esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Communication
### State-of-the-Art Research on Network Communication
#### 4.1.1 Design Factors
The former is responsible for data collection, while the latter is responsible for computation and communication. In this architecture, a master node is used to compute the trust values of all sensor nodes within its range, which can identify malicious behaviors effectively, so as to avoid collaborative attacks. Besides security issues, there are many issues related to human health. For example, some sensor devices are deployed on or in human body, so radio radiation, sensor working temperature, cutaneous stimulation from electrodes and some other factors may injury the human body [16, 100]. Since human body is moving constantly, sensor nodes on or in the body have the feature of mobility. Along with body movements, the distance between sensor nodes will constantly change. This will cause serious topological partition problems in that some links between nodes are broken and built frequently. This kind of network is called Delay Tolerant Network (DTN), whose routing protocol is very difficult to design and realize [89].
## 1 Introduction
Moreover, to minimize performance degradation due to the blockage effect, a mesh structure is applied to the S-band elements to ensure better EM transparent characteristics due to the polarization-selective characteristics of the mesh structure. The extended via cavity wall, which is advantageous for stable manufacturing, is applied outside the nine X-band elements to minimize the mutual coupling and to reduce the antenna size [25]. The proposed antenna is fabricated, and the radiation characteristics such as reflection coefficient, bore-sight gain, and radiation patterns are measured in a full anechoic chamber. The results demonstrate that the proposed mesh structure of the E-shape antenna can minimize the blockage effect of the upper layer and the extended via cavity wall can improve the mutual coupling effect with a simple via hole manufacturing process.
## 3 Results and Discussion
### DSR Grading  
Table 7 represents the high PG temperatures (HH) for all asphalt binders, which are the minimum temperatures corresponding to (G*/sin\(\delta\)) \(\geq\)1 kPa, AASHTO T315-09 (2000), obtained from the DSR test. The results showed that the values of G*/sin \(\delta\) for 5%GPCM and the control binder were similar. G*/sin \(\delta\) values for the control binder and 5%GPCM were recorded at 1.9880 and 1.9472, respectively. For the 5%PCM and 7%GPCM, the G*/sin \(\delta\) values were reduced by 12.6% and 42.3% compared to the control binder, respectively. However, it was observed that the corresponding temperature for the 5%PCM was similar to that of the control binder (57.6 \({}^{\circ}\)C), as shown in Table 6. The reduction in the G*/sin \(\delta\) values can negatively affect the rutting resistance of the asphalt binder due to the decrease in the elastic response of the materials.  
Moreover, for the PG70-28, the G*/sin \(\delta\) value and the corresponding temperature were documented at 1.8770 and 69.9 \({}^{\circ}\)C, respectively. The addition of the 5% and 7%GPCM to the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Binder Name**} & \multicolumn{2}{c}{**Minimum Temperature (\({}^{\circ}\)C)**} & \multirow{2}{*}{**G*/sin\(\delta\)**} & **Continuous PG** \\  & **Corresponding to (G*/sin\(\delta\)) \(\geq\) 1 kPa** & & **Grade** \\ \hline Control & 58 & 1.9880 & PG 63.6-XX \\ \hline PG70-28 & 64 & 1.8770 & PG 69.9-XX \\ \hline
5\% PCM & 58 & 1.7363 & PG 62.3-XX \\ \hline
5\% GPCM & 52 & 1.9472 & PG 57.6-XX \\ \hline
7\% GPCM & 52 & 1.1452 & PG 53.9-XX \\ \hline
5\% GPCM-SBS & 58 & 1.8505 & PG 63.7-XX \\ \hline
7\% GPCM-SBS & 58 & 1.4316 & PG 61.5-XX \\ \hline \hline \end{tabular}
\end{table}
Table 7: Minimum temperature corresponding to (G*/sin\(\delta\)) \(\geq\)1 kPa and G*/sin\(\delta\).  
Figure 12: DSC diagram for control, 5%PCM, and 5%GPCM binders.  
PG70-28 binder resulted in a reduction in both the G*/sin values and their corresponding temperatures. The G*/sin \(\delta\) values for the 5%GPCM-SBS and 7%GPCM-SBS were recorded at 1.8505 and 1.4316, respectively, and the corresponding temperature was 63.7 \({}^{\circ}\)C and 61.5 \({}^{\circ}\)C, respectively.  
