esa-sceva/satcom-chunk-collection huggingface dataset:
# Method to Characterize Potential UAS Encounters Using Open Source Data
## 2 Materials and Methods
### Processing and Workflow  
The workflow was organized into pre-processing, geometry calculations, and results aggregation, with each component encoded by a dedicated MATLAB script. First, pre-processing consisted of the following:  
1. Download open source data for use cases
2. Filter data based on administrative boundaries
3. For FAA DOF, create small circle vectors centered on reports points with a radius of the horizontal position uncertainty
4. Interpolate data to have a fixed spacing of 500 feet between points
5. Aggregate all vectors into a single array of latitude and longitude points
6. Recheck and confirm that all points are within administrative boundary  
Next, distance calculations were completed, organized by administrative boundary. A maximum distance of 60 nautical miles was specified to reduce the quantity of discrete distance computations. As the scope focused on close encounters, points greater than 60 nautical miles apart should not be considered \"close\" for aviation operations. The geometry calculates consisted of the following:  
7. Calculate unique pairs of features (e.g., railways and roads)
8. Create a small circle with a 60 nautical mile radius centered on each point for one of the features
9. Identify which points of the other feature are within each small circle
10. Calculate the distance using the WGS 84 reference ellipsoid between the center of the small circle and all point-in-polygons
11. Determine the closest point by calculating the minimum for all computed distances  
After the geometry calculations, the results were aggregated across locations and pairs of features. General statistics such as the mean, median, and other percentiles were calculated.
## 5 Results and Discussion
### Performance Comparison under Different Workloads
#### 5.6.2 Average Throughput  
Figure 18 shows the average throughput for varying numbers of normal and malicious nodes, as outlined below.  
1. A total of 50 normal nodes and 1 malicious node. * Healthy network: It can be observed from Figure 18 that, in the 50-node case, the throughput decreases slightly compared to the 20-node case. This is because of  
Figure 18: Average throughput for different node numbers.  
the increased overhead of route discoveries. Moreover, a higher number of nodes can produce more traffic and cause channel contention, leading to collisions and retransmissions.
* Malicious network: The figure shows that throughput decreases because the black hole-attack node still adversely affects the network. The malicious node will drop packets, but its impact might be slightly diluted in a larger network.
* The NTRUEncrypt-secured network: The throughput decreases due to cryptographic overhead. However, this value is not far away from the healthy network. Cryptographic processing reduces the throughput, even though NTRUEncrypt cryptography ensures fewer packets are lost to the black hole-attack node compared to the malicious network.
2. A total of 50 normal nodes and 5 malicious nodes. * Healthy network: Figure 18 shows that throughput decreases in the 50-node scenario compared to the 20-node scenario. This is due to the increased overhead of route discoveries; the increasing node numbers cause high communication congestion and collisions. * Malicious network: The throughput decreases drastically compared to the 20-node malicious network case because the five black hole-attacking nodes will drop more packets, reducing the throughput. * The NTRUEncrypt-secured network: The throughput decreases due to the processing overhead of the NTRUEncrypt cryptography.
3. A total of 50 normal nodes and 10 malicious nodes. * Healthy network: Figure 18 shows that throughput decreases with the increasing number of normal and malicious nodes. This is because more nodes generate more traffic, leading to channel contention, packet collision, and retransmission. * Malicious network: The throughput decreases severely because the increase in the number of black hole-attack nodes causes more packet drops, degrading the throughput. * The NTRUEncrypt-secured network: The throughput decreases due to the computational overhead and complexities in the dense network.
