esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 3 Model
### Defendence Model  
We compute frequently recurring bottleneck paths under routing policies within the ICARUS framework. This generates a critical edge set that identifies high-risk links along with their attack frequency. Edges with higher frequencies handle greater traffic volumes, making them the most vulnerable components of the LSN and highly susceptible to targeted attacks.  
Under the K-Bottleneck Minimize strategy, each candidate path evaluates the overall vulnerability by accumulating the risk weights of the links it passes through and prioritizes paths with the smallest weights to avoid high-risk links. In this way, the most vulnerable high-capacity links in the network will be effectively bypassed. If attackers want to achieve the same destructive effect, they must use more bots or use higher bandwidth to attack multiple candidate links at the same time.  
As illustrated in Figure 3a, adversaries first perform active probing from compromised terminals to map routing paths and then utilize this information to orchestrate DDoS attacks. As illustrated in Figure 3b, our routing mechanism strategically avoids these critical links. While attackers previously required minimal traffic to congest highly utilized bottleneck links, the diversion strategy forces them to generate significantly larger attack volumes, leveraging the high-capacity nature of ISLs to achieve comparable disruption.  
To characterize the distribution patterns of these metrics, we employ cumulative distribution function (CDF) plots. The CDF is a fundamental statistical function that  
Figure 3: Mitigation DDoS attack through Bottleneck Minimize routing scheme. (**a**) Attacker probes to obtain LSN information; (**b**) Routing avoids the bottleneck link via alternate paths.  
characterizes the probability that a random variable \(X\) takes a value less than or equal to a specified threshold \(x\), thereby capturing the overall distributional characteristics of the dataset. The formal definition of the CDF is given in Equation (5):  
\[F_{X}(x)=P(X\leq x) \tag{5}\]  
where \(X\) represents the random variable under analysis, and \(F_{X}(x)\) quantifies the cumulative probability across the distribution. This approach enables evaluation of attack by mapping the likelihood of adversarial success under varying resource constraints.
## 4 Discussion  
This paper deals with a flux-controllable NI HTS flux-switching machine (FSM) for electric vehicle (EV) applications. In a variable-speed rotating machine for EVs, flux-regulation capabilities are very important for constant output operation. Conventional HTS rotating machines have excellent flux-regulation capabilities, but for an HTS rotating machine using no-insulation (NI) HTS field coils, ensuring flux-regulation capabilities is difficult, due to their inherent charge and discharge delays. However, the NI winding technique can dramatically improve the operational stability of HTS field coils. Therefore, research to implement an HTS rotating machine with flux-regulation capabilities,  
Figure 21: Phase current of Phase C in the resistance-load condition according to the MMF of the additional field winding.  
Figure 22: Torque curve according to the MMF of the additional field winding.  
while improving the operating stability of the HTS field coil via the NI winding technique, is required for EV applications.  
In this paper, we propose an HTS rotating machine with a flux switching structure and an NI HTS field coil. The proposed NI HTS flux-switching machine (FSM) uses additional windings for flux regulation, which enables feasible flux control. In addition, an NI HTS field coil was fabricated and tested, because the characteristic resistance, \(R_{C}\), of the NI HTS field coil is required as a design parameter. Based on the \(R_{C}\) value of the fabricated NI HTS field coil, an NI HTS FSM was designed, and the flux-regulation performance outcomes were analyzed. The flux-regulation performance capabilities were analyzed using a combination of the finite element method (FEM) and an electrical circuit analysis. The electrical circuit model was devised based on the equivalent circuit model of the NI HTS coil. The simulation results showed that the charging time constant (\(\tau\)) of the NI HTS FSM is 608 s, and that the coil current becomes fully charged approximately 9500 s after the excitation current reaches the target. This means that the HTS FSM using NI HTS field coils cannot control the flux by adjusting the field current of the HTS field coil. Therefore, additional windings are required for the flux control of the NI HTS FSM. In a simulation of the model with additional windings for flux regulation, the flux linkage and back-EMF characteristics, the output characteristics under a resistance load, and the torque characteristics of the proposed machine, were investigated. The simulation results demonstrated that the NI HTS FSM is capable of effective flux-regulation performance, enabling power and torque regulation. However, further research on the applicability of the proposed NI HTS FSM is needed, with the fabrication and testing of prototype models.
