esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods
### Comparisons: 1.0 km LAI-RM v. MODIS LAI and 12.0 km MODIS LAI v. EPIC LAI  
#### 2.3.1 Comparison 1: LAI-RM to MODIS LAI (Mc5; Mc6) at 1.0 km  
We first compared LAI-RM (1.0 km resolution) to the corresponding MODIS LAI product (collection 5 (i.e., Mc5) [1.0 km resolution] and collection 6 (i.e., Mc6) [0.5 km resolution]). MODIS LAI Mc5 was compared one to one with the LAI-RM, whereas the Mc6 product was averaged over four 0.5 km cells within each 1.0 km area. MODIS variability was estimated temporally bracketing LAI values \(\pm\) 1 MODIS LAI date (i.e., 8 days prior and post) on each side of the comparison date.
# A Complex Network Important Node Identification Based on the KPDN Method
## 5 Results and Analysis
### Analysis of Real Networks
#### 5.1.1 Size of Real Network from \(10^{3}\) to \(10^{4}\)
Therefore, the remaining number of network edges is also an important indicator. According to the actual data, it can be observed from Figure 6 that the proposed method in this paper has an obvious effect on the change in the edge residual rate index of the subgraph. Taking Figure 6c as an example, When 20% of the nodes in the original network are deleted, the calculation results are: KPDN: 19.6875%, PageRank: 19.8865%, Degree: 21.10%, DWT: 21.72%, KPD: 22.34%, WL: 26.69%, INCC: 38.67%, K-shell: 33.88%, CC: 34.94%, Random: 62.51%, CI: 96.13%. Similarly, it can be seen that the effects of Figure 6a-d,f are quite ideal, and the effect of Figure 6e is also slightly better. Figure 5: Comparison of subgraph sensitivity on real-world networks (scale \(10^{3}\)–\(10^{4}\)). (**a–f**) correspond to: (**a**) email-univ, (**b**) p2p-Gnutella08, (**c**) fb-pages-government, (**d**) US-airports, (**e**) power-eris1176, and (**f**) bio-grid-worm.
# Vulnerability of Satellite Quantum Key Distribution to Disruption from Ground-Based Lasers
## 3 Results
### Excess Photon Rate  
Using the above results and following the calculations outlined in Section 2.4, we can estimate the excess photon rate at the QKD receiver for a given launch power from the laser terminal. We assume good seeing conditions and low atmospheric turbulence (\(C_{n}^{2}=1\times 10^{-15}\) m\({}^{-2/3}\)) because these are the conditions under which _Micius_ is currently capable of operating, and are the conditions under which many future QKD satellites will need to work and will achieve their highest quantum key rate.  
The larger the transmitting aperture of the laser terminal, the more optical power will be incident on the satellite due to the reduced beam divergence. In this calculation, we have assumed that the laser launch aperture will have a diameter between 10 cm and 30 cm since these sizes are comparable to the optics currently used on a range of small-to-medium vehicle-mounted optical directed energy devices. (The calculations here further assume that the useable aperture of a telescope is roughly 80% of the diameter of the primary optic).  
Figure 4 shows the estimated excess photon rates at the ground station for a laser terminal launching 1 kW of power with a 10 cm aperture 100 km away from the ground station, and Figure 5 shows the excess photon rate for the same launch power via a 30 cm aperture 1000 km away from the ground station. The laser power of 1 kW was chosen based on the optical loss from laser terminal, to satellite, to ground station, and the need to obtain certain excess photon rates at the ground station to achieve disruption. (The exact relative locations of the laser terminal and ground station in these example calculations correspond to the locations shown in Figure 3 above).  