## 2 Literature Review
### Multi-Objective FSP under Uncertainty Conditions  
Few works have examined uncertainties simultaneously with multi-objective decisions in comparison with a single objective. Table 2 presents the main characteristics of the studies performed in this area. Makespan, tardiness, and flowtime are the most analyzed measures, and earliness is not widely studied. In addition, stochastic setup times are not considered. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Reference** & **Objective Function(s)** & **Problem Characteristics** & **Solution Approach** & **Remarks** \\ \hline [PERSON] et al. [54] & [PERSON], & Normally distributed processing times, & Genetic algorithm. & Considered that jobs have different expected completion times or different expected due dates. & \\ [PERSON] et al. [55] & [PERSON], & Processing times with a normal probability distribution. & Improved genetic algorithm with a new generation scheme, which can preserve the good characteristics of the parents in the new generations. Robustness is achieved by maximizing Prob(Cmax \(<\) expected completion time) & No recommendations given. \\ \hline [PERSON] et al. [56] & [PERSON], & Two-machine IPSF: Processing times modeled through scenario sets. & 2-approximation algorithm. & The 2-approximation algorithm may be improved to find results better than the 2 worst-case/ ratio for the unbounded min-max version. \\ \hline [PERSON] and [PERSON] [57] & [PERSON], & Machine breakdowns generated by a generic probability distribution. & The multimodal optimization approach generates different schedules, and the most robust solution is selected depending on the negative effects of machine breakdowns. & High flexibility given to the decision-maker for dealing with additional concerns. The inclusion of preference aggregation methods with social choice theory is recommended. \\ \hline [PERSON] and [PERSON] [58] & [PERSON], & Dynamic arrivals, Processing times under a uniform distribution. & Reactive-reactive approach. Predictive robust
## 2 Materials and Methods
### Parametric Images from Radiofrequency Signals
#### 2.2.1 Entropy Image  
Entropy is considered a measure of the average uncertainty of a signal in information theory and is defined as a negative summation of the probability of a given signal multiplied by its base 2 logarithm [20]. Shannon entropy is used widely as a similarity measure in signal processing applications. [PERSON] first applied Shannon entropy to analyze ultrasound signals [21]. The Shannon entropy of a discrete random variable \(X\) with possible values \(\{x_{1},\ x_{2},\ \cdots,\ x_{n}\}\) is defined as follows:  
\[H=-\sum_{i=1}^{n}p(x_{i})\log_{2}[p(x_{i})], \tag{1}\]  
where \(p(\cdot)\) represents the probability distribution function.  
In this study, an entropy image was calculated using an envelope image by moving a small window through the entire image area. All pixel values in the square window were used to establish the probability density function, and the entropy was calculated at the center of the window using Equation (1). This procedure was repeated in both the axial and lateral directions, with a fixed overlap ratio. The size of the window was set to three times the pulse length to obtain a stable statistical parameter of entropy [22], and the overlap ratio of the window was set to one-tenth of the axial length to obtain fine spatial resolution. The entropy image represents the characteristics of the textual patterns inside  
Figure 2: Overall scheme of signal processing techniques using RF signals.  
a small region, which is closely related to the local microstructure of the scanning area. Figure 3a,b show the sample B-mode and entropy images generated from the RF signals.
## 3 Model Description
### Model Implementation and Validation  
In terms of prediction performance, considering two metrics widely adopted in regression problems: (i) Root Mean Squared Error (RMSE); and (ii) Mean Absolute Error (MAE). Two evaluation measures were selected to indicate the performance of the prediction models.  
The Mean Absolute Error (MAE) is Equation (12)  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|x_{i}-y_{i}\right| \tag{12}\]  
The Root Mean Squared Error (RMSE) is Equation (13)  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(x_{i}-y_{i}\right)^{2}} \tag{13}\]  
In the above formula, \(y_{i}\) is the predicted value, \(x_{i}\) is the actual value, and \(N\) is the number of sample points. In general, \(MAE\) is the average of absolute errors, and it can reflect the actual situation of the predicted value error. The \(RMSE\) is the expected value of the difference between the parameter estimate and the parameter actual value, it can evaluate the degree of the data change, and the smaller value of the \(RMSE\), the better accuracy of the prediction model.