## 1 Introduction
Consequently, the side-lobe clutter and ghost artifact can be suppressed in the beamforming output due to their lower coherence relative to the main-lobe signal. DMAS beamforming in combination with adaptive weighting has been adopted not only in medical ultrasound but also in photoacoustic imaging [19]. Note that, though the original form of DMAS beamforming is computationally demanding due to the multiplication between every possible channel pair, a low computational cost formulation has been exploited and implemented in real time on a research scanner [20]. However, since DMAS imaging is based on the multiplicative coupling of RF channel signals, it demands oversampling to avoid aliasing the corresponding spectral components for imaging. We have previously developed an alternative high-order baseband DMAS beamforming (BB-DMAS) [21] to avoid the aforementioned oversampling because the multiplication of baseband channel data does not lead to undesired spectral components at high frequencies. When the order of baseband DMAS beamforming is represented as a rational \(p\) value larger than unity, the magnitude of time-delayed channel signals is scaled by \(p\)th root and then restored by \(p\)th power after channel sum. Note that a higher \(p\) value corresponds to higher spatial coherence in BB-DMAS beamforming and thus leads to higher image contrast ratios. Note that an improved RF version of DMAS beamforming has recently been developed as the \(p\)-DAS [22], which is also capable of flexibly tuning the image quality by the \(p\) value but still at the cost of oversampling. Similar to other coherence-based beamforming, the downside of DMAS beamforming includes the loss of image magnitude in the speckle region [23] and also dark-region artifacts [24] around wire reflectors and hyperechoic cysts. Previous attempts to introduce DMAS beamforming into multi-angle PW imaging has been reported but only in either dimension of the echo matrix. For example, the echo matrix can be firstly summed in the dimension of the PW transmit angle to achieve synthetic transmit focusing before applying DMAS processing in the dimension of the receiving channel, as in Reference [25]. On the contrary, when the echo matrix is firstly summed in the dimension of the receiving channel to render the low-quality image as in CPWC imaging, DMAS processing can be performed to extract signal coherence in the dimension of the PW transmit angle [26]. In this study, a novel 2D-DMAS beamforming is proposed to further boost the image quality of multi-angle PW imaging. Baseband multiplicative coupling in both the dimensions of PW transmit angle and receiving channel is used to extract 2D spatial coherence of the echo matrix. Note that the original CPWC imaging can be regarded as 2D-DAS since the echo matrix is coherently summed in both the dimensions of PW transmit angle and receiving channel. Using 2D-DAS as a reference, image quality of 2D-DMAS beamforming will be compared to that of one-dimensional DMAS beamforming for multi-angle PW imaging.
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 3 Antenna Results and Discussion
### Reflection Coefficient  
The reflection coefficient characteristic of the proposed metamaterial-based array dipole antenna is displayed in Figure 10. The proposed antenna offers a wide operating bandwidth from 23.1 GHz to 44.8 GHz. With an Agilent E8364B network analyzer in openair, ambient settings, the S-parameters of the recommended antenna are measured. As seen in Figure 10, the slight difference between the simulation and measurement results is caused by cable/connector losses. A very broad impedance bandwidth of 63.92% (S11 \(\leq-10\) dB) is provided by the recommended antenna between 23 GHz and 45 GHz. The proposed bandwidth spans the whole n257, n258, and n259 frequency ranges as well as the n260 and n261 frequency bands for 5G NR millimeter-wave communication, which is recommended by the Third Generation Partnership Project (3 GPP) and the ITU.
