esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Liquid Film Characteristics during Atomization of Aluminum Alloy Powder
## 3 Simulation Results and Discussion
### Liquid Film Characteristics with Disk Structure at Different Speeds
(**a**) t = 2.0 s; (**b**) t = 2.2 s; (**c**) t = 2.4 s; (**d**) t = 2.6 s; (**e**) t = 2.8 s; (**f**) t = 3.2 s.  
the condition of insufficient surface tension, and it will finally separate to form a liquid filament, as shown in Figure 21c; t = 2.4 s. At the initial stage of liquid filament formation, the roots of the liquid filament are thicker at the edge of the disk, and they are gradually narrowed and refined with the evolution of the liquid filament, as shown in Figure 21d; t = 3.6 s. With the destruction of the surface tension at the roots of the liquid filament, another liquid filament is produced and gradually extends and becomes longer under the centrifugal force, as shown in Figure 21e; t = 4.8 s. With the further development of the liquid film and liquid filaments, the number of liquid filaments gradually increases, as shown in Figure 21f; t = 6.0 s. The liquid filament moving away from the disk edge does not break immediately but extends radially outward to a certain distance, and a liquid filament flow will be formed on the disk edge, as shown in Figure 21g; t = 8.0 s. After the liquid filament is formed, it is first stretched to the limit length to maintain the same length, and the diameter of the tip is gradually reduced. Moreover, the liquid filament is unstable at the R-T interface and generates an interface wave disturbance along the direction of the liquid filament, and the end of the liquid filament breaks and forms atomized droplets, as shown in Figure 21h; t = 10.0 s. The radial velocity of the liquid filament is very small compared with the tangential velocity of the liquid filament at the edge of the rotating disk, and the running track of the liquid filament is similar to an involute line. Droplets formed by atomization will fly and disperse along the original movement track and gradually evolve into a ball, as shown in Figure 21i; t = 15.0 s.
## 5 Simulation Results
### Chain Networks
From Figure 9(b), we find the ratios of the average end-to-end delays are 89.87%, 88.97%, 81.38%, and 87.91%, respectively. Compared to the case of \(\sigma_{dB}=0.01\) dB, the number of scheduled transmissions are smaller. However, the proposed MAC protocol still achieves considerable improvements in both end-to-end throughput and delay.
## 2 Materials and Methods
### Model Validation Metrics  
To evaluate the performance of the prediction models, 'R-squared' and 'absolute errors in n \(\sigma\)' were used as model validation metrics. The absolute error in \(n\)\(\sigma\)' is shown in Equation (3). First, the absolute errors between the actual measured value of PM\({}_{2.5}\), \(y_{i}\), and the predicted value of PM\({}_{2.5}\), \(\widehat{y_{i}}\) were calculated. Second, the percentage of absolute errors, \(|\widehat{y_{i}}-y_{i}|\), that fell within n \(\sigma\) were calculated. The closer the absolute error of \(n\)\(\sigma\)' is to 1, the more accurate the predictive model.  
\[Absolute\ error\ in\ n\ \sigma=\frac{\textit{The number of data with absolute error}<n\ \sigma}{\textit{Total number of data}} \tag{3}\]  
The standard of 'Absolute Error in \(n\)\(\sigma\)' is determined based on the standard deviation of PM\({}_{2.5}\). For a conservative judgment, 1 \(\sigma\) (standard deviation) was set to 12. As shown in Table 3, the same threshold of 'absolute error in \(n\)\(\sigma\)' was set for both NW and AD.
