esa-sceva/satcom-chunk-collection huggingface dataset:
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## Appendix A Generalized Methods and Limitations of Optical and Radar Sensors for Snow Cover Mapping  
The effectiveness of optical spaceborne sensors in detecting snow cover is largely due to the high reflectivity (albedo) of snow compared to other Earth's surface materials [93]. These sensors typically operate using multispectral bands, including visible light, infrared, and thermal. The most common method for snow cover identification employs the NDSI, which leverages spectral information from the visible (green) and near-infrared bands [94; 95]:  
\[NDSI=\ \frac{\sigma_{green}-\ \sigma_{infrared}}{\sigma_{green}+\ \sigma_{infrared}} \tag{1}\]  
where \(\sigma\) denotes the reflectance in a specific wavelength band of the electromagnetic spectrum. Cloud cover and dense forests present significant obstacles to operational snow cover monitoring [96]. A notable limitation is that the snow surface is often completely obscured from the satellite sensor's view by clouds. Another common problem in optical multispectral imagery is the confusion between snow and ice-clouds [97]. Additionally, in densely forested areas, particularly coniferous forests, the reflectance in the green band is highly sensitive to increases in canopy cover (high canopy cover values can cause negative NDSI values in snow-covered areas), making accurate snow cover classification in such regions challenging [98].  
Wet snow monitoring with SAR remote sensing is based on the ratio of backscattering coefficient between one wet snow image and a reference image that represents the typical backscattering conditions of snow-free and dry snow surfaces. [PERSON] et al. [40] determined that a threshold of \(-3\) dB provides a good accuracy for wet snow classification. Recently,a more efficient method for wet snow mapping was developed by means of the fusion of backscattering coefficients ratios from co-polarization and cross-polarization signals [30], so that in this instance established as optimal a \(-2\) dB threshold for the combined backscattering ratio. However, it has been shown that these fixed thresholds (\(-2\) dB and \(-3\) dB) may not be suitable under certain land cover uses (agricultural, forested, urban areas, etc.), incidence angles and snow wetness conditions [99; 100].  
In active-microwave remote sensing with L-, C-, or X-band forest canopy above wet snow typically increases the backscattering coefficient due to volume scattering within the canopy. This effect reduces the ratio of the backscattering coefficient between wet snow and snow-free or dry snow surfaces [99]. Conversely, in passive-microwave remote sensing, forest canopy attenuates backscattering signal from snow and ground, while adding its own contribution [101]. Additionally, [PERSON] et al. [102] demonstrated that the backscattering coefficient of wet snow is strongly dependent on the snow-surface roughness and the incidence angle. It was also found that very high liquid-water content within the snowpack could reduce the differences between the backscattering coefficients of wet and dry snow or snow-free conditions.
# Niching Grey Wolf Optimizer for Multimodal Optimization Problems
## 3 Result and Discussion
### Results of the Engineering Application
#### 3.4.3 Three Bars Truss Design Result  
Table 8 summarizes the results of three bars truss design problem. The WCA and DEDS algorithms achieved three and two best indexes, respectively. The NGWO algorithm's results are very similar to the previous best results obtained by various optimization algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithms** & **y1** & **y2** & **y3** & **y4** & **f (Best)** & **f (Mean)** & **f (Worst)** & **SD** & **NFEs** \\ \hline NGWO & 0.34094 & 3.5810 & 9.0321 & 0.2063 & **2.036** & **2.046** & **2.0632** & 1.02 \(\times\) 10\({}^{2}\) & **20,100** \\ GA [54] & 0.2489 & 6.173 & 8.1789 & 0.2533 & 2.4331 & N/A & N/A & N/A & N/A \\ SC [55] & 0.2444 & 6.238 & 8.2886 & 0.2446 & 2.3854 & 3.2551 & 6.3997 & 9.60 \(\times\) 10\({}^{-1}\) & 33,095 \\ FSA [56] & 0.2443 & 6.2158 & 8.2939 & 0.2443 & 2.3811 & 2.4042 & 2.489 & N/A & 56,243 \\ AATM [57] & 0.2441 & 6.2209 & 8.2982 & 0.2444 & 2.3823 & 2.387 & 2.3916 & 2.20 \(\times\) 10\({}^{-3}\) & 30,000 \\ HEAA [58] & 0.2444 & 6.2175 & 8.2915 & 0.2444 & 2.381 & 2.381 & 2.381 & **1.30 \(\times\) 10\({}^{-5}\)** & 30,000 \\ EEGWO [41] & 0.2444 & 6.217 & 8.2928 & 0.2444 & 2.3813 & 2.3817 & 2.3824 & 4.18 \(\times\) 10\({}^{-4}\) & 50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative study of Welded Beam design problem.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Algorithms** & **y1** & **y2** & **y3** & **f (Best)** & **f (Mean)** & **f (Worst)** & **SD** & **NFEs** \\ \hline NGWO & 10.32826 & 0.35486 & 0.05026 & 0.011053 & 0.011755 & 0.012454 & 5.21 \(\times\) 10\({}^{-4}\) & **20,100** \\ GA [59] & 10.89052 & 0.36396 & 0.05198 & 0.01268 & 0.01274 & 0.01297 & 5.90 \(\times\) 10\({}^{-5}\) & 80,000 \\ FSA [56] & 11.21390 & 0.35800 & 0.05174 & 0.01266 & 0.01266 & 0.01266 & **2.20 \(\times\) 10\({}^{-8}\)** & 49,531 \\ CPSO [60] & 11.24454 & 0.35764 & 0.05172 & 0.01267 & 0.01273 & 0.01292 & 5.20 \(\times\) 10\({}^{-5}\) & 200,000 \\ HPSO [60] & 11.26503 & 0.35712 & 0.05170 & 0.01266 & 0.01270 & 0.01271 & 1.58 \(\times\) 10\({}^{-5}\) & 81,000 \\ GSA [13] & 14.22867 & 0.05 & 0.31731 & 0.01287 & 0.01343 & 0.01421 & 1.34 \(\times\) 10\({}^{-2}\) & 30,000 \\ GWO [18] & 10.04249 & 0.34454 & 0.05117 & 0.01267 & 0.01269 & 0.01272 & 2.10 \(\times\) 10\({}^{-5}\) & 30,000 \\ AFA [61] & 11.31956 & 0.35619 & 0.05166 & 0.01266 & 0.01266 & 0.01280 & 1.27 \(\times\) 10\({}^{-2}\) & 50,000 \\ TEO [62] & 11.16839 & 0.35879 & 0.05177 & 0.01266 & 0.01268 & 0.01271 & 4.41 \(\times\) 10\({}^{-2}\) & 300,000 \\ MGWO [63] & 11.80809 & 0.34819 & 0.05133 & 0.01266 & 0.01267 & 0.01270 & 1.10 \(\times\) 10\({}^{-5}\) & 30,000 \\ EEGWO [41] & 11.3113 & 0.35634 & 0.05167 & 0.01266 & 0.01268 & 0.01272 & 2.22 \(\times\) 10\({}^{-5}\) & 50,000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative study of tension–compression coil spring design.
We have recently demonstrated that the yield of red clover biomass production may be substantially (up to almost 50%) increased by using seed treatment with CP and EMF [30]. In this study, we aimed to test the hypothesis that the yield of common buckwheat may be also increased using such treatments that are technically easier in comparison to molecular genomics. However, in most of the studies performed (reviewed in [23; 24; 25; 26; 27; 28; 29]) only the effects on germination and early seedling growth were estimated, and only a single study has been reported for common buckwheat [31]. Previously, we have demonstrated that a several fold increase in biomass production of perennial plants may be achieved after one year of plant growth [32]. Our experience showed that the results from laboratory germination tests in vitro may be different from those obtained when seedling emergence in a substrate was estimated [33; 34; 35]. Field observations for the entire vegetation season are required to test the effects under conditions close to those of agricultural crop cultivation, as well as to estimate the persistence and dynamics of plant response to seed treatment. The aim of this study was to perform an investigation of the effects of seed treatment with radio-frequency EMF or CP on two cultivars of common buckwheat ('VB Nojai' and 'VB Vokia') both in the laboratory conditions as well as during plant cultivation in the field for the entire vegetation season. For this, the effects of treatments on germination in vitro and on seedling emergence under field conditions were estimated. We also evaluated the effects on seedling growth in the field, on the parameters of photosynthetic efficiency and radical scavenging activity in leaves after 4 and 8 weeks, as well as changes in the crop biomass production, seed harvest and nutritional value after 14 weeks of common buckwheat cultivation.