Atmospheric turbulence increases both the divergence of the laser beam and causes the direction of the beam to jitter. This significantly increases the optical losses. For example, for the scenario in Figure 5, increasing the ground-level turbulence to a moderate level of \(C_{n}^{2}=1\times 10^{-14}\) m\({}^{-2/3}\) reduces the excess photon rate at the ground station by about 60%. While this would significantly decrease the number of excess photons (or require a 3-fold increase in laser power to maintain the same received photon rate), the increased atmospheric turbulence will also significantly increase the losses on the QKD transmission, reducing the achieved quantum key rate, meaning fewer photons from the mobile laser terminal will be required to achieve complete disruption. (It should be noted that, over the 100 km and 1000 km distances involved in these calculations, it cannot be assumed that weather conditions are the same or comparable for ground sta  
Figure 4: Excess photon rate at the QKD ground station and optical power loss over time (represented in seconds elapsed since the satellite came into view of the ground station) achieved for a 1 kW laser launched via a 10 cm telescope 100 km from the QKD ground.
# Plasma-Activated Water Affects the Antioxidant Contents in Water Spinach
## 2 Materials and Method
### PAW Treatments
#### 2.3.2 Phase-2: Pot Experiment in Soil with Fertilizer  
PAW can only offer nitrogen-based nutrients, such as NO\({}_{2}\)\({}^{-}\), NO\({}_{3}\)\({}^{-}\), and NH\({}_{4}\)\({}^{+}\), for the growth of water spinach. However, the lack of other nutrients in the soil might restrict plant growth. Therefore, fertilizer was added to the soil in the Phase-2 experiment to fulfill the other essential nutrients (e.g., phosphorus, potassium, etc.) necessary during plant growth.  
Cultivation soil with fertilizer was used for planting and the soil without fertilizer served as a control. Eight liters (3.64 kg) of cultivation soil mixed with 40 g of fertilizer were prepared at a ratio of 12% nitrogen, 18% phosphorus, and 12% potassium according to the manufacturer's instructions (BENEFITCOTE Fertilizer, Green Orchids Company, New Taipei City, Taiwan). Untreated water spinach seeds were grown in planting pots containing 600 g of soil with or without fertilizer.  
Plasma treatment was applied to the irrigation water; thus, four treatment combinations are listed as follows: Group 1, non-treated water and no fertilizer (RO); Group 2, non-treated water but with fertilizer (RO + Fert); Group 3, using PAW but no fertilizer (PAW), and Group 4, using PAW and fertilizer (PAW + Fert). Each pot of water spinach was watered with 150 mL of either PAW or RO water three times weekly for 4 weeks. A daylight T9 fluorescent lamp (6500 K, 18 W) was used as an aid for 13 h from 7 a.m. to 8 p.m. (Figure 2).  
Figure 2: A schematic diagram of the Phase-1 and Phase-2 pot experiments.
## 3 Implementation
### Acquisition of Depth Map  
Depth information is critical to improve the geometric projection results. In this section, we use a neural network called monocular residual matching (monoResMatch) network to infer accurate and dense depth estimation in a self-supervised manner from a single image [25]. As shown in Figure 4, first, a multi-scale feature extractor takes a single raw image as input and computes deep learnable representations at different scales from quarter resolution \(F_{L}^{2}\) to full-resolution \(F_{L}^{0}\) in order to toughen the network to ambiguities in photometric appearance. Second, deep high-dimensional features at input image resolution are processed to estimate, through an hourglass structure with skip-connections, multi-scale inverse depth (i.e., disparity) maps aligned with the input and a virtual right view learned during training so as to make the network learn to emulate a binocular setup; thus, allowing further processing in the stereo domain. Third, a disparity refinement stage estimates residual corrections to the initial disparity. In particular, deep features from the first stage and back-warped features of the virtual right image are used to construct a cost volume that stores the stereo matching costs using a correlation layer. Finally, the depth map can be obtained according to the theory of binocular matching.  
Figure 4: Illustration of monoResMatch architecture [25].
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## Appendix A Protograph-Based QC-LDPC Codes
### Construction for Non-Binary Codes  
In the non-binary codes, the entries of codewords, parity-check, and generator matrices are in a finite field \(\mathbb{F}_{q}\), where \(q\) is a power of a prime. There are several ways to construct non-binary PB QC-LDPC codes. The base matrix \(\mathrm{B}\in\mathbb{F}_{2}^{\lambda\times\omega}\) typically remains binary, as defined as in Appendix A.1. We use the unconstrained and random assignment strategy in [80] to extend \(\mathrm{B}\) to a PCM.  