The addition of 5% and 7%GPCM reduced the service temperature compared to the control binder. For example, the service temperatures of 5%GPCM and 7%GPCM dropped to 57.6 \({}^{\circ}\)C and 53.9 \({}^{\circ}\)C, respectively, compared to that of the control binder (63.6 \({}^{\circ}\)C). Similarly, adding 5% and 7%GPCM to the PG70-28 binder reduced the service temperatures of the modified binder to 63.7 \({}^{\circ}\)C and 61.5 \({}^{\circ}\)C, respectively, compared to the PG70-28 binder (69.9 \({}^{\circ}\)C).  
Generally, it can be concluded that, as the GPCM content increased in the binder, the G*/sin \(\delta\) values and the service temperature decreased. Thus, the rutting resistance decreased.
## 5 Results and Discussion
### Classifier Model
The experimental results show that when the threshold is set very low, such as 0.0005, although the detection rate of adversarial examples is high, the false positive rate of the detector is high as well, with about 70% of the original samples being identified as adversarial examples. As the threshold gradually increases, the false positive rate decreases, and the detection rates for the FGSM and PGD adversarial examples also decrease accordingly. However, at the threshold of 0.0006, the reconstruction error based on \(|x|_{\infty}\) can effectively be used to detect gradient-based adversarial examples. At this point, the false positive rate for original sam \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Norm**} & **Original** & **FGSM** & **BIM** & **PGD** & \multirow{2}{*}{**CW**} & **Deepfool** \\  & **Sample** & **(\(\epsilon\) = 0.15)** & **(\(\epsilon\) = 0.15)** & **(\(\epsilon\) = 0.15)** & & \\ \hline \(L_{0}\) & 0.9999 & 1.0 & 1.0 & 1.0 & 1.0 & 1.0 \\ \(L_{1}\) & 0.0387 & 0.0264 & 0.0327 & 0.0311 & 0.0387 & 0.0380 \\ \(L_{2}\) & 0.0021 & 0.0012 & 0.0016 & 0.0015 & 0.0022 & 0.0021 \\ \(L_{\infty}\) & 0.0530 & 0.0370 & 0.0449 & 0.0430 & 0.0530 & 0.0520 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reconstruction errors based on different norms. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**0.0005**} & **0.0006** & **0.0007** & **0.0008** & **0.0009** & **0.001** & **0.002** & **0.005** & **0.01** \\ \hline Original & 0.7064 & 0.3845 & 0.2918 & 0.1773 & 0.1082 & 0.0727 & 0.0009 & 0.0 & 0.0 \\ FGSM & 0.9036 & 0.8036 & 0.6609 & 0.5573 & 0.4373 & 0.3445 & 0.0109 & 0.0 & 0.0 \\ PGD & 0.8500 & 0.7300 & 0.5836 & 0.4409 & 0.3155 & 0.2191 & 0.0018 & 0.0 & 0.0 \\ CW & 0.6873 & 0.4709 & 0.2873 & 0.1727 & 0.1055 & 0.0699 & 0.0018 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection rates based on \(\|x\|_{\infty}\) under various reconstruction error thresholds. ples is around 38%, but the detection rates for the FGSM and PGD adversarial examples can reach up to 80% and 73%, respectively, making this threshold setting optimal. We also tested C&W adversarial examples and found that their detection is consistent with the original samples, demonstrating the subtlety of adversarial examples generated by optimization methods. When the threshold continues to increase, the detector becomes largely ineffective. Hence, appropriate norm measurements and threshold selection are crucial elements in detecting adversarial examples.
# The Production of Curli Amyloid Fibers Is Deeply Integrated into the Biology of _Escherichia coli_
## 2 Results and Discussion
### Multiple Regulatory Networks Control Curli Gene Expression
Very few genes were found to overlap, especially between swimming motility and curli production. Half the genes that affect both swimming motility and curli encode for either adenosine triphosphate (ATP) synthase or LPS biosynthesis genes (Table S7). A second study looked for biofilm defective mutants in the Keio collection using crystal violet and 96-well plates [128]. The biofilm mutants were subsequently tested for flagella, type I pili, and curli production [129]. For curli production, growth was for three days on CFA amended with CR but not Coomassieie brilliant blue. Comparison of flagella, curli, and type I pili genes also identified little overlap between flagella and curli associated genes (Table S7). However, fewer curli genes were identified, perhaps due to the use of LB (Luria-Bertani) media in the initial biofilm screen [129]. LB media has relatively high salt concentration, which inhibits curli production [19]. Additionally, the CR phenotypes presented here are different for several strains listed in the LB screen; for example, several inner core LPS mutants are listed as WT for CR binding in their study [129]. Consequently, we tested our strains using similar conditions and found that several of the phenotypic differences were due to media, growth, or staining differences (Compare CR plates in Figure S2, Table S8). Figure 6: Venn diagrams demonstrating few genes associated with curli affect swarming or swimming. Comparison of genes found to affect curli with swimming and swarming associated genes. Sections of Venn diagrams: \({}^{\text{a}}\) (orange), curli associated genes that do not affect swarming or swimming motility; \({}^{\text{b}}\) (dark green), curli associated genes that affect swarming motility; \({}^{\text{c}}\) (dark blue), curli associated genes that affect swimming motility; \({}^{\text{d}}\) (light blue), swimming defective genes that do not affect curli; and \({}^{\text{e}}\) (light green), swarming defective genes that do not affect curli. See Table S7 for complete list of genes.