## 3 Results
### Adversarial Attack Configuration  
The attacker knows that the targeted image classification system uses ResNet-34 to train the image classification model. He/she also knows the data being used for that training, i.e., the CIFAR-100 training set. The attacker uses a test set of the same dataset and state-of-art adversarial attack algorithms: FGSM [1], Deepfool [13], CW [14], and PGD [15]. The parameters of each type of AML are listed in Table 3 and defined in the next section.  
In the FGSM trial, we set the \(\epsilon\) parameter, which is a hyperparameter determining the size of the perturbations introduced to the input data, to 0.007. The value of \(\epsilon\) is a tradeoff between the adversarial attack strength and the perturbation perceptibility. Raising this value could increase the exploit success rate; however, it might show apparent noise on the targeted image that could be revealed to human perception. We set the default value to 0.007 because the added perturbations are not easily perceived by human eyes. The FGSM attack success accuracy based on the selected \(\epsilon\) on the CIFAR-100 test set is 65.75%.  
To execute the Deepfool attack, we limit the attack iterations to 50 steps before stopping. During each iteration, the attack calculates the direction of the closest decision boundary to the original input data point in order to determine the minimum perturbation required to deceive the targeted DNN model. The overshoot parameter is set to 0.02, which multiplies the computed perturbation vector and adds it to the input image. With these settings, the attack success accuracy reaches 99.92%.  
To ensure a successful attack by the CW method, we utilize the C&W attack parameters listed in Table 3: \(c=1\), \(\kappa=0\), steps \(s=50\), and lr = 0.01. The '\(c\)' hyperparameter determines the magnitude of the perturbation, while the margin parameter (\(\kappa\)) determines the confidence gap between the predicted and target classes. The steps (\(s\)) parameter  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Targeted Model** & **Dataset** & **Adversarial Attack** & **Parameters** & **Attack Success Ratio (\%)** \\ \hline \multirow{4}{*}{ResNet-34} & \multirow{4}{*}{CIFAR-100} & FGSM & \(\epsilon=0.007\) & 65.75 \\ \cline{3-5}  & & Deepfool & s = 50, overshoot = 0.02 & 99.92 \\ \cline{1-1} \cline{2-5}  & & CW & c = 1.0, \(\kappa=0\), s = 50, lr = 0.01 & 98.64 \\ \cline{1-1} \cline{2-5}  & & PGD & \(\epsilon=0.03\), \(\alpha=0.004\), s = 40 & 98.83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment settings.  
represents the number of iterations required for the attack to succeed or end. Lastly, the learning rate (lr) controls the optimization iteration steps. With these adjustments, we achieve an attack success rate of 98.64%.  
The PGD attack is adjusted with the following parameters: \(\epsilon\) = 0.03, alpha \(\alpha\) = 0.004, and steps = 40. \(\epsilon\); steps were explained in previous attacks, while alpha functions similar to the learning rate determine the size of each optimization step. This attack has a success rate of 98.83%.
## 2 Cu, Cu2O, CuO, and CuX Nanoparticles and Nanocomposites
### Metal-Organic Frameworks  
Metal-organic frameworks (MOFs) represent an interesting class of hybrid organic-inorganic porous materials with tunable properties [127]. The preparation of Cu\({}_{2}\)O nanoparticles immobilizedwithin the pores of (Zn(Himdc)(bipy)\({}_{0.5}\)) (Himdc = 4,5-imidazolelociderboxylate; bipy = 4,4\({}^{\prime}\)-bipyridine, Scheme 9) was achieved by [PERSON] et al. through reduction with hydrazine hydrate of the CuSO\({}_{4}\)-impregnated MOF [128]. XRD, XPS, and TEM characterization of the resulting solid **17** showed the presence of Cu\({}_{2}\)O nanoparticles (mainly 2-3 nm in size and containing minor amounts of CuO), dispersed within the pores of the essentially intact Zn(II)-ligands framework. Two-components CuAAC catalytic runs (Scheme 1), in the presence of an equivalent amount of Et\({}_{3}\)N and 0.5 mol % of **17** in _t_-BuOH-H\({}_{2}\)O (2:1), afforded nearly quantitative yields after 7 h at 50 \({}^{\circ}\)C. The MOF-supported catalyst could be recovered by filtration, dissolution of the triazole product in CHCl\({}_{3}\) and centrifugation, thus allowing the completion of three reaction cycles with reportedly unchanged activity.  