There is significant flexibility in selecting the edge weights for constructing non-binary QC-LDPC codes, which can be classified as constrained or unconstrained ([80] Section II). In this work, we focus on an unconstrained and random assignment strategy, where each edge in \(T_{\mathcal{C}}\) corresponding to a \(1\) in the binary matrix \(B\) is replaced with a coefficient \(h_{i,j}\in\mathbb{F}_{q}\). Alternatively, these coefficients could be selected based on predefined rules to ensure appropriate edge-weight diversity, which can lead to enhanced performance. This methodology allows non-binary codes to retain the structural benefits of binary QC-LDPC codes while extending their functionality to finite fields, offering improved error-correcting capabilities for larger \(q\) values.
# Enhancing CT Segmentation Security against Adversarial Attack: Most Activated Filter Approach
## 3 Experiments
### Robustness of Distinguishing Gaussian Noise from Adversarial Attacks on CT Segmentation  
In the approach targeting filters highly activated by perturbations, there was an observed tendency to misclassify certain noise types, such as Gaussian noise, as adversarial attacks. This occurs even when such noise has only a minor effect on segmentation performance. For instance, images with Gaussian noise at a variance of 0.001, which result in a reduced dice score of 0.4474 from 0.4524, are consistently identified as adversarial by our binary classifiers trained on FGSM, BIM, and SMIA attacks (on filter number 2).  
To address this issue, the proposed approach is to develop noise detectors to distinct between the noise and the attack methods. Since filter number 2 is the most activated by attacks, the noise detector is trained against the adversarial samples. It showcased the highest differential accuracy between Gaussian noise and adversarial samples, achieving both a PPV and sensitivity of 1.0. In addition, out of 228 samples for each attack method, although the classifier is relatively successful in distinguishing Gaussian noise from FGSM and BIM attacks, it mislabeled 24 samples as SMIA instead of Gaussian noise. This is likely due to the objective function of SMIA incorporating a Gaussian kernel, which complicates the discrimination between the adversarial sample and Gaussian noise.  
For comparative insights, other baseline detectors are also examined. MagNet identifies Gaussian noise as genuine samples with a high accuracy of 99.12% but falls behind in detecting true adversarial attacks, as shown in Table 6. The Park method also correctly identifies Gaussian noise as genuine samples with an accuracy of 96.93%, highlighting its relative effectiveness in distinguishing Gaussian noise. However, this also suggests its inconsistent performance against other types of adversarial perturbations.
## 6 Performance Evaluation
### Performance Evaluation Metrics  
We considered the following evaluation metrics in our simulation study.  
1. **Interest satisfaction rate (ISR):** The Interest satisfaction rate is a measure of the total number of Data packets \(D_{pkt}\) successfully received in relation to the total number of Interest packets \(I_{pkt}\) generated in the network. The high ISR corresponds to the high QoS of the network and vice versa. The mathematical formulation for ISR is presented as follows. \[\text{Interest satisfaction rate}=\frac{\sum_{i=1}^{N}D_{pkt}^{i}}{\sum_{i=1}^{n}I_{ pkt}^{i}}\]
2. **Interest satisfaction delay:** Interest satisfaction delay is defined as the time (\(t\)) taken by the Interest (I) to reach the provider (P), i.e., \(t_{1,p}\), the Interest processing at the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Simulator & NS-3 (ndnSIM 2.5) \\ Area & 100 \(\times\) 100 \\ Total number of sensor nodes & 100 \\ Node distribution & layered distribution \\ Communication stack & NDN \\ Wireless interface & IEEE 802.15.4 \\ Propagation delay model & Constant Speed Propagation Delay Model \\ Energy consumption & 0.5 \(\upmu\)J/bit \\ PIT Timer & 4 sec \\ Interest Packet size & 48 bytes \\ Data Packet size & 96 bytes \\ Simulation time & 1800 sec \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation Parameters.  
provider node \(t_{l,p}^{p\eta}\), and the time taken by the Data packet to reach again the consumer node C, i.e., \(t_{D,c}\).  