Evaluation of Chloride Resistance of Early-Strength Concrete Using Blended Binder and Polycarboxylate-Based Chemical Admixture  
[PERSON]  
1 The mixing proportions of concrete were examined with regard to the durability performance and early strength in coastal areas. Research was conducted to improve the C24 mix (characteristic strength of 24 MPa). C35 concrete (characteristic strength of 35 MPa) was selected as a comparison group, as it exhibits the minimum proposed strength criterion for concrete in the marine environment. To secure the early strength of the C24 concrete, 50% of the total ordinary Portland cement (OPC) binder was replaced with early Portland cement (EPC); and to provide durability, 20% was substituted with ground granulated blast-furnace slag (GGBS). In addition, a polycarboxylate (PC)-based superplasticizer was used to reduce the unit water content. The compressive strength, chloride ion diffusion coefficient, chloride penetration depth, and pore structure were evaluated. After one day, the compressive strength improved by 40% when using EPC and GGBS, and an average increase of 20% was observed over 91 days. EPC and GGBS also reduced the overall porosity, which may increase the watertightness of concrete. The salt resistance performance was improved because the rapid early development of strength increased the watertightness of the surface and immobilization of chloride ions, decreasing the chloride diffusion coefficient by 50%.
## 2 Computational Methods
### Flow Field Solution Method  
The Reynolds-averaged Navier-Stokes equations were employed for the flow field solution [13]. Spatial discretization primarily uses the first-order upwind difference scheme. The convection terms were treated with a high-resolution (HR) scheme, while the temporal discretization adopted the lower-upper symmetric Gauss-Seidel (LU-SGS) implicit time integration method.  
\[\frac{\partial W}{\partial t}+\frac{\partial f}{\partial x}+\frac{\partial g}{ \partial y}+\frac{\partial q}{\partial z}=\frac{\partial R}{\partial x}+\frac {\partial S}{\partial y}+\frac{\partial T}{\partial z} \tag{1}\]  
In the Equation (1), \(W\) is the vector of conservative variables; \(f\), \(g\), \(q\) is the vector of convective fluxes; and \(R\), \(S\), \(T\) are the vectors of viscous fluxes.  
During numerical simulations of axial-flow compressors, turbulence models such as the k-\(\epsilon\) model and the k-\(\omega\) model are commonly used. To ensure computational accuracy, the k-\(\omega\) shear stress transport (SST) model and the k-\(\epsilon\) renormalization group (RNG) model were specifically used to simulate the performance of the compressor rotor blades. The simulation results, as presented in Figure 2, indicate that the SST model provided closer agreement with experimental data. Consequently, the SST model was selected for subsequent numerical simulations of the axial-flow compressor.
## 3 Methodology
### Data Preprocessing  
GEE (Google Earth Engine) serves as an online platform for geographic information processing that integrates a large amount of remotely sensed and geospatial data to provide users with high performance data processing and analysis capabilities [46,47]. The GEE platform was utilized to handle various conditional factors such as standardizing spatial resolution, identifying dynamic factors, and overlaying factor data. A feature matrix was generated using the landslide inventory data. According to [PERSON] [48] and [PERSON] [49], the data imbalance issue suggests that employing a one-to-one ratio of landslide-to-non-landslide samples is not entirely justifiable, given that the actual landslide area represents only a minor fraction of the total regional area. Recognizing this problem, the feature matrix of this study was constructed employing a 1:2 ratio containing 8023 landslide and 16,046 non-landslide samples. To process the impact factors, the vector data were rasterized into 1 km \(\times\) 1 km resolution cells. To ensure consistent spatial resolution throughout the dataset, the raster data were all sampled as 1 km \(\times\) 1 km resolution cells. In addition, to ensure consistency and comparability in the temporal analysis, it is important to consider that precipitation data are typically available at monthly scales. NDVI data, derived from satellite imagery, are processed at a seasonal scale. Land cover data is updated annually, thus necessitating the use of a consistent annual temporal resolution across all factors. While finer temporal resolutions could offer better insights into short-term landslide triggers, using an annual resolution allows for the analysis of broader, long-term  
Figure 3: Dynamic landslide susceptibility evaluation process.  