# Anti-Biofilm Agents to Overcome _Pseudomonas aeruginosa Antibiotic Resistance_
## 3 Anti-Biofilm Agents to Tackle _P. aeruginosa_
### Analogs of Native Autoinducers
#### 3.1.3. Lectin Inhibitors
Their orientation in the LecB binding site appears comparable to that of D-mannose and enables the formation of H-bonds with Asp96 and Gly24 while the aromatic ring interacts with the side chains of Val69 and Asp96. On top of that, other interactions such as lipophilic contacts between the \(c\)-glycosidic methyl group and Thr45 and Ala23 are a key feature for further enhancement of the affinity towards LecB. Finally, **ABA-38** and **ABA-39** revealed no toxicity as well as good metabolic stability and oral bioavailability in vivo. These glycommetics could thus be used in further assays as monotherapy and in combination with antibiotics against biofilm-associated infections. These two compounds were used as hits for the development of the 2,5-dimethyliophene derivative **ABA-40**[87]. This ABA shows slightly increased potency to quench LecB thanks to additional lipophilic interactions with the side chains of Val69, Asp96 and Ser97. Remarkably good ADMET properties of this compound combined with potent anti-biofilm activity (>80% biofilm mass reduction at 100 uM) made it the final lead in the aryl-sulfonamide glycolimetic series. The inhibition of lectins has proved to efficiently disturb the establishment of biofilms. Multivalent lectin inhibitors like **FD2** (**ABA-35**) are effective at reducing biofilm formation and inducing its dispersal. However, those molecules are hard to synthesize, have high molecular weights and various disadvantages including undesirable bacterial aggregation promotion and harmful immune system interferences. Nevertheless, monovalent glycommetics displaying better ADMET properties showed potent inhibitory activity towards LecB. Those interesting results could be a first step towards the development of new drug-like lectin inhibitors.
# Intermitt FOCV Using an I-V Curve Tracer for Minimizing Energy Loss
###### Abstract  
Conventional fractional open-circuit voltage (FOCV) methods in maximum power point tracking (MPPT) are widely adopted for their simple structure and low computing power requirements. However, under mismatch and environmental changing conditions, the FOCV methods introduce a large amount of energy loss due to their maximum power point being fixed at the initial setup. To reduce energy loss, the intermittent FOCV MPPT proposed in this paper regularly refreshes all the parameters for each condition in time by using an I-V curve tracer. The proposed intermittent FOCV consists of two phases: the scan and set phases. In scan phase, the I-V curve of a photovoltaic (PV) cell is scanned and its power is calculated. In set phase, the global MPP of the PV cell is extracted and set by controlling the 8-bit capacitance array. Simulation and calculation based on experimental results with a single PV cell show that the energy loss of the proposed intermittent FOCV under daily temperature and illuminance distributions decreased by up to 99.9% compared to that of the conventional FOCV.  
maximum power point tracking (MPPT); fractional open-circuit voltage (FOCV); photovoltaic (PV); I-V curve; energy harvesting Article
## 4 Discussion  
### Significance Analysis of Multi-Features  
Hyperspectral remote sensing images have rich spectral information, which is the main basis of image classification. In contrast, SAR images can record geometric and physical properties, such as surface roughness and dielectric constant through backscattering intensity. The PolSAR image features that can be used for classification include polarization features and texture features. The unique spatial information of these features can effectively supplement the hyperspectral image information, which has proven to be beneficial to the fine classification of ground objects.  
The ranking of the importance of all the GF-3 and OHS features by the Recursive Feature Elimination (RFE) [65] method is shown in Figure 11(a), which is used to represent the separate contributions to the final synergetic classification. There are seven most important features, namely, entropy (T), contrast, anisotropy, correlation, homogeneity, energy, and NDWI. However, we found that the final overall accuracy and Kappa coefficient derived from all 18 features are higher than those from the seven most important features, as shown in Figure 11(b).  
Figure 13 shows box-and-whisker diagrams for the seven most important features, which provide a preliminary insight into the causes of misclassification. It can be seen that these seven features can better distinguish the wetland landcover types. The features, such as entropy, anisotropy, homogeneity, and NDWI, contribute greatly to distinguishing different landcover due to the spectrum and appearances of wetland (Figure 12(a),c,e,g). However, contrast and correlation features do not perform well due to similar wetland texture features (Figure 12(b),d). The low separability of some wetland types, e.g., saltwater vs. river, farmland vs. tidal flat, and shrub vs. grass, gives rise to misclassification to a great extent.  