Interest satisfaction delay \(=t_{l,p}+t_{l,p}^{p\prime}+t_{D,c}\)
3. **Total number of packets processed:** It corresponds to the total number of Interests and Data packets processed in the network.
4. **Total energy consumption:** The total energy consumption is defined as the amount of energy consumed by all the nodes (N) in Interest and Data packet forwarding. \[\text{Energy Consumption}=\sum_{i=1}^{N}e_{i}^{l_{pits}}+e_{i}^{D_{pits}}\]
5. **Communication overhead:** The communication load refers to the overall bandwidth consumption that may happen due to Interest or Data packet transmissions in the network.
# The Indicator of GDI Engine Operating Mode and Its Influence on Eco-Driving
## 2 GDI Engine Modes of Run and Their Impact on Fuel Consumption
The longer periods or higher regeneration frequency may offset the savings obtained when supplying the stratified mixture. Particulate Matter (PM) emissions from gasoline direct injection (GDI) engines, particularly Particle Number (PN) emissions, demand a trade-off between the measures that best reduce PN (such as high inlet air temperatures, high exhaust temperatures, or low aromatic fuels) and those which give the efficiency gains. A fully vaporised fuel-air mixture will give low levels of PN emissions. Any fuel that remains in a liquid state at ignition, especially when a stratified mixture is created, burns as a diffusion flame, leading to high levels of PN emissions known as a pool fire [28; 29]. In addition, the GDI engine produces higher PN emissions at high load or if there is fuel liquid impingement on the piston bowl and cylinder wall fuel liquid impingement on the piston bowl and cylinder wall [30]. Various injection and airflow strategies have been implemented, such as multiple injections and spray-guided techniques, to achieve the proper mixture in the combustion chamber of a GDI engine. Major injector manufacturers began developing a piezo-outwardly type injector and a multi-hole type injector to improve engine performance and reduce exhaust emissions [15; 21]. The less economical, homogeneous mixture in the GDI engine is created by fuel injection at the beginning of the intake stroke. The injected fuel mixes with the air and evaporates during the rest of the intake stroke and the compression stroke [31]. Therefore, the type of the formed mixture is determined by the fuel injection phase. In practice, the described process of mixture creation and the operation of the catalytic converter is strongly affected by driver behaviour. Based on the analysis of HEV eco-driving possibility, the authors concluded that fuel economy is strongly affected by the drivers' perception [32]. In the experiment, parameters of three possible injection modes were registered and analysed:  
1. The late fuel injection in the stratified mode (fuel is injected late into the cylinder during the compression stroke enables lean mixture burning by keeping rich combustible mixture near spark plug during discharge time). 2. The early fuel injection in the homogeneous mode (fuel is injected in the suction stroke to form a homogeneous mixture due to sufficient time for the mixture preparation). 3. The fuel shut off during deceleration, such as coasting or braking when there is no torque requirement. The GDI system consists of sensors that detect the engine conditions and the engine ECU, which controls the system based on these sensors' signals and actuators. The engine ECU carries out fuel injection control, idle speed control and ignition timing control [33]. The way the driver interacts with vehicle controls strongly influence the engine's operation,especially the change of the fuel injection mode, and affects fuel consumption. It was the subject of the experiment described in Section 4.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 4 Discussion  
### Classification Accuracy Analysis  
According to the classification accuracy evaluation results derived using the confusion matrix, the object-oriented classification methods were superior to the pixel-based classification methods; this finding was consistent with the results of a previous study [11]. [PERSON] et al. [11] comparatively analyzed the performances of pixel- and object-based approaches in mapping rubber plantations and showed that \"the rule-object-based phenology approach (with an overall accuracy of 77.5% and a kappa coefficient of 0.66) and nearest-neighbor-object-based phenology approach (91.0% and 0.86) achieved higher accuracies than the rule-pixel-based phenology approach (72.7% and 0.59)\". Although poor classification results were not obtained by these approaches in our study, some dirt roads were misidentified as farmland, mainly because only asphalt roads and buildings were selected as the training samples for building objects (not including dirt roads), and some training samples of farmland objects were in the bare land class. Since this study focused on the identification of rubber plantation through remote sensing, we did not conduct an in-depth exploration of the estimation of dirt roads.  