trends in landslide susceptibility and land system dynamics. Studies by [PERSON] [30] and [PERSON] [50] have used similar temporal resolutions to effectively model large-scale landslide susceptibility, emphasizing that annual data are generally sufficient for understanding long-term susceptibility patterns.  
Subsequently, landslide and non-landslide data are integrated with the impact factors to create a dataset. To ensure the scientific rigor and reliability of the model, the dataset is randomly partitioned into a training set and testing set, with a 4:1 ratio. This approach helps maintain the representativeness and independence of the data for model validation.
# Opportunities for Industry 4.0 to Support Remanufacturing
## Literature Review
Even though progress towards the uptake of remanufacturing is still hampered by a few challenges, Industry 4.0 reveals some powerful emerging trends that have the potential to address these bottlenecks. While Industry 3.0 focused on the automation of a single machine and process, Industry 4.0 pushes towards end-to-end digitalization of all physical assets and the entire supply chain [7]. The rise of Industry 4.0 is leading to the drastic and rapid growth of data volume (Big Data), driven by:  
* The availability of computing power and connectivity,
* The advancement of analytic capability (e.g., artificial intelligence),
* The introduction of new patterns of human and machine interaction (e.g., augmented reality systems), and
* The advent of technologies that ease the transformation of digital data into physical objects (e.g., additive manufacturing and rapid prototyping). When it comes to digitalization, Industry 4.0 possesses four main characteristics:  
* The vertical networking of small production systems, such as smart factories and smart products;
* The horizontal integration of global value creation networks, such as new business and cooperation models;
* Through-engineering across the product design, product use, and EOL stages;
* Exponential acceleration of technologies. The fundamental idea of Industry 4.0 is to increase availability and integrated use of relevant data by connecting all products, resources, and companies involved in the value chain and, ultimately, to generate additional value from available data and to maximize customer benefit [8]. According to a recent global industry survey [7], Industry 4.0 is no longer considered a \"future trend\" and for many companies it is already the core of their strategy and research agenda. Around 40% of companies surveyed reported that their vertical and horizontal value chains and through-engineering model are already benefiting from an advanced level of digitization and integration; this number is expected to increase to 75% within 5 years. Meanwhile, survey results suggested that global industrial product companies would invest USD907 billion per year through to 2020 for digital transformation. It is believed that the digitization of horizontal and vertical value chains, as well as the product life cycle, will eventually revolutionize the product, manufacturing/remanufacturing industry, and corresponding business models.
# Data Driven Approach for Eye Disease Classification with Machine Learning
## 4 Results
### Hierarchical Prediction Accuracy  
In Section 3.1, the data modeling was described, in which the diagnosis was also stored in a hierarchical structure and it was suggested that the classification could be performed at each level. For example, the diagnosis for Keratoconjunctivitis, generated by the system, is as follows:  
Cornea \(\rightarrow\) Keratitis \(\rightarrow\) Neurotrophic keratoconjunctivitis  
Here \"Cornea\" represents a first-level diagnosis, \"Cornea \(\rightarrow\) Keratitis\" represent second-level diagnoses, whereas complete diagnosis will be considered \"Cornea \(\rightarrow\) Keratitis \(\rightarrow\) Neurotrophic keratoconjunctivitis\". To relate data frequency and to verify system reliability by prediction level, accuracies were calculated. This was important because in case of a rare disease, the system should be able to give a reliable diagnosis.  
Figure 8: True positive rate (TPR) and false positive rate (FPR) of classification algorithms for 52 classes (diseases) shown in Table 6. TPR is drawn as horizontal bars, whereas FPR is drawn on a circular scale. Although the naïve Bayes algorithm exhibits the smallest FPR, its TPR is also low for a number of classes. However, the decision tree and random forest algorithms exhibit better overall performance, with maximum horizontal bars approaching the maximum value of 1.  
For example, if machine-learning algorithms are unable to give a complete diagnosis (e.g., _Cornea \(\rightarrow\) Keratitis \(\rightarrow\) Neurotrophic keratoconjunctivitis_), a second-level diagnosis, namely, _Cornea \(\rightarrow\) Keratitis_, should be given or at least a first-level diagnosis, that is, _Cornea_. The advantage of this scheme is that if the system is unable to give a complete diagnosis, then it should at least specify a direction or the part of the eye in which the issue may exist, that is, partial but accurate diagnosis.  