## 4 Results  
### Temporal and Spatial Changes  
#### 4.1.1 FVC  
The FVC is an indicator factor characterizing the vegetation growth state and can reflect the size of the photosynthetic area of plants and the lush degree of vegetation growth. Based on the 16-day vegetation index product of the EOS/MODIS satellite from 2000 to 2021, this study used the pixel dichotomy method to calculate the FVC and then used  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Slope (\(Veg_{OBS}\))**} & \multirow{2}{*}{**Driving Factors**} & \multicolumn{2}{c}{**Classification Criteria for Driving Factors**} & \multicolumn{2}{c}{**Contribution Rate of Driving Factors (\%)**} \\ \cline{3-6}  & & **Slope (\(Veg_{CC}\))** & **Slope (\(Veg_{HA}\))** & **Natural Factors** & **Human Activities** \\ \hline \multirow{4}{*}{\(>\)0} & CC and \(HA\) & \(>\)0 & \(>\)0 & Slope (\(Veg_{CC}\))/Slope & Slope (\(Veg_{HA}\))/Slope \\  & CC & \(>\)0 & \(<\)0 & 100 & 0 \\  & \(HA\) & \(<\)0 & \(>\)0 & 0 & 100 \\  & CC and \(HA\) & \(<\)0 & \(<\)0 & Slope (\(Veg_{CC}\))/Slope & Slope (\(Veg_{HA}\))/Slope \\ \(<\)0 & CC & \(<\)0 & \(>\)0 & 100 & 0 \\  & \(HA\) & \(>\)0 & \(<\)0 & 0 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Determination criteria for driving factors of changes in vegetation ecological parameters and calculation method for contribution rate.  
the maximum synthesis method to obtain the monthly and annual FVC. The temporal variation trend and the spatial distribution characteristics of the FVC in the Kokyar Project Area were discussed.  
The FVC in the Kokyar Project Area presented the distribution characteristics of \"high in the south and low in the north\" (Figure 2a). Additionally, from 2000 to 2021, the FVC increased significantly (Figure 2b), with an annual average increase of about 1.09, and the \"greening\" in the northern part of the Kokyar Project Area was obvious (Figure 2a). The FVC in 2021 reached the highest value over the years (46.60), with an increase of 107.11% compared with that in 2000 (22.50). This shows that over the past 22 years, the \"Kokyar Greening Project\" has brought huge ecological benefits to the area. However, the northern border area of the study area remains in a desertification situation, i.e., the phase D area of the project (Figure 1), indicating that the \"Kokyar Greening Project\" still needs to be further implemented.
# [PERSON] on Korean Block Ciphers
## 5 Conclusions  
In this paper, we presented the optimized implementation of ARX-based Korean block ciphers in quantum gates. The proposed implementation achieved the optimal number of qubit and quantum gates by carefully designing the adder circuit and its reversible gates. This is the first implementation of Korean block ciphers in quantum gates. The result shows that the CHAM block cipher requires the lowest quantum resources, which means the CHAM block cipher is the most vulnerable towards  
\begin{table}
\begin{tabular}{c c|c c c c c c c} \hline \hline \multirow{2}{*}{**Plainetet (bit)**} & \multirow{2}{*}{**Key (bit)**} & \multicolumn{2}{c}{**Qubits Used**} & \multicolumn{2}{c}{**Toffoli Gates**} & \multicolumn{2}{c}{**CNOT Gates**} & \multicolumn{2}{c}{**X Gates**} \\ \cline{3-10}  & & **[PERSON]** & [PERSON] [9] & **[PERSON]** & [PERSON] [9] & **[PERSON]** & [PERSON] [9] & **[PERSON]** & [PERSON] [9] \\ \hline
32 & 64 & 97 & 96 & 1290 & 512 & 3706 & 2816 & 42 & 448 \\
48 & 72 & 121 & 120 & 1982 & 864 & 5006 & 3312 & 42 & 792 \\
48 & 96 & 145 & 144 & 2074 & 864 & 5866 & 4800 & 45 & 768 \\
64 & 96 & 161 & 160 & 3162 & 1344 & 8890 & 5184 & 54 & 1248 \\
64 & 128 & 193 & 192 & 3286 & 1408 & 9238 & 7396 & 57 & 1216 \\
96 & 96 & 193 & 192 & 5172 & 2496 & 14,436 & 9792 & 60 & 2400 \\
96 & 144 & 241 & 240 & 5360 & 2592 & 14,960 & 10,080 & 64 & 2448 \\
128 & 128 & 257 & 256 & 7942 & 4352 & 22,086 & 17,152 & 75 & 4224 \\
128 & 192 & 321 & 320 & 8192 & 4416 & 22,784 & 17,472 & 80 & 4224 \\
128 & 256 & 385 & 384 & 8444 & 4608 & 23,484 & 26,624 & 81 & 4352 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between SPECK and SIMON in terms of quantum resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Block Cipher** & **Qubits Used** & **Toffoli Gates** & **CNOT Gates** & **X Gates** \\ \hline [PERSON] et al. [6] & 984 & 151,552 & 166,548 & 1456 \\ \hline [PERSON] et al. [21] & 976 & 150,528 & 192,832 & 1370 \\ \hline [PERSON] et al. [20] & 864 & 16,940 & 107,960 & 1507 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantum resources to implement AES-128.  