The selection of the optimal feature subset is a crucial step in the estimation process, which influences the classification accuracy and the simplicity of the model for use in practical applications. For the selection of the optimal feature subset based on pixel information (see Figure 7), with increasing number of feature variables, the overall classification accuracy of features 1-26 showed an upwards trend with slight fluctuations. When the number of features was 6, 19, and 26, the overall accuracy reached peak values of 96.95%, 97.71%, and 98.47%, respectively. There was not a large difference among the three overall accuracies. To simplify the practical application of the proposed approach with few feature variables, we again evaluated the classification results using the validation samples. The results are provided in Table 6. For the pixel-based RF classifier, there were slight differences in the overall accuracy, KIA, producer's accuracy, and user's accuracy among the sets of classification results when the first 6, 19, and 26 features were selected. Therefore, the first six features can be selected as the optimal feature subset to build the pixel-based RF classifier in practical application. However, for the pixel-SVM classifier, as the number of features increased, the overall accuracy, KIA, producer's accuracy, and user's accuracy of the classification results increased. This finding shows that different classifiers respond differently to the number of characteristic variables. Although the classification accuracy of the pixel-based SVM was better, the number of feature variables used was too high (up to 26), and making the approach inconvenient for practical applications. Therefore, we do not advocate for the use of this method.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Ref. No.**} & **Peak Gain (dBi)** & **HPBW (Degrees)** & **MIMO Features/ Number of Ports** & **TARC** \\ \hline
[49] & \(\sim\)10 & - & Yes/2 & \(<\)0.2 \\ \hline
[4] & \(\sim\)8 & \(\sim\)37 & No/1 & - \\ \hline
[50] & \(\sim\)4.82 & - & Yes/2 & - \\ \hline Proposed Work & \(\sim\)10.5 & \(\sim\)60 & Yes/2 & \(<\)0.1 \\ \hline \multirow{2}{*}{**Ref. No.**} & **Largest Dimension Per Port (cm)** & **Simulated Frequency Range Closest to** & **Measured Frequency Range Closest to** & **ECC** \\ \cline{2-5}  & **2.585** & \(\sim\)4.2–4.65 & \(\sim\)4.2–4.4 & \(<\)0.0004 \\ \hline
[4] & 6.5 & - & \(\sim\)4.18–4.39 & - \\ \hline
[50] & - & \(\sim\)4–4.3 & - & - \\ \hline Proposed Work & 8.231 & \(\sim\)4.21–4.38 & \(\sim\)4.26–4.4 & \(\sim\)0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with the previous radio altimeter studies in the literature.
## 4 Experimental Setup and Simulation Results
### Index Comparison  
For index-based performance analysis, we have considered eight indexes where three are for settling time, three are for peak overshoot, one is for the objective function, and the remaining is for converge speed of the optimization algorithms. As the indexes have different ranges, we have normalized them using their maximum values and plotted them in the spider graph to compare the indexes. The normalized index value is calculated using the following equation:  
\[\text{Index Value}=\frac{\text{\emph{Actual Index Value}}}{\text{\emph{Corresponding Index Maximum Value accross the Algorithms}}}.\]  
From the above equation, it can be understood that any value which is closer to 1 represents the maximum value. As this is a minimization problem, the lesser the value is, the better the index is. Hence, the smaller boundary inside the solid line represents the better solution.  
Also, from Figure 12, it can be easily seen that although for some indexes PPA reaches the maximum value, extremely smaller values in other indexes makes the inner area smaller compared to other algorithms. So, it can be said that PPA outperforms PSO and EHO in this problem.