## 2 Materials and Methods  
### Fabrication of Trench MOS Barrier Schottky Structures (TMBS) and Growth of GNW  
A trench MOS barrier Schottky diode with blocking voltage capability up to 60 V was fabricated for this study. The substrate used was an n-type lightly doped epitaxial silicon wafer on a highly doped n-type substrate. Active area openings were formed on the epitaxial layer with trench structures (see Figure 1). Polysilicon was used as the gate electrode inside the trenches. An insulating layer, consisting of thermal oxide, insulates the polysilicon and the epitaxial silicon area to form the trench MOS structure. The pre-metal dielectric layer was then deposited and patterned for dry etching of contact opening on the epitaxial layer. The substrate was cleaned with an HF solution to remove native oxide on the contact opening prior to deposition of titanium (Ti) film on the structures to form the Schottky junction. The substrate was then annealed at 700 \({}^{\circ}\)C for 60 s to form a titanium silicide (TiSi\({}_{2}\)) layer on the silicon epi. The TiSi\({}_{2}\)-TMBS is the baseline sample in this experiment. In the second part of the experiment, TiSi\({}_{2}\) was replaced with GNW to form the Schottky contact with silicon epi as discussed below.  
The GNW layer was grown in a radio frequency (RF) PECVD chamber from Oxford Instrument. The gases used were acetylene (C\({}_{2}\)H\({}_{2}\)) and hydrogen (H\({}_{2}\)), processed at a low pressure of 800 mTorr and low RF power of 100 W. The substrate heating temperature was 700 \({}^{\circ}\)C throughout the growth step of 45 min. For this experiment setting, C\({}_{2}\)H\({}_{2}\) and H\({}_{2}\) gases were used in a plasma chamber to grow the GNW on silicon. H\({}_{2}\) was used to promote graphitization during the growth process and remove the amorphous carbon. The C\({}_{2}\)H\({}_{2}\) molecules were adsorbed on the silicon surface. Dissociation and out-diffusion of hydrogen occurred leaving carbon atoms on the silicon surface creating nucleation sites [16]. Additional carbon atoms grew the graphene nanosheet on the silicon surface parallel to the substrate until it reached a steady-state growth condition. At this stage, the vertical nanowalls dominated the growth process resulting in the formation of two-dimensional carbon structures with a high aspect ratio.  
After the GNW growth completed, a metal layer (Ti) of 200 nm was deposited using RF sputtering method on top of the entire TMBS structure to become the anode. The samples were patterned via photolithography and etched leaving both the GNW and Ti layers on top of the trench areas only. A metal layer was also deposited on the back of the heavily doped substrate which acts as the cathode.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 3 Results  
### Urban Heat Island Modeling  
Overall, all nine models showed high predictive power (adjusted R\({}^{2}\)) and low RMSE values (Table 3). Across all models, we observed no less than 96.44% of the variation in observed temperatures explained by the focal buffers with an average R\({}^{2}\) of 0.9818. In addition, the maximum observed RMSE was 0.1837 \({}^{\circ}\)C during the afternoon model in Baltimore, MD. The average RMSE was 0.1276 \({}^{\circ}\)C. As noted in previous studies, the afternoon models had a consistently lower performance and were likely attributed to more detailed descriptions of urban environments such as variations in humidity, building heights, and the effects therein on wind patterns and building density [12].  
Across all three study areas, common patterns were observed: forested and otherwise vegetated areas are cooler than urbanized areas; lower-density urban areas are often cooler than high-density urban areas; morning high temperatures are always lower than afternoon and evening low temperatures; the greatest relative concentration of heat is in the morning; major arterial roadways are visible in all UHI surfaces, though they are often amplified in the evening (Figures 1-3). High density, sparsely vegetated areas near major arterial roadways appear to consistently amplify heat within these metropolitan regions.  
Figure 3: Baltimore, MD (**A**) aerial imagery with major waterbodies masked; (**B**) morning UHI; (**C**) afternoon UHI; (**D**) evening UHI.  