Figure 12: (**a**) Ranking of the importance of the features used for synergetic classification. Entropy (T) and entropy (P) represent the entropy of the GF-3 texture feature and polarization feature, respectively. (**b**) The accuracy metrics of feature sets.  
Figure 13: Box-and-whisker diagrams of the seven most important features. Green boxes represent 25 th to 75 th percentiles, and whiskers extend to minimum and maximum values (excluding outliers). Red square and green line within the green box indicate mean and median, respectively. (**a**) entropy, (**b**) contrast, (**c**) anisotropy, (**d**) correlation, (**e**) homogeneity, (**f**) energy, (**g**) NDWI.
# Analysis and Modelling of Shrinkage and Creep of Reactive Powder Concrete
## 4 Results and Discussion  
### Compressive Strength and Elastic Modulus of RPC  
The compressive strength and elastic modulus were determined for RPC samples after 28 days of aging. As shown in Figure 4, the compressive strength and elastic modulus of the RPC samples increase with increasing steel fibre content. The compressive strength increases from 120 MPa to 142 MPa, and the elastic modulus increases from 44.7 GPa to 48.0 GPa as the steel fibre content increases from 0% to 2%. The compressive strengths of the RPC samples with 1% and 2% steel fibre content are 11.9% and 18.3% higher, respectively, than that of RPC samples without steel fibre. The elastic modulus of the RPC samples with 1% and 2% steel fibre content are 4.4% and 7.3% greater, respectively, than that of RPC samples without steel fibre. The strength of RPC is far greater than that of normal strength concrete, which is usually in the range of 20-80 MPa. RPC has a dense structure, which is attributed to the low W/B, hydration of the cement, and the pozzolanic effect of the SF [38]. As a result, the RPC has an ultra-high strength. The steel fibre plays the role of a bridge and bowel in the RPC samples. Increasing steel fibre content can make more fibres to sustain the load, which decreases  
Figure 3: Creep tests of RPC.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & \(t_{0}\) **(day)** & \(\Delta t\) **(day)** & \(f_{\xi}\) **(MPa)** & \(\eta\) & **ST\%** \\ \hline I & & 330 & 120 & 0.2 & 0 \\ II & & 329 & 120 & 0.3 & 0 \\ III & 28 & 329 & 120 & 0.4 & 0 \\ IV & & 329 & 134 & 0.3 & 1 \\ V & & 329 & 142 & 0.3 & 2 \\ \hline \hline \end{tabular} Note: \(t_{0}\) is the RPC age at loading, \(\Delta t\) is the load duration,\(f_{\xi}\) is the compressive strength at loading, and \(\eta\) is the axial stress ratio, defined as the ratio of the loading stress to the compressive strength.  
\end{table}
Table 3: RPC creep experiment parameters.  
the stress between fibres and matrix and restricts the development of microcracks and transverse deformation. Thus, the compressive strength and the elastic modulus increase with increasing steel fibre content [39; 40].
