esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation  
### Governing Equation  
The flow governing equations are solved using a cell-centered control volume space discretization approach for the fluid domain. The continuity equation and RANS equation can be expressed in Cartesian tensor form as follows:  
\[\frac{\partial\rho}{\partial t}\ +\ \frac{\partial}{\partial x_{i}}\ (\rho u_{i})\ =\ 0 \tag{17}\]  
\[\frac{\partial}{\partial t}\ (\rho u_{i})\ +\ \frac{\partial}{\partial x_{i}} \left(\rho u_{i}u_{j}\right)\ =\ -\frac{\partial p}{\partial x_{i}}\ +\ \frac{\partial}{ \partial x_{i}}\left[\mu\left(\frac{\partial u_{i}}{\partial x_{i}}\ +\ \frac{\partial u_{i}}{\partial x_{j}}\ -\ \frac{2}{3}\delta_{ij}\frac{ \partial u_{i}}{\partial x_{j}}\right)\right]\ +\ \frac{\partial}{\partial x_{i}}\left(-\rho\overline{u_{i}u_{j}^{ *}}\right) \tag{18}\]  
The Reynolds term (\(\overline{u_{i}^{*}u_{j}^{*}}\)) is an additional term that represents the effects of turbulence and must be modeled in order to close the RANS equation. A common method employs the Boussinesq hypothesis to relate the Reynolds stress to the mean velocity gradients as follows:  
\[-\rho\overline{u_{i}^{*}u_{j}^{*}}\ =\ \mu_{t}\left(\frac{\partial u_{i}}{ \partial x_{j}}\ +\ \frac{\partial u_{j}}{\partial x_{i}}\right)\ -\ \frac{2}{3}\left(\rho k\ +\ \mu_{t}\frac{ \partial u_{k}}{\partial x_{k}}\right)\delta_{ij} \tag{19}\]Here, the turbulent viscosity, \(\mu_{t}\), is computed by combing \(k\) and \(\omega\) (explained earlier in Section 2.3) as  
\[\mu_{t}=\alpha^{*}\frac{\rho k}{\omega} \tag{20}\]  
where, the coefficient \(\alpha^{*}\) damps the turbulent viscosity causing a low-Reynolds number correction.
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 3 Methods
### Dataset
#### 3.1.2 Data Labeling  
The labeling work consists of two steps: preliminary sample area selection and pixel-by-pixel labeling. The former involves creating a mask through visual interpretation (without the need for precise boundaries) and then using the mask to clip the corresponding area of UAV images as the preliminary sample area. The latter involves detailed outlining of the edges of solid waste to create labeled data. All training dataset are from the preliminary sample area. The selection rules for the preliminary sample area can be summarized as follows:  
1. Area Selection: Priority is given to identifying large, concentrated areas. In the sample area selection, garbage piles with an area of at least 1 square meter, as determined by expert judgment, and consisting of more than 900 pixels based on the spatial resolution, are considered. If there are objects of similar nature near a large garbage pile, they are also considered as garbage.
2. Covering Multiple Scene Types: Solid waste piles commonly found in daily life are widely distributed in various environments such as buildings, roads, grasslands, rivers, and forests. In order for the model in this study to possess the ability to recognize solid waste in multiple scene types, the sampling areas need to include various scene types.
3. Balancing Garbage Category Proportions: Based on the overview of the entire image, garbage can be classified into three categories: construction waste, household waste, and mixed waste. Construction waste includes materials such as wood, bricks, and concrete generated during construction and demolition processes. Household waste includes garbage such as plastic, kitchen waste, and paper generated in daily life. Mixed waste refers to garbage that is a mixture of different sources and types. This study does not distinguish between different types of garbage, but when creating sampling areas, it is necessary to maintain a balance in the quantities of these three types to meet monitoring requirements.
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 2 Methodology
### Applied Algorithms for Predicting Lung Cancer
#### 2.12.4 Decision Tree  
Decision trees have been used widely in classification and regression problems. Therefore, they were aptly used to predict lung cancer, where the target variable is a binary value (0 or 1) indicating no cancer and cancer, respectively. The decision tree establishes a relationship between the target output and other input features [35].  
The following is the definition of the Gini impurity used to gauge a decision tree split's quality:  
\[\text{Gini}(D)=1-\sum_{i=1}^{C}p_{i}^{2} \tag{5}\]  
In the dataset \(D\), \(p_{i}\) is the probability of class \(i\), and \(C\) is the number of classes in the dataset.  