the Grover search algorithm. We also evaluated the SPECK and SIMON on quantum gates. The result shows that hardware-oriented block cipher (i.e., SIMON) shows the lower quantum resources than SPECK.  
Investigation, H.K. ([PERSON]), H.K. ([PERSON]) and J.P.; Software, K.J. and H.S.; Supervision, H.S.; Writing--original draft, K.J.; Writing--review and editing, S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (<Q|Crypton>, No. 2019-0-00033, Study on Quantum Security Evaluation of Cryptography based on Computational Quantum Complexity)  
The authors declare no conflict of interest
## 3 Preliminaries and Definition  
### Cryptographic Background  
#### 3.1.1 Notations  
If \(X\) and \(Y\) are strings, then \(X||Y\) denotes the concatenation of \(X\) and \(Y\). If \(S\) is a set, then \(s\stackrel{{\$}}{{\leftarrow}}S\) denotes that \(s\) is selected uniformly at random from \(S\). For a probabilistic algorithm \(\mathcal{A}\), \(x\stackrel{{\$}}{{\leftarrow}}\mathcal{A}\) assigns the output of \(\mathcal{A}\) to the variable \(x\). If \(\mathcal{A}\) is deterministic, we drop the dollar sign above the arrow. All algorithms considered in this study are possibly randomized unless indicated otherwise.  
Figure 2: Data aggregation path.
# Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System
## 4 Proposed System
### Uploading and Sharing Processes
#### 4.3.1. Uploading Process  
The procedure for exporting the EHRs is shown in subsequent steps:  
1. [label=Step 1]
2.  Mobile Gateway execution  
The mobile adapter creates a blockchain username and password to access the Ethereum blockchain, and then authorizes a query to upload a transaction. The web interface connects the user node blockchain to nodes in the cloud. These configurations establish a processing request (Ts) within the data-uploaded process as a query that contains application information.
3. [label=Step 2]
4. Data Uploading to IPFS Storage (Performed by Healthcare Administrator)  
The EHR supervisor issues commands to the smart contract, such as a reminder of a fresh data-uploading invitation, followed by acquiring a transaction again from gateways. The smart contract validates transactions based on the policies listed in the policy repository. When the transaction is authorized, a response is sent to the gateways through blockchain clients/users, allowing information to be uploaded (Step 3 in Figure 5).  
When a transaction is authorized, the mobile gateways can use an asymmetric encryption technique to encode the EHR files using the public key of the EHR administrator (Step 7 in Figure 6). Subsequently, the encrypted information is posted to the IPFS cloud platform, which is linked to the gateway's blockchain web application (Step 4 in Figure 6).  