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 4 Training Points
We attribute the decrease in accuracy seen in a number of the surrogate models employing LHS sampling to the low dimensional parameter space, thus we do not exploit the main strength of Latin hypercube sampling--better accuracy in high dimensional spaces. Additionally, recent work has shown that this is not true when model hyperparameters are unknown and must be estimated from training data observed at the chosen design sites [41]. In Table 5, we tabulate the \(rRMSE\) of each of the surrogate models for several choices of the number of tensored Gauss-Legendre points used to train the surrogate models. We note that the Legendre polynomials employing discrete projections do not do well at approximating the physical model for a low number of training points. This likely has to do partly with fewer points being employed for the quadrature rule. The sparsity controlled regression-based Legendre surrogate model does not have this same problem, but the error decreases more slowly than some of the other surrogate models as more training points are employed. The MARS surrogate model does not improve greatly when training points are added. The radial basis function, Gaussian processes, and neural networks surrogate models behave similarly and, as expected, with the \(rRMSE\) error bound decreasing as more training points are employed. In Section 5, we employ the Gauss-Legendre points for surrogate model construction, since this choice provides a good comparison of all the surrogate models. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Points** & **Legendre** & **Leg. Reg.** & **MARS** & **RBF** & **GP** & **NN** \\ \hline GL & \(2.13\times 10^{-1}\) & \(3.05\times 10^{-1}\) & \(4.18\times 10^{-1}\) & \(2.08\times 10^{-1}\) & \(1.98\times 10^{-1}\) & \(2.62\times 10^{-1}\) \\ Random & \(9.39\times 10^{0}\) & \(2.67\times 10^{-1}\) & \(3.57\times 10^{-1}\) & \(1.12\times 10^{-1}\) & \(7.79\times 10^{-2}\) & \(2.02\times 10^{-1}\) \\ CC & \(2.26\times 10^{-1}\) & \(2.90\times 10^{-1}\) & \(4.31\times 10^{-1}\) & \(2.21\times 10^{-1}\) & \(2.39\times 10^{-1}\) & \(2.94\times 10^{-1}\) \\ LHS & \(9.44\times 10^{0}\) & \(3.53\times 10^{-1}\) & \(3.60\times 10^{-1}\) & \(1.67\times 10^{-1}\) & \(1.51\times 10^{-1}\) & \(2.26\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of surrogate models relative error using randomly generated training points where Legendre refers to the model constructed via Equation (21) and Legendre Reg. refers to the model constructed via Equation (26).
## 3 Results and Discussion
### Lipid Peroxidation  
The lipid peroxidation results of this research are presented in Table 1. The results revealed that the application of SC-CO\({}_{2}\) did not change (\(p>0.05\)) the peroxidation values of the meat samples throughout all seven days of storage. The TBARS values were all below 0.29 mg/kg. The lack of changes in lipid peroxidation could be due to the removal of the visible fat from the chicken breast meat samples, thus limiting the oxidation of the lipid process. A similar situation was seen in a study by [PERSON] et al. [4] on chicken samples that were cooked up to 60 \({}^{\circ}\)C, kept at 4 \({}^{\circ}\)C for zero, two, and four days and showed no significant changes in TBARS values, which all were below 0.2 mg/kg. However, [PERSON] et al. [24] reported that the application of SC-CO\({}_{2}\) at 13.8 MPa under 35 \({}^{\circ}\)C for 2 h on ground pork meat increased its TBARS value after five days of storage at 4 \({}^{\circ}\)C. The lipids are commonly dispersed as triacyl glycerides, phospholipids, and sterols in both the intra- and extra-cellular spaces of meat. Lipids are, however, chemically unstable and, consequently, easily oxidizable, particularly when being stored and handled post-mortem. The rancid smell, off-flavor growth, droplet losses, discoloration, nutrient value loss, and shelf-life decline are causes of lipid oxidation that negatively affect the meat. Several factors, including iron content, distribution of unsaturated fatty acids, pH, and antioxidant levels, influence the rates and degree of lipid oxidation [25].
## 4 Machine Learning with Big Data Analytics to Achieve Fuel Efficiency
### Vessel Monitoring System and Data Transmission  
Data collected from the ship, or the environment are recorded and stored in the data acquisition system where monitoring systems are usually installed onboard the ship for continuous monitoring of the operational conditions. The network middleware is used for communication and management of data where these data are transmitted to the shore for further analysis. There are several challenges in the transmission of data to shore as vessels are often operating at regions out of coverage of the shore station thereby having to deal with unstable connections. Sometimes, synchronizing big files between shore and the vessels becomes a significant issue due to the possibility of duplicate data and time lapse between machines. Some technologies can assist in the replication/synchronization of data  
Figure 7: (a) Ship resistance/resistance coefficients vs. tugboat speed obtained from numerical simulation; (b) ship resistance model in FINE/Marine®.  
between the vessel and the land by establishing a private cloud between the firm and its vessels. The most commonly used method to transmit data is through electromagnetic wave transmission technology which is also known as very high frequency (VHF) radio transmission. Satellites are commonly used among ships as satellite communications (such as INMARSAT and COSPAS-SARSAT) are reliable for offshore operations and emergency communications. However, they tend to be costly; therefore, 4G is used as an alternative for the transmission of information that is not time sensitive.