Mathematical Representation  
Let the feature set be denoted by \(X\) and the target variable by \(Y\), where \(Y\in\{0,1\}\). The definition is such that 0 denotes no cancer, and 1 denotes the presence of cancer. Thus, the decision function is defined as \[f(X)=\begin{cases}0&\text{if }X\in R_{0}\\ 1&\text{if }X\in R_{1}\end{cases}\]  
where \(R_{0}\) and \(R_{1}\) correspond to the regions representing class 0 and class 1, respectively.
## 4 Discussion
### Time and Frequency Domain Similarities in the S1 & S2 Dataset  
GEE proved to be an invaluable tool for processing and downloading Copernicus data quickly, efficiently and accurately. Cloud-processed time series of VWC derived from S2 data produced the typical phenological profile of winter wheat at the study site as was also presented by [PERSON] et al. [54]. The LAI mapping of winter wheat at the BVCR based on S1 radar data was explored by [PERSON] et al. [25], demonstrating the efficacy of merging distinct S1 acquisition geometries to retrieve wheat LAI in southern Argentina. In the present study, we analyzed the RVI for two distinct S1 orbits that allow having two radar channels to merge the S1 & S2 time series. Regarding the wheat growth cycle at the focused area, we found the same temporal trends for S1 RVI and S1 LAI GP model developed by [PERSON] et al. [25]. In Figures 3d and 4d an amplitude offset of RVI for orbit 68 and orbit 141 can be appreciated. These RVI differences, which are related to the mismatch of the local incidence angle, provide valuable information on the three-dimensional structure of the vegetation. The BVCR's representative crop rotation at the study site was faithfully reproduced by long time series of S2 GP VWC (see Figures 3a and 4a). The S2 GP VWC and S1 RVI time series follow the same temporal patterns, so three phenological cycles (corn-sunflower-wheat) can be captured at the study sites (see Figure 1). In addition, this time correspondence enabled us to gauge the spectral similarities in the S1 & S2 dataset. The frequency domain analysis determines the peak and position of the principal spectral components that are mainly related to crop rotation and phenology. Seeking to customize the response of the SM kernels we selected Q = 4 prioritizing the most representative spectral components in the S1 & S2 dataset (see Section 2.5).
## 3 Results and Discussion
### Leakage Detection with the Gauge-Type Pressure Sensor Feedback  
From the previous application, the following hypothesis emerged: the signal from the pressure sensor can be used to reveal leakage of the system. Leakage during actual manufacturing process could be hazardous for the operators of the oven, so a leakage scenario was induced in controlled conditions. The sealing ring was deliberately broken at a given point. For a thorough evaluation, the ring was removed completely; there was no barrier for the vapour to leak from the process zone to the outer atmosphere. The pressure probe and the thermocouple placed inside the oven were kept in the same position for each measurement. The experiment was executed under the heating power of 100%.  
Figure 9 show a significant difference in the curves if the sealing is compromised. If the sealing of the workspace is damaged, the conditions of safe operation are also broken, while not only the hot vapours can leave the workspace, but harmful and difficult to sense VOCs (volatile organic compounds) as well (e.g., solder-flux gases).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Vapour Height, mm**} \\ \hline Heating Power, (oven & & & \\ setting, \%) & 60\% & 80\% & 100\% \\ \hline Gauge-type pressure & & & \\ sensor & & & \\ Thermocouple & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of two different sensors by vapour height [mm].  
Figure 8: Pressure and temperature measurement with the variation of the heating delay; top: full profile; center: heating phase; bottom: solder break phase (HD = heating delay).  
Focusing on the heating, Figure 9 is consequential with the previous cases in the sealed case. However, when the sealing is broken, the signal response becomes flat.  
According to Figure 9 and to Figure 11 (see below), the leakage can be identified with the applied pressure sensors. The pressure amplitude drops significantly to a near-zero level when the sealing fails, while the temperature sensors still read similar results to the standard programmed temperature profile. With temperature workspace monitoring, it is not possible to pinpoint slight changes in the sealing (see Figure 10).  
It is recommended to define a practical threshold for the pressure signal at heating power settings above 60%. Should the value does not reach the threshold during heating, the oven controls can warn about a leakage. In a real-life scenario, the hazard in the vicinity of the oven can be reported to the operator at the electronics manufacturing shop floor. In our investigated case, this threshold could be set to 5 Pa, which is sufficient, considering  
Figure 10: Comparison between the temperature curves (soldering profiles) at the sealed and non-sealed state of the oven.  