3.  Mining the Transaction  
In addition to sending EHRs into cloud storage, the information from the publishing communications (Area ID, Patient ID) and hash value is stored in the unverified transaction pool. Miners group transactions throughout the pools into chunks for computing regularly. The identity of the miner is sent to other miners to confirm the block's data. When all the miners approve, the authenticated block and its signatures are chronologically added to the blockchain. Subsequently, all service providers acquire that transaction and utilize the blockchain application to replicate the blockchain copies (Steps 6, 7, and 8 in Figure 5).
# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Dataset  
For lung segmentation, we used two public CXR datasets: JSRT [14] and MC [15]. JSRT is a dataset jointly created by the Japanese Society of Radiological Technology and the Japanese Radiological Society, and contains 247 the posterior-anterior (PA) CXR images. Among these images, 154 images have a pulmonary nodule, and the other 93 images are normal. All the images are sized \(2048\times 2048\) pixels and associated with the labeled annotations of other anatomical structures, including the lungs. The MC dataset is jointly populated by the National Library of Medicine and the Department of Health and Human Services in the U.S. This dataset consists of 138 PA CXR images; among these images, 80 images are normal and 58 correspond to tuberculosis patients. The images are sized \(4020\times 4892\) or \(4892\times 4020\) pixels.  
For the spinal cord segmentation task with the MRI images, we used the dataset employed in the spinal cord gray matter challenge [17]. The dataset involves images collected from the following four sites: University College London (_site1_), Polytechnique Montreal (_site2_), University of Zurich (_site3_), and Vanderbilt University (_site4_). Specifically, the dataset consists of 80 MRI images corresponding to 20 cases from each site. The data from these sites exhibit individual visual characteristics mainly due to the imaging equipment from different vendors being used. Therefore, in the evaluation of the domain robustness of the segmentation networks, the images from each site were considered to correspond to a single domain.  
Figure 3 shows sample images of each dataset. From this figure, it can be observed that there exists a certain degree of distributional shift (i.e., domain difference) among the datasets. For example, the MC and JSRT datasets have considerably different visual features as can be observed through the annotations on the sample images in Figure 3. The JSRT dataset contains images from patients having lung nodules, which can be characterized as a small spot. In contrast, the MC dataset includes images from tuberculosis patients whose lesions are widely spread over the lung area. Such a domain shift is difficult to be resolved via image preprocessing (e.g., histogram equalization) or data augmentation (e.g., brightness and contrast adjustment) as observed in the following experiments.
## 6 Evaluation and Experimental Results
### End-to-End Transmission Delay Reduction
#### 6.3.2 Propagation Delay  
For calculating the propagation delay of HIP DEX handshake packets, we use the following formula:  
\[\mathrm{P_{r}}=\frac{\mathrm{D}}{\mathrm{T_{s}}},\]  
where:  
* \(\mathrm{P_{r}}\) is the propagation delay in m/s;
* \(\mathrm{D}\) is the distance between the Initiator and the Responder. In the experiment, we chose nodes with 1 meter of distance;
* \(\mathrm{T_{s}}\) is the propagation speed, which is calculated as speed of \(light\times velocity\)_factor_. The speed of light is 2.998 \(\times\) 10\({}^{8}\) m/s and the velocity factor = 1 because the propagation medium is the air.  
The \(\mathrm{P_{r}}\) is equal to 0.003 \(\mathrm{\SIUnitSymbolMicro s}\) for one packet. Consequently, \(\mathrm{P_{r}}\)(handshake) = 0.012 \(\mathrm{\SIUnitSymbolMicro s}\). It is a negligent value that has no impact on the total handshake transmission delay.
