esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Blockchain and Consignment Contracts to Optimize Food Supply Chains Under Uncertainty
## 6 Numerical Illustration
### Analysis of Profit  
The estimated profit of both firms and the supply chain on TS with and without blockchain and CP with and without blockchain are shown in Figure 3. It is observed that the total profit of the entire supply chain increases when blockchain technology was applied by the firm. Moreover, the profit is higher for the consignment contract rather than the traditional system. For the traditional policy, the optimal value of the order quantities are observed as 99.04 and 99.09 for WBC and BC, respectively, which turns out to be a profit of \(3.58\times 10^{9}\) and \(4.39\times 10^{9}\). Furthermore, the consignment contract shows a better profitability with \(3.98\times 10^{9}\) and \(4.57\times 10^{9}\) for WBC and BC, respectively. Moreover, in all cases, the manufacturer receives higher profit than the retailer. The fixed fee in this case is calculated as \(-4.05\times 10^{8}\). This shows a negative fixed fee, which implies that the retailer has to pay the fixed fee or royalty to the manufacturer, as the manufacturer confirms higher profit in CP as compared to TS.
## 2 Materials and Methods
### Research Methodology and Model Construction
#### 2.3.5 Model Accuracy Evaluation  
This paper uses a confusion matrix to evaluate the simulation accuracy of the SCV-RF and MAML-RF models. The confusion matrix is a commonly used tool for evaluating the performance of classification models. It quantifies simulation performance by constructing four metrics: true positive (_TP_), false positive (_FP_), true negative (_TN_), and false negative (_FN_) [36]. In this paper, the selected metrics for evaluating the model's simulation accuracy are accuracy (A), precision (P), and recall (R). For multi-class classification problems, the values of P and R are calculated using the weighted method. This method involves computing P and R for each class. Then it takes a weighted average based on the sample size of each class to obtain the overall model. The specific calculation process is detailed in Formulas (3)-(5).  
\[Accuracy=\frac{\sum\limits_{i=1}^{I}TP(i)}{N}, \tag{3}\]  
\[Precision=\frac{\sum\limits_{i=1}^{I}TP(i)}{\sum\limits_{i=1}^{I}TP(i)+\sum \limits_{i=1}^{I}FP(i)}, \tag{4}\]  
\[Recall=\frac{\sum\limits_{i=1}^{I}TP(i)}{\sum\limits_{i=1}^{I}TP(i)+\sum \limits_{i=1}^{I}FN(i)}, \tag{5}\]  
In these formulas, \(i\) represents the \(i\)-th class, taking values from 1 to \(I\), where \(I\) is 4. \(N\) is the total number of samples in the confusion matrix. _TP_ indicates that the actual class is positive and the simulation is also positive. _TN_ refers to cases where the actual class is negative and the simulation is negative. _FP_ occurs when the actual class is negative but the simulation is positive. _FN_ occurs when the actual class is positive but the simulation is negative.
# Rates of High-Temperature Evaporation of Promising Fire-Extinguishing Liquid Droplets
## 3 Experimental Setup and Methods
### Heating on the Substrate  
For the realization of conductive heat transfer (Figure 1a), the heating plate PL-01 is used (the operating temperature range of the heating surface is 40-400 \({}^{\circ}\)C, and the accuracy of temperature setting is \(\pm 1\)\({}^{\circ}\)C). A polished copper substrate (with thickness of 4 mm and diameter of 50 mm) is placed in the center of the plate, heated to a predetermined temperature (depending on the experiment, the temperature varies from 70 to 100 \({}^{\circ}\)C). After the temperature on the substrate surface reaches the set value, a drop of liquid with a volume of \(V_{\text{d}}\approx 10\)\(\mu\)L is placed on it with the help of a dispenser. Evaporation of the droplet lying on the substrate is observed using a high-speed video camera with shooting frequency of up to 6\(\cdot 10^{5}\) frames per second and the maximum shooting resolution of \(1280\times 1280\) pixels. To determine the substrate temperature, an infrared thermometer is used (Testo 835-H1, with a 4-point laser designator and temperature measurement accuracy of \(\pm\) 0.5% of the measured value). Typical video frames obtained in the experiments are shown in Figure 2.  
It should be noted that the gas space above the substrate was open, i.e., the drop on the substrate was not covered with any cap. Therefore, we can conclude on the conditions of natural air convection over the substrate surface in the vicinity of the droplet. Using a low-inertia thermocouple, additional measurements were performed. They have shown that the air temperature near the droplet surface is 4-8 \({}^{\circ}\)C above the initial temperature in the experiment. At that time, the substrate temperature ranged from 50 to 100 \({}^{\circ}\)C. Therefore, we have taken into account the droplet heating from the substrate.  
