and dependence on the quality of hand-engineered features. These limitations have led to a shift towards deep learning methods in recent years.
# Prediction of Pre- and Post-Breakage Behavior of Laminated Glass Using a Phase-Field Damage Model
## 3 Phase-Field Model
### Viscoelasticity of Interface Layer  
The response of a polymer interlayer is significantly time-dependent. However, even in a low velocity impact, the response time is in the range of milliseconds, so that the rate dependency can be neglected and an empirical rule can be used to evaluate its current shear stiffness. In particular, adopting the \(t/2\) rule [24], the instantaneous shear modulus of the interlayer \(G_{\rm interlayer}\) is provided by  
\[G_{\rm interlayer}(t_{i},T_{\rm act})=G(t_{i}/2,T_{\rm act}), \tag{38}\]  
where \(t_{i}\) is the current time and \(T_{\rm act}\) is the actual temperature. Function \(G(t,T)\) can be approximated by the Generalized Maxwell chain model [25] as  
\[G(t,T)=G_{\infty}+\sum_{p=1}^{N}G_{p}\exp^{\frac{t}{p+1}p}, \tag{39}\]  
where \(N\) stands for the number of Maxwell units. The corresponding shear stiffnesses \(G_{p}\) associated with the selected relaxation times \(\tau_{p}\) are obtained by fitting Equation (39) to experimental measurements [26; 27]. The temperature shift factor \(\mathpzc{a}_{\rm T}\) follows from the well known Williams-Landel-Ferry equation  
\[\log\mathpzc{a}_{\rm T}(T_{\rm act})=\frac{-C_{1}(T_{\rm act}-T_{0})}{C_{2}+T _{\rm act}-T_{0}}. \tag{40}\]  
In the present study, we adopt the results derived for the PVB interlayer in [26]. These are summarized in Table 4 complemented with \(G_{\infty}=232.26\) kPa. The constants \(C_{1}=8.635\) and \(C_{2}=42.422\) were found for the reference temperature \(T_{0}=20\)\({}^{\circ}\)C.
# A Comprehensive Analysis of Smart Grid Systems against Cyber-Physical Attacks
## 4 Function-Based Methodology
### Cyber-Physical Attack Scenarios  
Attack on gas-fired peaker plants (Attack I): The main motive of the attackers is to attack the gas distribution network via a business network. They perform social engineering (via spear-phishing emails) to employees with the goal to discover account details. The next step is to compromise the vulnerable workstations in the business network and try to find the connection to the industrial control system network. Once the attackers gain access to the control system network, they start compromising the workstations of the control center, which provides access to the physical devices (as in the case of the Ukraine Power grid attack [5]) and machinery such as compressor stations, Remote Terminal Units (RTU), sensors and wireless nodes of the communication network. In this attack scenario, they compromise RTU present along the natural gas distribution pipeline and misguide them to send incorrect readings to the control center and close down one of the compressor stations. The primary motive is to cut down the supply of the natural gas to the GPP. The attackers perform this attack during peak hours, leveraging the online data available on the Independent System Operators (ISO) website [33], so that the attack has a high impact on SGR. We have modeled this scenario by taking an interval during the peak time, closing the peaker power plant and evaluating the impact of such a contingency on the power grid.  
Attack on Distributed Energy Resources (DER) (Attack II): In this attack scenario, attackers compromise the control center from where they control DERs. They send shutdown signals to a critical number of DERs to reduce power generation when it is required (using online ISO data [33]). It creates a demand-supply mismatch and, thus, degrades SGR. In the simulation, we have modeled a generator as a DER Solar PV [34]. We assume the attacker has compromised the DER station and can control its functionality remotely at any point in time. When generation is not able to meet demand, the power line frequency in the area decreases. We have modeled this scenario in the simulation by taking an interval during the peak demand of the day and shut down the DER generator.