Figure 9: Comparison between the pressure signals at the sealed and non-sealed oven during heating.  
even an unfiltered raw signal, as shown in Figure 11. The threshold setting should be set for each oven and data acquisition methodology.
## 2 Materials and Methods
### Enzyme-Linked Immunosorbent Assay (ELISA) Specific to S. Typhimurium OMPs  
The specific antibody responses of IgG, IgM, and IgG subclasses (IgG1 and IgG2a) to \(S\). Typhimurium OMPs were assessed using ELISA, as detailed previously [23; 28]. Using triplicate sera from immunized and non-immunized mice, the optimal concentrations of serum and enzyme conjugates were determined based on the method of the checkerboard test [29; 30]. The wells of microplates (Thermo Scientific Inc.) were coated with _Salmonella_ OMPs by incubating OMPs at 0.5 \(\,\mathrm{\SIUnitSymbolMicro g}/100\)\(\,\mathrm{\SIUnitSymbolMicro m}\) overnight at 4 \(\,\mathrm{\SIUnitSymbolMicro C}\), then blocked with 1% bovine serum albumin in PBS containing Tween 20 (PBS-T) at 37 \(\,\mathrm{\SIUnitSymbolMicro C}\) for 2 h. After three washes with PBS-T, the wells were treated with the sera diluted at a 1:200 ratio. The plates were incubated at 37 \(\,\mathrm{\SIUnitSymbolMicro C}\) for 1 h and then washed with PBS-T three times to remove antibodies with nonspecific binding to OMPs. Subsequently, the wells were treated with horseradish peroxidase (HRP)-conjugated antibodies, including the goat anti-mouse IgG (1:30000 dilution, Bethyl Laboratories, Montgomery, TX, USA), anti-mouse IgM (1:35000 dilution, Bethyl Laboratories), anti-mouse IgG1 (1:25000 dilution, Bethyl Laboratories), and anti-mouse IgG2a (1:30000 dilution, Bethyl Laboratories) secondary antibodies. After washing with PBS-T three times, 3,3\({}^{\prime}\),5,5\({}^{\prime}\)-tetramethylbenzamidetidine (TMB, Surmocrides Inc., Eden Prairie, MN, USA) was added to each well as a chromogenic substrate and the enzymatic reaction was quenched by adding 0.5 M \(\,\mathrm{H}_{2}\mathrm{SO}_{4}\) after 5 min of incubation. Colorimetric changes were measured at \(\mathrm{OD}_{450}\) using an Epoch plate reader (BioTek, Suwanee, GA, USA).
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 2 Strong Adaptive Kalman Filter (SAKF)
### Algorithm Designs
Generally, when the Equation (19) is satisfied, \(\theta\) should be maximized to achieve the highest robustness. However, the UAV navigation system is time-varying, the Equation (19) may not satisfy with a large \(\theta\). Thus, when in high-dynamic environment, the value of \(\theta\) is set to be relatively smaller. \(\theta\) is usually set at the range of \((0,0.02)\) in navigation. Here, \(\mathbf{\widetilde{S}}_{k}\) represents error covariance matrix of \(\mathbf{X}_{k}\)'s linear combination. Usually, \(\mathbf{\widetilde{S}}_{k}\) is set to be equal to \(\mathbf{I}_{k}\) (unit matrix) in navigation. **Part 4. ** The Q matrix update equation is given as follows  
\[\hat{\mathbf{Q}}_{k}=(1-\alpha_{k})\hat{\mathbf{Q}}_{k-1}+\alpha_{k}(\mathbf{K}_{k}\varepsilon _{k}\varepsilon_{k}^{T}\mathbf{K}_{k}^{T}+\mathbf{P}_{k}-\mathbf{\Phi}_{k/k-1}\mathbf{P}_{k} \mathbf{\Phi}_{k/k-1}^{T}) \tag{20}\]  
where \(\alpha_{k}\in(0,1)\) and \(\beta_{k}\in(0,1)\) respectively represent adjusting factors of Q-matrix and R-matrix. Essentially, they relate to the ratio between residual and historical information. The estimation's adaptive and tracking skills rely more on current information. Therefore, we import forgetting factors \(b_{\alpha}\) and \(b_{\beta}\) to reinforce the role of the residual. The recurrent equation is given as follows:  
\[\alpha_{k}=\frac{\alpha_{k-1}}{\alpha_{k-1}+b_{\alpha}} \tag{21}\]  
\[\beta_{k}=\frac{\beta_{k-1}}{\beta_{k-1}+b_{\beta}} \tag{22}\]  
where \(\alpha_{0}=\beta_{0}=1\), and \(b_{\alpha},b_{\beta}\in(0,1)\). When the value of \(k\) is large enough, there are \(\alpha_{k}\approx 1-b_{\alpha}\) and \(\beta_{k}\approx 1-b_{\beta}\). Generally, the value of \(b_{\alpha},b_{\beta}\) is between 0.9 and 0.999.