The performed video recording has revealed significant differences in the velocities and other characteristics of the droplet speed among the studied compositions on the substrate. The studies are carried out on an experimental setup similar to References [41; 42] using the shadow method. A drop with a given volume is placed on a polished copper substrate using a high-precision single-channel electronic dispenser Finnpipette Novus. Then, using the shadow method, droplets are photographed. The contact angle (\(\theta\)), diameter (\(d\)), height (\(h\)), and drop area (\(S\)) are determined from the shadow image using the drop profile processing methods (Table 2).  
Figure 2: Typical frames with images of droplets of the studied compositions on a heated substrate.  
The relative error of the methods for determining these parameters when processing photographic images does not exceed 5% [42]. Typical shadow images of droplets of the studied extinguishing agents are presented in Figure 3.
## 2 The Method for Self-Healing Efficiency Estimation
### Measured Data Processing  
2.1 Transformation of Full Wavefield Images from Space-Space-Time Domain to the Wavenumber-Wavenumber-Time Domain  
A procedure analogical to one described in Section 2.1.1 is realized for all full wavefield data obtained during the self-healing monitoring period.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 5 Performance Evaluation
LTE can then allocate more resources to the unlicensed band. It can significantly improve the throughput. When the Wi-Fi is lightly loaded, the difference in throughput between LTE and Wi-Fi will be significant, resulting in relatively poor fairness. As the number of Wi-Fi stations increases, Wi-Fi will ask for more resources in the unlicensed bands. The Wi-Fi throughput and the fairness index of both schemes will also improve. Our proposed scheme pays more attention to the issue of fairness. Hence, the fairness index is higher than that of NBS. Figure 13(b) shows the change in the fairness index when the LTE is heavily loaded. There are 50 LTE users, and the number of Wi-Fi stations increased from 5 to 25. When the Wi-Fi load is light, and the LTE load is heavy, LTE will get more resources and result in a lower fairness index to maximize the overall throughput. Figure 13(b) shows that if the number of Wi-Fi stations is 10, the fairness index is dropped slightly in our proposed scheme compared to when the number of Wi-Fi stations is 5. The reason is that the Wi-Fi load is still not too heavy to adjust the time ratio \(\delta\) to give Wi-Fi more resources. Therefore, LTE obtains more resources to maximize the overall throughput and causes a lower fairness value in this situation. As the Wi-Fi load increases, Wi-Fi stations can have more sharing of the resources according to both schemes' adjustments. The fairness index is improved. The improvement of our proposed schemes is better than that of NBS, as shown in the figure. Figure 13: [PERSON]’s Fairness Index under a different number of stations with 20 LTE users.
# Unsupervised Feature Selection for Outlier Detection on Streaming Data to Enhance Network Security
## 5 Evaluation
### Data Source  
Rather than relying on a security-domain specific single dataset such as KDD'99, NSL-KDD or a predestined younger one CSE-CIC-IDS2018 ([[https://registry.opendata.aws/cse-cic-ids2018/](https://registry.opendata.aws/cse-cic-ids2018/)]([https://registry.opendata.aws/cse-cic-ids2018/](https://registry.opendata.aws/cse-cic-ids2018/)), accessed on 5 March 2021), we have deliberately chosen real-world candidate datasets from the ODDS ([[http://odds.cs.stonybrook.edu/about-odds/](http://odds.cs.stonybrook.edu/about-odds/)]([http://odds.cs.stonybrook.edu/about-odds/](http://odds.cs.stonybrook.edu/about-odds/)), accessed on 5 March 2021) (Outlier Detection DataSets) Library [65] which are commonly used to evaluate OD methods for various reasons. In recent years, the majority of state-of-the-art IDS datasets have been criticized by many researchers since their data is out of date or do not represent today's threat landscape [51; 66; 67]. Even if CSE-CIC-IDS2018 overcomes some shortcomings, it was not optimal for the extensive number of measurements performed (Figure 5) due to its enormous number of data instances in multiple files. Furthermore, we wanted to stress test UFSSOD with very dynamic datasets, meaning that outliers might occur in various features, which are typically not the same. As could be shown with its predecessor, the CICIDS2017, only a subset of three to four features per attack is enough to describe it best [51], making it look quite static. With an ensemble of the 15 datasets from ODDS shown together with their characteristics in Table 3, we have focused on a variety of datasets in terms of length, dimension, and outlier percentage from multidisciplinary domains. Except for _lympho_ and _vertebral_, which have been neglected due to their insignificant number of data instances or dimensions, those datasets are also used by PyOD for benchmarking. Furthermore, to reduce the processing runtime of each OD algorithm we truncated _mnist_, _musk_, _optdigits_, _pendigits_, _satellite_, _satimage_-2, and _s shuttle_ while mostly maintaining its outlier percentage.
# Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
###### Abstract  
Acoustic emission (AE) source localization has been used to visualize progress failures generated in a wide variety of materials. In the conventional approaches, AE source localization algorithms assume that the AE signal is propagated as a straight line. However, it is supposed that progress failures form heterogeneity of elastic wave velocity distributions. Hence, with the conventional source localization, it is expected that the localization accuracy is reduced in heterogeneous materials since diffraction and refraction waves are generated. Thus, if the straight propagation waves are classified, conventional source localizations are performed in the heterogeneous materials. The self-organizing map (SOM) is one of the unsupervised learning methods, and the SOM has potential to classify straight propagation waves for the source localizations. However, the application of classified AE signals in source localization is not popular. If classified AE signals are applied to the time difference of arrival (TDOA) method, which is the popular localization method, it is expected that number of visualized sources are decreased because the algorithm does not consider the selection of the propagated wave. Although ray tracing has potential to localize a larger number of sources than the TDOA method, it is expected that the localized sources are less accurate in comparison with results of the TDOA method. In this study, classified waves were applied to two of the source localizations, and model tests based on pencil-lead breaks (PLBs) generating artificial AE sources were conducted to validate the performance of the source localizations with classified waves. The results of the validation confirmed that the maximum error in the TDOA method is larger in comparison with ray tracing conducted with 20 mm intervals of source candidates. Moreover, ray tracing localizes the same number of sources as the number of PLB tests. Therefore, ray tracing is expected to more practically localize AE sources than the TDOA method if classified waves are applied.  
AE source localization; heterogeneous materials; self-organizing map; unsupervised learning methods +
Footnote †: journal: Applied Physics Letters  
Acticle  
Application of Classified Elastic Waves for AE Source Localization Based on Self-Organizing Map
# A New Method of Ski Tracks Extraction Based on Laser Intensity Information
## 3 Methodology
### Ski Tracks Extraction
#### 3.4.1 K-Neighborhood Acquisition of k-d Tree  
The topological relationship between the scattered point cloud datasets of Wanlong ski resort must be established before point clouds can be grown and segmented. With the establishment of a topological relationship for the point cloud, the processing range of data can be effectively reduced, and the algorithm will perform more efficiently. Currently, octree and k-d trees are commonly used point cloud data organizing algorithms. In spite of the fact that the time complexity of searching K adjacent points by both of them is \(O(n)\), the octree method does not provide a satisfactory location for a single point. Therefore, this paper uses k-d trees to establish the topological relationship of point clouds, and the FLANN library is used to implement k-d tree search in PCL. K-dimensional trees are used to organize the set of points in k-dimensional space. It is similar to a binary search tree but with additional constraints. The three-dimensional point cloud is analyzed using a three-dimensional k-d tree. The k-d tree places the value \(A\) of the specified dimension on the root, and then \(B,D\), and \(E\) with smaller values are found on the left subtree, and the larger values \(C,F\), and \(G\) are found on the right subtree. Once this process is repeated for both the left and right subtrees, only one element will remain in the last tree. The specific workflow is shown in Figure 5.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 4 Problems and Prospects  
### Potential Problems  
Although the concept of microalgae-assisted aquaculture has emerged into the limelight owing to aforementioned advantages, further developments are required before the industrial implementation is feasible. This novel concept itself has a couple of potential problems, which may hinder its wide application.  
Firstly, the safety level of biomass recycled from wastewater as aquaculture feed has not been assessed comprehensively. As the biomass production in wastewater is coupled with bacteria growth, harvested biomass consists of both microalgal biomass and bacterial biomass. To our knowledge, some bacterial species may contain toxic components or release bio-toxin, thus, the survival of aquatic animals will be seriously threatened if the biomass contaminated by bacterial toxins is used as aquaculture feed. Hence, strict control of toxic bacteria in wastewater treatment is pivotal to the successful application of microalgae-based aquaculture.  
Secondly, flotation and fungi-assisted harvesting have a couple of problems related with economic feasibility and biomass safety. For example, flotation may bring solid wastes in aquaculture wastewater to the surface of the aqueous phase with microalgae biomass together, thus reducing the safety level of harvested staff in aquaculture practice. In addition, the cost of fungi-pellets production in an artificial medium is not low enough to support the commercial application of fungi-assisted harvesting. Hence, without addressing the problems associated with economic feasibility and biomass safety, harvesting technologies are not mature enough to support the industrialization of microalgae-assisted aquaculture.  
Thirdly, the lack of knowledge on economic assessment and life cycle analysis is a barrier to the industrialization of microalgae-based aquaculture. Based on previous studies on technologies, the use of microalgae in aquaculture industry, including feed production, water quality control, and nutrients recovery could promote nutrients recycle, mitigate greenhouse gas emission, and reduce aquaculture cost [92]. However, the economic performance of microalgae-based aquaculture has never been fully studied. Besides, the life cycle analysis has not been conducted yet to evaluate the effects of microalgae-based aquaculture on natural environment. Therefore, aiming at controlling potential threats of microalgae-based aquaculture, it is necessary to comprehensively evaluate its economic performance and environmental impacts.