Figure 2: Washington, D.C. (**A**) aerial imagery with major waterbodies masked; (**B**) morning UHI; (**C**) afternoon UHI; (**D**) evening UHI.
# A Review of Smart Grid Anomaly Detection Approaches
## 4 Discussion  
Research indicates that the operational components of a power management system are susceptible to many forms of abnormalities resulting from cyber-attacks, such as those affecting the topological database or device configuration, whether caused by unintentional or intentional alterations. When discussing anomalies, it is crucial to consider the harm or decay of materials and devices incorporated into the network, as well as weather conditions, and the network infrastructure.  
This paper provides an initial classification of seven study objects related to SG anomalies that are amenable to AI-based methods or tools, such as distributed computing-capable devices that could give the network more intelligence. So, this classification is intended to be a contribution, and of course, it can be improved.  
Each object of study describes multiple scenarios, each with different proposed solutions to detect anomalies of different natures; consequently, some solutions are not comparable. However, for each method or framework identified, we have tried to show, where possible, any weaknesses, opportunities for improvement, or alternatives against which they were evaluated.  
This classification can be also a limitation of this work since it could be incomplete. We intend to improve and refine it in future works. Some papers are complex to place in one category or another as they analyze intersecting scenarios, such as unusual consumption behavior, data integrity attacks, and network intrusions.  
The works reviewed address increasingly novel issues to propose optimal solutions, independent of the grid model and endowed with greater involvement of artificial intelligence, which is an evolution towards what could be called new-generation smart grids.  
There are no definitive or unique anomaly detection solutions due to factors such as geographic location, installed infrastructure, and regulations. For this reason, the collection of reviewed works is a guiding reference on how to approach the study of diverse types of anomalies and can be a starting point for working to prevent anomalous behavior in SGs.
## 3 Results
### One-Minute Standing Position Test  
In the one-minute standing position, no statistically significant differences in spinal angles were observed between implant conditions. The average values of the angles remained relatively constant regardless of the added implant volume (Figure 4) (see Appendix A for Table A1 contents).  
Figure 4: Mean and standard deviation and \(p\)-values of the lateral orbital margin (LOM) and tragus of the ear (TRG) angle (**a**); TRG and seventh cervical process (C7) angle (**b**); first, third, and eleventh thoracic vertebrae (T1, T3, and T11) and the first lumbar vertebrae (L1) angle (**c**); L1 and four lumbar vertebrae (L1 and L4) angle (**d**); and L4 and second sacral vertebra (S2) angle (**e**) across implant conditions.  
No statistically significant differences were found in the CoP measurements between implant conditions. The AP and ML CoP displacements and velocities showed mean values similar across implant volumes with most having similar standard deviations (Figure 5) (see Appendix B for Table A2 contents).  
The sEMG activity results showed no statistically significant differences in the activity of the ECM, ESC, PM, UT, LT, LM, and Tra/IO muscles across implant conditions. The mean activation levels of these muscles were similar between the control and other implant volumes, even when an anterior load was added. Significant statistical differences were found in the LS activity associated with an observed power of 0.864 (F = 4.416, \(p\)-value = 0.006). Pairwise comparison analysis revealed statistical differences between the 220 mL implant condition and the control and 315 mL condition. Specifically, lower LS values were observed with the 220 mL implant compared to the two conditions. The group data generally showed a high standard deviation, especially in the LS muscle, indicating some variability between conditions within the group of adult females studied (Figure 6) (see Appendix C for Table A3 contents).  
Figure 5: Mean, standard deviation, and \(p\)-values of the anteroposterior (AP) (**a**) and mediolateral (ML) (**b**) center of pressure (CoP) displacements and ML CoP velocity (**c**). Median, interquartile range, and \(p\)-value of the AP CoP velocity (**d**) across implant conditions.