Considering also that TEM, XRD, and CO\({}_{2}\) adsorption measurements showed no significant changes with respect to the pristine material, these result led to the conclusion that pendant Himdc carboxylate groups in the MOF structure may have a positive role in stabilizing Cu\({}_{2}\)O nanoparticles and preventing their aggregation. Unfortunately, the lack of any information about the content of copper and MOF components in the crude triazoles (either Zn(II) or the organic ligands) makes it difficult to draw any conclusion about this interesting CuAAC system from the point of view of product contamination.
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
###### Abstract  
This article presents a summary of three common false-bottom occurrences in echosounder imaging based on an analysis of echosounder data. Utilizing the imaging principle of the echosounder, a comprehensive analysis was conducted and an explanation of each situation's causes, imaging characteristics, impacts, and solutions is presented. Additionally, the article includes calculations to determine the precise location of the false bottom, which were subsequently validated through actual data collection. To address the two most impactful false-bottom scenarios in target detection, solutions are proposed from two perspectives. By accurately judging the position and imaging characteristics of these false bottoms, the article concludes with an analysis of the causes of false bottoms and presents corresponding solutions. The article aims to facilitate the rapid identification and elimination of false bottoms, thus mitigating their adverse effects on target detection.  
echosounder; false bottom; imaging characteristics; judging position; eliminate
## 4 Experiments (Application to [PERSON]'s Airline Passenger Satisfaction Data) and Discussion
6: IF Inflight WiFi service is low or medium AND Customer type is loyal customer AND Ease of online booking is high AND Leg room service is high THEN Passenger is satisfied). The last rule says that for loyal customers, WiFi services are not significant when online booking and leg room services are of a high quality--perhaps, this rule relates to older passengers who usually do not use WiFi devices. Another such an example is rule No. 9 in solution No. 5: IF Inflight entertainment is high AND Inflight WiFi service is low or medium AND Customer type is disloyal customer AND Flight distance is short THEN Passenger is neutral or dissatisfied. This rule says that high quality of inflight entertainment is not enough to satisfy disloyal passengers travelling on short distanceswhen WiFi services are low--perhaps, this rule concerns young passengers travelling on short distances and only interested in inflight WiFi services.
# On the Capacity of Optical Backbone Networks
## 4 Link and Network Capacity
### Network Capacity
On average, there is about 13% improvement for real capacities and about 20% improvement for Shannon capacities. On a final note, it can be mentioned that using a more complex methodology [16] reports a capacity of 109.2 Tbit/s for NSFNET, which is quite close to the value of 98.6 Tbit/s given in Table 8.  
It is worth noting that in our analysis, we used unidirectional traffic demands, i.e., one-way traffic. In this context, a bidirectional flow of information is described by utilizing two unidirectional demands, one for each direction. Alternatively, if bidirectional traffic demands (two-way) are employed, the bidirectional flow can be described with just one demand. The results given in Tables 7 and 8 for network capacities are based on unidirectional traffic demands, so we can denote these capacities as unidirectional capacities. On the other hand, bidirectional network capacity corresponds to using bidirectional traffic demands. In this case, the values of \(C_{net}\) given Tables 7 and 8 must be halved. Although Table 5 gives results for the channel capacities considering a link, Table 8 provides results for the average channel capacity considering a network, and it is noteworthy that for \(R_{s}=64\) Gbaud and \(L=800\) km in the first table, and for \(L_{s}=80\) km and the COST239 in the second table, the capacities are approximately the same (750 Gbit/s). This result arises because the link length of 800 km is close to the average path length of the COST239 network, which is 682 km. It is important to note that a critical point in this analysis is the definition of the span length, which, in both cases in this comparison, is set equal to 80 km. In this way, for \(L=800\) km, we have 10 spans, while the number of spans varies with the path length. As for the other networks, where the average path lengths are much longer than the link lengths defined in Table 5, it is not possible to make such a comparison.