## 2 The Multi-GNSS, Dual Frequency, Single-Baseline RTK Functional Model  
The functional model we will use assumes two receivers \(r\) = 1,2 that track \(s\) = 1,..., \(m\) GPS, Galileo, QZSS and BDS satellites on \(f\) frequencies. For the zero- and short-baseline models, the relative ionospheric and tropospheric delays and satellite orbit errors can be assumed negligible. We make use of broadcast ephemerides for satellite orbits and clocks and the instantaneous (single-epoch), linearized DD system of observation equations read,  
\[y=Aa+Bb, \tag{1}\]  
where \(y\) is the vector of DD carrier-phase and code observations, \(A\) is the design matrix of the DD integer ambiguities in vector \(a\) and \(B\) corresponds to the design matrix of the real-valued baseline components \(b\). The LAMBDA method [21] will be used for full integer ambiguity resolution (IAR) to obtain the precise fixed baseline solution. Provided that the success rate, i.e., the probability of correct integer estimation, is sufficiently high, the baseline will have mm-cm level precision owing to the precise carrier-phase observations. We make use of system-specific reference satellites when performing the between-satellite single-differences. However, it is emphasized that if we would take a common reference satellite on the overlapping frequencies between the systems, it could further strengthen the model provided that the inter-system biases (ISBs) can be calibrated [22; 23]. For the stochastic model, an elevation weighting sine-function was used, as employed in RTKlib v2.4.3 [24].
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 3 Autonomous Vehicle Perception Sensors
### LiDAR  
LiDAR stands for Light Detection and Ranging. LiDAR works on the principle of emitting a laser light or Infrared (IR) beam and receiving the reflection of the light beams to measure the surrounding environment. The LiDAR used for autonomous vehicles are mostly in the 900 nanometer (nm) wavelength, but some have a longer wavelength which works better in fog or rain. LiDAR outputs point cloud data (PCD) which contain the position (x, y, z coordinates) and intensity information of the objects. Intensity value shows the object's reflectivity (the amount of light reflected by the object). The position of the objects is detected using the speed of light and time of flight (ToF). There are various classes of LiDAR. The two major ones are Mechanical LiDAR and Solid State LiDAR (SSL). In Mechanical/Spinning LiDAR, the laser is emitted and received at the environment using rotating lenses driven by an electric motor to capture a desired FoV around the ego vehicle. Solid State  
Figure 4: Radar sensor processing pipeline.  
LiDARs (SSL) do not have any spinning lenses to direct the laser light; instead, they steer the laser lights electronically. These LiDARs are more robust, reliable, and less expensive than the mechanical counterparts, but their downside is their smaller and limited FoV compared the mechanical ones. It is worth noting that the laser beams generated in LiDARs are class 1 laser or the lowest intensity out of four categories of laser classes; therefore, their operation is considered harmless for the human eyes. Typical mechanical LiDAR hardware consists of various components as shown in Figure 5.  
The laser light is generated and emitted using diode emitters; it is passed through rotating lenses to focus its energy at various angles. The light is then reflected off the objects and received through another set of rotating lenses. Reflected light information is received through photoreceivers. The timer/counter then calculates the ToF between the emitted and received laser beams to measure the object position state information. The resulting point cloud information is then sent to an on-board vehicle processor for further processing. LiDAR adds angular resolution and higher accuracy to the camera and radar sensors.  
Similar to cameras, LiDAR captures 2D (X-Y direction) spatial information; however, unlike cameras it captures ranges to create a 3D spatial information of environment around the ego vehicle. By measuring range, LiDAR adds angular resolution (horizontal azimuth and vertical) for better measurement accuracy compared to the camera. Also, having a higher frequency and shorter wavelengths enables LiDAR to be more accurate than radar sensor measurements. Typical LiDAR has a range of about 40 to 100 m, resolution accuracy between 1.5-10 cm, vertical angular resolution between 0.35 to 2 degrees, horizontal angular resolution of 0.2 degrees and an operating frequency of 10-20 Hz.
# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 6 Discussion
### Data Resolution  
* The mean wind speed and capacity factor are not affected by the data resolution.
* The systematic influence on the mean wind power density is caused by its cubic equation. By using the standard deviation of the wind speed, it can be corrected to an uncertainty of about 0.1%.
* By filtering the data thoroughly, effects on the power curve can be reduced to an acceptable level. The standard deviation of the resulting energy yields can be estimated at 0.4%.
* Whenever downtime events are derived from operational data, the number and length of downtime events heavily depend on the chosen data resolution. Availability metrics are affected accordingly. Thus, discrete event information should be used whenever possible. The production-based availability is furthermore affected through the power curve.