## 3 Results and Discussion
### Effects of Different Sterilization Treatments on the Functional Properties  
#### 3.4.1 TPC  
As shown in Figure 5A, the TPC was enhanced significantly after all different sterilization treatments (\(p<0.05\)), among which TS showed the most significant effect, which increased TPC from 491.63 to 626.96 \(\upmu\)g GAE/mL (27.45%), followed by TSN (20.48%). Similar observations of the enhanced TPC were also reported in US-treated kiwifruit juice [37], strawberry juice [38], and rosehip nectar [39]. This result occurred principally because the US or US-combined treatment could lead to the disruption of cell walls and vacuoles of plant tissues and lead to a cavitational collapse in the surrounding colloidal particles, which all contribute to the dissolution and release of phenolic compounds into the juice [37]. Additionally, a high-intensity US treatment might produce nascent hydroxyl groups, which could be combined with the aromatic rings to produce more phenolic compounds [21].  
Figure 5: Effects of different sterilization treatments on the functional indicators of grape juice. (**A**) Total polyphenol content (TPC); (**B**) Total Anthocyanin Content (TAC); (**C**) Total Flavonoid Content (TFC); (**D**) DPPH scavenging activity; and (**E**) FRAP assays.
# Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
## 2 Materials and Methods
### Geospatial and Statistical Datasets  
The datasets we used in this study include lake bathymetric data, remote sensing products, and demographic and economic data (see Table 1). More details about each dataset are described below. We derived the influencing factors from remote sensing images at the pixel scale and then aggregated them to the county-level statistics.  
\begin{table}
\begin{tabular}{c c c} \hline
**Remote Sensing Dataset** & **Resolution** & **Application** \\ \hline Lake Bathymetric Digital Elevation Model (DEM) & 5 m & Extracting lake elevation \\ European Commission’s Joint Research Centre (JRC) Global & 30 m & Extracting lake surface area \\ Surface Water (GSW) & 30 m & Extracting evapotranspiration \\ Penman–Monteith–Leuning Evaportranspiration V2 (PML\_V2) & 500 m & Extracting precipitation \\ Daily Surface Weather and Climatological Summaries & 1 km & Extracting annual temperature and \\ (Daymet V3) & 1 km & temperature difference between day and night \\ Terra Land Surface Temperature and Emissivity & 1 km & Extracting land cover information \\ The USGS National Land Cover Database (NLCD) & 30 m & Extracting land cover information \\ \hline \end{tabular}
\end{table}
Table 1: Multi-source remote sensing datasets used in this study.  
Figure 2: Our study area with lake bathymetric data in the state of Minnesota.
###### Abstract  
In this study, a regression model of paddy soil properties using diffuse reflectance spectroscopy was developed to replace chemical soil analysis as a more efficient alternative. Soil samples were collected and analyzed from saltwater paddy fields located in Jeongnam-myeon, Hwaseong-si, Gyeonggi-do in the Republic of Korea, and the spectral data of wet and dry soil were collected. The regression models were compared and analyzed using partial least squares regression (PLSR) with Savitzky-Golay smoothing (SG smoothing) and Standard Normal Variate (SNV) preprocessing to predict the soil properties. Analysis showed that the predictive regression model of wet soil with SG smoothing and an SNV did not meet the evaluation criteria of a fair model. However, the regression model of dry soil with SG smoothing was fair for clay, pH, EC, and TN at RPD = 1.90, 1.87, 1.60, and 1.43 and R\({}^{2}\) = 0.79, 0.81, 0.64, and 0.64, respectively, while the regression model of dry soil with an SNV was good for clay, pH, EC, and TN at RPD = 2.21, 1.96, 1.70, and 1.44 and R\({}^{2}\) = 0.84, 0.81, 0.76, 0.69, respectively. When developing predictive regression models of soil properties, the accuracy for dry soil was higher than that for wet soil, and when applying a single round of preprocessing, the regression model with SNV preprocessing was more accurate than that with SG smoothing.  
soil properties; VIS-NIR; DRS; preprocessing; PLSR +
Footnote †: journal: Applied Sciences