## 3 Results
### Hemolysis  
As previously discussed, the PHB polymer coating significantly reduced the Mg AZ31 alloy corrosion rate in SBF and reduced the concentration of corrosion byproducts in the corroding medium.  
Table 4 reports the optical density measurements utilized for the evaluation of the hemolysis ratio from the spectroscopic analysis of incubated extraction fluids of magnesium alloy with and without PHB polymer coating with diluted blood and the positive and negative controls. In the last column is reported the Mg\({}^{2+}\) concentration of the respective extraction fluids after 72 h of PHB-coated and bare alloy sample conditioning.  
The results indicate that the corrosion medium of the bare uncoated magnesium alloy induces a serious hemolysis process, with a rate that was as high as 37.61%, compared to that of the PHB-coated alloy samples, where the hemolysis rate was 4.19%, showing better blood compatibility and meeting the requirements for application as medical material (5%).  
The concentration of magnesium ions has a great influence on blood as well as on tissue cells. An AZ31 magnesium alloy implanted in the body, if characterized by a too high degradation rate, will lead to an excessive and dangerous increase of magnesium ion concentration in the fluid surrounding the implant with a detrimental effect on patient health.  
After 72 h SBF conditioning, the content of Mg\({}^{2+}\) ions in the bare magnesium alloy corrosion fluid was as high as 22.9 \(\pm\) 1.3 mmol/L, while that of the PHB-coated alloy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **OD Value** & **Hemolysis Rate** & **Mg\({}^{2+}\), mmol/L** \\ \hline AZ31 Mg & 0.2487 \(\pm\) 0.0775 & 37.61\% & 22.9 \(\pm\) 1.3 \\ PHB-coated AZ31 Mg & 0.0530 \(\pm\) 0.0117 & 4.19\% & 2.4 \(\pm\) 0.2 \\ Positive control & 0.6137 \(\pm\) 0.0117 & & \\ Negative control & 0.0285 \(\pm\) 0.0117 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed data of hemolysis rate testing.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Cultured 1 Day**} & \multicolumn{4}{c}{**Cultured 3 Days**} & \multicolumn{4}{c}{**Cultured 5 Days**} \\ \hline
**Extraction** & **Conc.** & **OD** & **RGR** & **Toxic** & **OD** & **RGR** & **Toxic** & **OD** & **RGR** & **Toxic** \\  
corrosion fluid was only 2.4 \(\pm\) 0.2 mmol/L, which remains in the range of variability of physiological fluids.  
This result is in line with the hemolysis results discussed before. The too high Mg\({}^{2+}\) ion concentration resulted in an increase of hemolysis that is to be attributed to the significant increase of solution ionic strength [53; 54]. As Mg\({}^{2+}\) ion concentration increases, the osmotic pressure of the solution rises. The osmotic differential can cause erythrocyte swelling and cell membrane rupture with release of free hemoglobin and consequent hemolysis of red blood cells.
# Improving Freight Traffic Efficiency at Urban Intersections Using Heavy Vehicle Platooning
## 3 Methodology
### Base Model Development  
The simulation experiments were conducted using VISSIM. The study site was a major intersection in Tallahassee, Florida (Orange Ave and Adams Street), selected due to its high volume of freight traffic. The simulation was configured to replicate real-world conditions, incorporating distinct behavior models for non-CVs, platoon leaders, and platoon followers.  
The development of a VISSIM base model is a critical step in traffic-simulation modeling, as it provides the foundation for subsequent evaluations and analyses. The base model was constructed to replicate real-world traffic conditions accurately while adhering to the Florida Department of Transportation (FDOT) Traffic Engineering Manual guidelines. The base model-development process involved the following components:  
* Network Design: The intersection layout, lane configurations, and approach geometries were modeled based on actual roadway characteristics.
* Signal Timing: The signal phasing and timing plans were obtained directly from traffic signal engineers, ensuring that the simulation accurately reflects real-world operations.