## 2 Architecture of the DT System of Solar Car  
The DT system architecture of a solar car contains four layers: physical layer, connection layer, virtual layer and service layer, and the composition architecture is shown in Figure 1.  
The physical layer is composed of physical objects, and mainly includes the solar car, as well as the internal and external environment, and the various operational logics existing between them. Concretely, the external environment in this system includes weather conditions such as temperature, wind and light intensity, and road conditions such as road slope and road surface unevenness. The actual vehicle used in this system is the self-developed \"Sun Shuttle III\" four-seater solar car. The appearance and specifications are shown in Figure 2 and Table 1, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Curb Mass & 395 kg \\ Dimensions & 5000 \(\times\) 1650 \(\times\) 1200 mm \\ Wheelbase & 2850 mm \\ Battery Capacity & 20 kWh \\ Solar Panel Area & 5 m\({}^{2}\) \\ Max Power & 12 kW \\ Top Speed & 130 km/h \\ Range & 700 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of “Sun Shuttle III”.  
The connection layer is also named the data layer. In this system, in addition to the inherent data in the physical space, the data also includes multi-mode and multi-type operational data collected by various sensors in real time. Based on the 4G communication technology and TCP, the collected sensor data are finally transmitted to the cloud platform to realize the information interaction between physical and virtual entities.  
The virtual layer, also called the model layer, refers to the DT model of the solar car, which is a virtual copy of the physical entity, including the mechanism model and the data-driven model. The model is jointly built by CarSim (an automotive system simulation software developed by Mechanical Simulation Corporation) and Simulink, which can run in parallel and interact with the real vehicle in real time. Among them, \"dynamic\" is the key characteristic of this model, which means the model needs to have the abilities of self-learning and self-adjustment.  
The service layer, i.e., the functional application of the DT, includes but is not limited to dynamic monitoring, report output, data analysis, data storage, and algorithm optimization. After the DT model runs in simulation, it can perform real-time monitoring of the real vehicle, driving strategy optimization and energy consumption prediction, etc.  
Figure 1: Elements of the presented architecture.  
Figure 2: Appearance of “Sun Shuttle III”.
# Targeted Universal Adversarial Examples for Remote Sensing
## 4 Experiments
### Data Descriptions  
We utilized three remote sensing image datasets for scene classification: UCM ([[http://weegee.vision.ucmered.edu/datasets/landuse.html](http://weegee.vision.ucmered.edu/datasets/landuse.html)]([http://weegee.vision.ucmered.edu/datasets/landuse.html](http://weegee.vision.ucmered.edu/datasets/landuse.html)) (accessed on 1 September 2022)) (UC Merced Land Use Dataset), AID (Aerial Image Dataset) ([[https://captain-whu.github.io/AID/](https://captain-whu.github.io/AID/)]([https://captain-whu.github.io/AID/](https://captain-whu.github.io/AID/)) (accessed on 1 September 2022)) and EuroSAT [27]. The details of the three datasets are elaborated as follows.  
**UCM**: There are 2100 overhead scene images with 21 land use classes in UCM. For each class, there are 100 aerial images, 256 \(\times\) 256, which are extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The 21 land-use classes are agricultural, airplane, baseball diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis court.  
**AID:** AID is a large-scale aerial image dataset collected from Google Earth imagery. AID consists of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks, and viaduct. There are 10,000 images in total in AID, and the image numbers for each class are not identical, varying from 220 to 420. The pixel size of each image is 600 \(\times\) 600 pixels.  
**EuroSAT**: EuroSAT is a satellite image dataset for land use and land cover classification tasks, created from satellite images taken by the satellite Sentinel-2A via Amazon S3; it covers over 34 European countries. It consists of 27,000 labeled images measuring 64 \(\times\) 64 pixels with 10 different classes: AnnualCrop, Forest, HerbaceousVegatation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, and SeaLake.