# A Cost-Efficient MCSA-Based Fault Diagnostic Framework for SCIM at Low-Load Conditions
###### Abstract  
In industry, electric motors such as the squirrel cage induction motor (SCIM) generate motive power and are particularly popular due to their low acquisition cost, strength, and robustness. Along with these benefits, they have minimal maintenance costs and can run for extended periods before requiring repair and/or maintenance. Early fault detection in SCIMs, especially at low-load conditions, further helps minimize maintenance costs and mitigate abrupt equipment failure when loading is increased. Recent research on these devices is focused on fault/failure diagnostics with the aim of reducing downtime, minimizing costs, and increasing utility and productivity. Data-driven predictive maintenance offers a reliable avenue for intelligent monitoring whereby signals generated by the equipment are harnessed for fault detection and isolation (FDI). Particularly, motor current signature analysis (MCSA) provides a reliable avenue for extracting and/or exploiting discriminant information from signals for FDI and/or fault diagnosis. This study presents a fault diagnostic framework that exploits underlying spectral characteristics following MCSA and intelligent classification for fault diagnosis based on extracted spectral features. Results show that the extracted features reflect induction motor fault conditions with significant diagnostic performance (minimal false alarm rate) from intelligent models, out of which the random forest (RF) classifier was the most accurate, with an accuracy of 79.25%. Further assessment of the models showed that RF had the highest computational cost of 3.66 s, while NBC had the lowest at 0.003 s. Other significant empirical assessments were conducted, and the results support the validity of the proposed FDI technique.  
machine learning; peak detection; fault diagnosis; frequency domain; low-load condition
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 3 System Design
### mmWave: Millimeter Wave Frontends  
WISCANet supports mmWave RFFEs to extend the Ettus USRP products beyond sub-6 frequencies to enable mmWave applications. We integrate these front-ends by using LFRX/LFTX or BasicTX/BasicRX daughterboards on X310 or N210 radios to transmit and receive directly at baseband to interface with mmWave frontends. This configuration is directly supported by UHD and does not require any extensions or modifications to the WISCANet framework or baseband software packages.  
These modified USRP devices connect directly to a Sivers Evaluation Kit (EVK) or similar mmWave frontend (such as the TMYTEK BBOX). This mmWave front-end provides an up-converter to the mmWave RF frequency and an optional integrated antenna array controlled by the baseband software. The WISCANet baseband processing architecture is unchanged, and existing applications can be directly extended to mmWave operating frequencies. This provides an accessible and affordable mmWave testbed for the academic and independent research communities.
# An Overview of Key Technologies in Physical Layer Security
## 3 Secure Multi-Antenna Techniques
### Artificial Noise (AN) Precoding
It was shown in [50] that when an eavesdropper uses zero-forcing to mitigate the interference introduced by the artificial noise transmission with a large number of receiving antennas and knows some of the main channel's CSI, the secrecy of the system can be compromised.
## 2 Research Method
### Quantitative and Qualitative Decision Criteria
#### 2.2.1 Quantitative Decision Criteria  
* Cost of hydrogen production (USD/kg H\({}_{2}\))  
This criterion represents the cost to produce one kilogram of H\({}_{2}\) from the given technology. It is an indicator of the economic viability of each technology. To account for the time-value of money, H\({}_{2}\) production costs that are collected from the published literature are adjusted to the 2024 USD using the Chemical Engineering Plant Cost Index, or CEPCI [11]. To adjust H\({}_{2}\) product cost from a previous year to 2024, Equation (1) is applied:  
\[\text{H}_{2}\text{ Cost}_{2024}=\text{H}_{2}\text{ Cost}_{\text{YOP}}\Big{(} \frac{\text{CEPCI}_{2024}}{\text{CEPCI}_{\text{YOP}}}\Big{)} \tag{1}\]  
where the subscript YOP signifies the year of publication of the H\({}_{2}\) production cost.  
* Energy consumption (MJ/kg H\({}_{2}\))  
This quantitative criterion represents the amount of energy (MJ) required to produce one kilogram of H\({}_{2}\) from the given technology.  
* Global warming impact (Kg CO\({}_{2}\)-eq/kg H\({}_{2}\))  
This quantitative criterion represents the global warming potential (GWP) of the H\({}_{2}\) production technology and shows how many kilograms of CO\({}_{2}\) are emitted per kilogram of H\({}_{2}\) produced.  