Performance measures for all classifiers are shown in Table 7. The random forest algorithm outperformed all others, followed by the decision tree and the neural network algorithms. However, accuracy dropped for second-level and complete diagnosis. This is obviously owing to the relatively low frequency of the data available for complete diagnosis. Overall, this demonstrates that the proposed framework is quite effective with data of sufficient frequency.
## 2 System Model
### Model Constraints  
#### 2.2.1 Energy Storage System Constraints  
Consider the following constraints for the energy storage system:  
\[P_{\text{bess}}^{\text{min}}\leq P_{\text{bess}}\left(k\right)\leq P_{\text{bess }}^{\text{max}} \tag{1}\]  
\[E_{\text{bess}}^{\text{min}}\leq E_{\text{bess}}\left(k\right)\leq E_{\text{bess }}^{\text{max}} \tag{2}\]  
That is, it is necessary to satisfy the maximum charging and discharging power constraints of the energy storage system, and the battery capacity of the energy storage system must  
\begin{table}
\begin{tabular}{l l c} \hline \hline
**Symbol** & **Explanation** & \\ \hline \(\beta_{cur,i}(k)\) & Power of the shed load for the \(i\)-th controllable load & \\ \hline \(\eta_{grid}(k)\) & Electricity price for buying (\(c_{grid}^{bur}(k)\)) or selling (\(c_{grid}^{sel}(k)\)) & \\ \hline \(C_{2}\) & Optimization objective to minimize dependence on the main grid & \\ \hline \(\lambda_{grid}\) & Power purchased from the main grid & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Decision Variables** & **Descriptions** & **Type** \\ \hline \(\delta_{DG}(k)\) & on/off status of the diesel generator at & binary \\  & time \(k\) & \\ \(P_{DG}(k)\) & output power of the diesel generator at & continuous \\ \(\delta_{\text{bess}}\left(k\right)\) & status of the BESS at time \(k\) & binary \\ \(P_{\text{bess}}\left(k\right)\) & charging/discharging power of the BESS & continuous \\ \(T_{sch}^{on,i}\) & start time of the \(i\)th controllable load & integer \\ \(T_{sch}^{off,i}\) & shutdown time of the \(i\)th & integer \\  & controllable load & integer \\ \(P_{sch,i}(k)\) & power consumption of the \(i\)th & continuous \\  & controllable load at time \(k\) (kW) & continuous \\ \(\beta_{cur}(k)\) & proportion of flexible load curtailment at & continuous \\  & time \(k\) (\%) & continuous \\ \hline \hline \end{tabular}
\end{table}
Table 2: Decision variables for energy management problem.  
not exceed the maximum and minimum capacity values. The capacity of the energy storage system is calculated as follows:  
\[\begin{split} E_{\text{bess}}\left(k+1\right)&=E_{ \text{bess}}\left(k\right)+\eta_{\text{bess}}\;P_{\text{bess}}\left(k\right) \Delta t-\varepsilon_{\text{bess}}\;\Delta t\\ \eta_{\text{bess}}&=\left\{\begin{array}{cc}\eta_{ \text{bess}}^{c}\;,&P_{\text{bess}}\left(k\right)>0\\ \eta_{\text{bess}}^{d}\;,&P_{\text{bess}}\left(k\right)\leq 0\end{array}\right.\end{split} \tag{3}\]  
where \(\eta_{\text{bess}}\) represents the charge/discharge efficiency, and \(\varepsilon_{\text{bess}}\) represents the self-discharge rate of the battery.
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 3 Verification and Analysis  
The simulation data and the real measurement data were respectively used to verify the strong adaptive filtering algorithm proposed in this paper. In Section 3.1, the simulation was used for the verification. In Section 3.2, four kinds of algorithms were used on a real UAV testing platform. By comparing the performance of each algorithm, the superiority of SAKF was verified.  
Performance Analysis of High-Speed Unmanned Aerial Vehicle (UAV) Integrated Navigation Algorithms Based on Simulation
## 4 Results and Discussion  
In order to understand the LULC changes and dynamics of urban expansion in the BMR, this study divides the results into four sections: (i) LULC classification and accuracy assessment from the satellites images; (ii) Analysis of LULC change in the study periods; (iii) LULC prediction and validation; and (iv) LULC model application.