* Speed Profile: Speed limits and acceleration/deceleration profiles were assigned based on observed vehicle behavior to maintain realistic vehicle movements.
* Vehicle Input: Traffic input was derived from field-collected data, including traffic-movement counts, queue lengths, and average speeds, to represent real-world demand.
* Vehicle Routing: Origin-destination flows were modeled based on observed movement patterns to ensure that vehicle distributions were realistic.  
Once the base models were developed, a calibration procedure was conducted to ensure accuracy in simulating field operations, after which they were executed under the PM peak hour scenario.
# AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation
## 6 Discussion
### Future Directions  
Based on the existing study, multiple intriguing directions for future investigations arise:  
* Improvement of Image Data Quality: Future research should utilize clinically validated imaging modalities (e.g., high-resolution CT, MRI) to supplant the pseudo-randomly allocated images. This is expected to enhance the clinical significance and precision of the CNN-based models.
* Advanced Ensemble Strategies: Investigating alternate ensemble methodologies--such as weighted ensembling, hybrid stacking approaches, or the integration of supplementary base models--could enhance the equilibrium between precision and recall. Refining the amalgamation of numerical and visual predictions is crucial for enhancing clinical results.
* Dynamic Thresholding and Calibration Techniques: Creating adaptive thresholding methods that can be modified according to patient risk profiles or changing clinical recommendations may improve the system's real-time decision-making abilities. Consideration should be given to advanced calibration techniques, including utilizing Bayesian methods or ensemble calibration.
* Prospective Clinical Validation: A crucial further step is to authenticate the diagnostic system in a prospective clinical study. Incorporating the model into a clinical workflow will yield insights into its efficacy, resilience, and effect on patient outcomes in a practical environment.
* Integration of Supplementary Data Modalities: Enhancing the framework to encompass additional data sources--such as longitudinal patient records, genetic markers, or real-time monitoring data--may further augment the diagnostic efficacy. This multi-modal integration may provide a comprehensive perspective on patient risk factors and illness progression.  
* Optimization of Computational Resources: Shifting from CPU-based experiments to GPU-accelerated frameworks may substantially improve the training efficiency of deep learning models. This enhancement would facilitate the examination of more intricate structures and more datasets, hence augmenting the model performance.
# A Digital Currency Architecture for Privacy and Owner-Custodianship
## 2 Background
### Architectural Considerations
#### 2.3.3 Privacy-Enhancing Cryptographic Techniques  
To achieve privacy by design, we consider applying privacy-enhancing technology of the sort used by privacy-enabling cryptocurrencies, such as Zcash and Monero. There are at least three possible approaches:  
1. _Stealth addresses, Pedersen commitments, and ring signatures._ Stealth addresses, which obscure public keys by deriving them separately from private keys [28], deliver privacy protection to the receiver of value [29]. Pedersen commitments, which obscure the amounts transacted to anyone other than the transacting parties [30; 31], remove transaction metadata from the ledger records [29]. Ring signatures, which allow signed messages to be attributable to \"a set of possible signers without revealing which member actually produced the signature\" [32], deliver privacy protection to the sender of value [29].
2. _Zero-knowledge proofs (ZKP)._ ZKP \"allow[s] one party to prove to another party that a statement is true without revealing any information apart from the fact that the statement is true\" [29] and can potentially be used to protect all of the transaction metadata [29]. Non-interactive approaches to ZKP, such as ZK-STARKs, deliver significant performance advantages over their interactive alternatives [33], and, based upon their measured performance [33; 34; 35], we anticipate that such operations should be fast enough to suffice for point-of-sale or e-commerce transactions, although stronger evidence to establish this fact would be useful.
3. _Blind signatures or blind ring signatures._ Because our proposed architecture is not peer-to-peer with respect to its users, we believe that it is also possible to combine a blind signature approach similar to the one suggested by [PERSON] [21]. Using this method would require either that recipients of (spent) tokens would immediately redeem them with the issuer or, equivalently, that the player would anonymously instruct the issuer to deposit funds into an account designated for the recipient. With blind ring signatures, the issuer could be a set of actors that work independently and either deposit or withdraw funds into a shared account whenever they, respectively, issue or redeem tokens, rather than a single actor [36].
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 3 Related Work  
Edge computing became more popular, particularly for applications with rapid response times and restricted bandwidth, since it places computing near data sources. Edge computing can be applied in different applications including smart cities, smart vehicles, smart traffic lights, and smart grids. These applications quickly integrate edge computing into their platforms, greatly increasing response time and saving network resources.