* Technology energy efficiency  
The energy efficiency of a technology is defined as the ratio of the lower heating value (LHV) of produced H\({}_{2}\) to total energy supplied to the H\({}_{2}\) production process. This efficiency is expressed as a percentage value. Some publications reported this process energy efficiency in terms of high heating value (HHV), and in order to keep comparisons between the production technologies consistent, these values are converted herein to be in terms of LHV by multiplying the reported value by the ratio of H\({}_{2}\) LHV to HHV, as shown in Equation (2).  
\[\frac{\left(\text{LHV}\right)_{\text{H2}}}{\left(\text{HHV}\right)_{\text{H2 }}}=\frac{120\text{ MJ}/\text{kg}}{142\text{ MJ}/\text{kg}}=0.8451 \tag{2}\]  
In addition to creating consistency in this comparative assessment, evaluating the technology energy efficiency in terms of LHV rather than HHV is more conservative, as it gives the lower bound of the efficiency and, therefore, does not overestimate this efficiency value.
# Shadow Fleets: A Growing Challenge in Global
## 4 Approaches to Detecting Dark and Gray Fleet Characteristics
### Ownership Registration  
Dark fleet vessels often have obscure ownership structures, complicating accountability. These vessels are commonly registered to owners in countries known for less stringent regulatory oversight, complicating the tracking and regulation of their activities, and contributing to difficulties in ensuring their compliance with international norms [48].  
The practice of registering vessels through complex networks of shell companies and offshore entities is designed to obscure their true ownership and, consequently, the responsibility for the vessel's activities. This lack of transparency hinders efforts to enforce maritime laws and regulations, as it becomes challenging to identify and hold the actual owners accountable for violations [49; 50].
## 3 Results
### Optical--Synthetic Aperture Radar (SAR) Results  
Results from objective 1 indicated that NDVI_RE1 was our best-performing optical index, but also that seasonal and species level effects were present in our data. Therefore, our analyses integrating optical and SAR data focused solely on spring samples (\(n\) = 332) and modeled species separately in objective 2. Each SAR metric (backscatter intensities and indices, polarimetric parameters, and InSAR coherence) was individually added to NDVI_RE1 regression models to predict cover crop biomass and assess how model statistics changed with each additional SAR metric. Figure 5: The natural log of cover crop biomass plotted against the normalized difference red-edge index (NDVI_RE1), which is calculated as (\(\frac{\text{(B8A-B5)}}{\text{(B8A-B5)}}\), where B5 is the 5 th band of the Sentinel-2 satellite (the first red edge band) and the narrow band near-infrared band (B8A). Only spring data are displayed (March–May). Model fits and errors are supplied in the upper left for the entire dataset (black) and by species. An untransformed biomass estimation is appended on the right of the figure for reference. The addition of most SAR metrics did not substantially improve model fits or reduce errors compared to models that had only NDVI_RE1 (Table 4) but were species-specific. However, combining InSAR coherence and NDVI_RE1 did increase our ability to predict the biomass of all three cereal grass species (Table 4), with improved model fits (i.e., R\({}^{2}\) increased by 4 to 11% and AICc decreased by 15 to 24 among species). Wheat, trtictale, and cereal rye models with InSAR coherence also demonstrated reductions in error that ranged from small to moderate: RMSE decreased by 42, 182, and 36 kg ha\({}^{-1}\) and MAE decreased by 95, 84, and 52 kg ha\({}^{-1}\), respectively. Models including both NDVI_RE1 and InSAR coherence were significantly better than the model using NDVI_RE1 alone in all three species (F = 15.75-26.86, \(p\) < 0.001; Table 5). Similarly, models with both NDVI_RE1 and InSAR coherence demonstrated an increased predictive ability in terms of cross-validation compared to models with just NDVI_RE1