esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
Other studies highlight the use of this ensemble framework in combination with the power of neural networks to enhance the performance of standard detection techniques [28]. At its core, ensemble learning is about bringing together multiple estimators to better address a machine learning task. While it falls under the umbrella of artificial intelligence, it leans more toward statistical learning. Drawing inspiration from natural behaviors, the ensemble approach mirrors the human tendency to weigh multiple perspectives when making intricate decisions. Various surveys delve into the effectiveness of ensemble methods, pointing out their respective merits and challenges and identifying contexts where they are particularly beneficial. Ensemble methods generally bifurcate into averaging and boosting methods. There is a substantial body of work that discusses the application of ensemble algorithms in medical imaging [29; 30; 31]. These studies often face the issue of not being able to use deep learning tools because of their reduced numbers of data. In the realm of CAD (computational-aided diagnostic) systems tailored for mammograms, several investigations akin to ours employ a methodology that encompasses image preprocessing, feature extraction using CNNs, and the application of a stacking ensemble method [32]. However, a notable difference lies in the data sets employed. While many studies like the one by [32] utilize the publicly available MIAS data set, they often do not tap into genuine clinical data sets. This discrepancy was highlighted in numerous medical surveys critiquing such computational tools. There are also mentions of ensemble learning's commendable results in other works like [33], especially given its enhanced performance outcomes. Nonetheless, these studies often differ in the type of data they use as input for their models. Several well-established machine learning algorithms like logistic regression, random forest, \(k\)-neighbors, and gradient boosting are highlighted in certain health computer-aided tools [34]. While these algorithms are prevalent in the broader domain of machine learning, they often fall short for medical imaging tasks, given that they are not primarily tailored for such data types. This work considers the heavy remarks raised in medical journals about the prevalent issue of insufficient reporting on diagnostic accuracy in deep learning applications [8]. These shortcomings underscore the need for comprehensive documentation and the rigorous evaluation of the methodologies employed. In the next section, we delve into the results we achieved using deep learning models. Furthermore, we mention the steps and methods we employed to enhance their performance, specifically by integrating deep learning models with conventional ensemble learning ML algorithms. In essence, by combining traditional machine learning tools with advanced deep learning models, this research endeavors to elevate the diagnostic precision and robustness of medical imaging solutions, ensuring that they are both effective and reproducible.
## 3 Results
### Indices for Phytoplankton Bloom Characterization
#### 3.2.4. CyaBI with Satellite-Derived Cyanobacterial Biomass  
The inputs used for the CyaBI (satellite) included satellite-derived chl-a, turbidity, and N\({}_{2}\)-fixing cyanobacteria biomass. The most extensive blooms according to the CyaBI with satellite-derived cyanobacterial biomass (corresponding to the lowest indicator values) were in the Hara and Kolga says (CyaBI = 0), Parnu Bay (0.05), and the NW Gulf of Riga (0.05) (Figure 9). The highest indicator values were calculated for Soela Strait (0.99), the NE Gulf of Riga (0.98), and Muuga-Tallinna-Kakumae Bay (0.94). Like in the case of other indices, considerable variation in the results was observed between different years for the CyaBI (satellite), and extreme values were calculated for the bays in different years. For the whole period (2016-2022), the median CyaBI was highest in Hiitu Shallow (0.70), the Hara and Kolga boys (0.69), and Kihelkonna Bay (0.67). All three of the coastal areas are open to the larger Baltic Sea. The lowest median CyaBI values were calculated for the NW Gulf of Riga (0.39), Narva-Kunda Bay (0.47), and Soela Strait (0.47) (Figure 9). The satellite CyaBI showed the largest contrast to the rest of the indices. Although the two areas neighbor each other on the west coasts of Saaremaa and Hiiumaa, Soela Strait had one of the lowest index values, whereas Kihelkonna Bay had the highest. Unlike the CyaBI (in situ) and CSA, only one of the Gulf of Riga water bodies had a very low index value. In contrast to previous indices, the variation within the whole Gulf of Riga according to the CyaBI (satellite) was very high.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 2 The Numerical Method
### The Direct Simulation Monte Carlo Method  
In the DSMC method, the objects of the simulation are samples of the real molecules that constitute the gas. By utilizing the Monte Carlo technique, the DSMC method can solve the gas-kinetic Boltzmann equation [26] in a probabilistic manner. In each DSMC time step, the free motions of molecules and the collisions between molecules are decoupled. First, the free motions of all molecules are tracked during the time step. Then, all binary collisions that should happen in this time step are performed. Such an algorithm can reproduce the physical process underlying the Boltzmann equation, as long as the time step is a fraction of the mean collision time of gas molecules. The macroscopic flow field can be obtained by sampling the microscopic quantities in each grid cell. Similarly, the aerodynamic force/heating distributions on the solid wall boundary can be derived from the statistics of microscopic fluxes.  
The DSMC solver used in this investigation is based on the body-fitted grid and the corresponding particle tracking strategy, so that curved boundaries can be treated conveniently. The present DSMC solver can handle arbitrary geometries using multi-block structured or unstructured grids. The simulated gas can be a mixture of multiple species, and each species can be monatomic or polyatomic. The high-temperature gas effect is partially considered by simulating the vibrational excitation and the relaxation of internal energy modes. For collisions between gas molecules and the interaction between gas molecules and solid surfaces, there are different models to be selected.  
In this paper, the no-time-counter (NTC) scheme [20] is employed to perform the pairwise collisions in each grid cell during one time step, so that the collision rates between molecules can be correct. The variable hard sphere (VHS) model [20] is adopted for the calculation of collision cross-sections and scattering angles. For the energy exchanges between translational/rotational/vibrational modes in inelastic collisions, the Larsen-Borgnakke redistribution approach [27] is used, and the relaxation rate of vibrational energy is consistent with the Millikan-White expression [28]. The Maxwellian model [20] describes the reflection of a gas molecule on the solid surface, where the probability of diffuse reflection is \(\alpha\) (called the accommodation coefficient), and the probability of specular reflection is \(1-\alpha\).
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 3 Problem Space
### Cost-Effective Dimension  
The cost-effective dimension acknowledges the transition from design to practical product development within the business landscape. Within this dimension, we have identified three significant pivotal factors for users. (i) _Overall Cost_: Affordability is a paramount consideration for any system implementation. However, the cost is associated with the features and the system's reliability. Finding the optimal balance point between cost, reliability, and feature richness is required during the design phase. (ii) _Energy Efficiency:_ Beyond cost, minimizing energy consumption is a crucial concern. Leveraging contemporary advancements like solar energy offers a sustainable approach to curbing energy usage. Similarly, the integration of low-power technologies can substantially reduce energy consumption. Within our review, we linked various edge technologies related to energy efficiency, outlining their advantages and limitations for users' informed consideration. (iii) _Water Utilization:_ At the core of any irrigation system lies efficient water resource management to minimize waste. This encompasses precise water distribution aligned with varying crop requirements, optimizing usage efficiency, and reducing unnecessary water loss.  
Our survey bridges the gap between design and practical application. Recognizing the significance of cost, energy efficiency, and water utilization not only aids in developing economically viable solutions but also ensures that the resultant systems align with sustainable practices and environmental responsibility.
# On the Communication Performance of Airborne Distributed Coherent Radar
## 2 Materials and Methods
### System Model
#### 2.1.3 Statistical Model of \(\theta_{i}^{i}\) and \(\theta_{r}\)  
In order to improve the communication transmission efficiency, the transmitting and receiving antennas on UAV are usually directional. When the position of the UAV fluctuates due to factors such as wind, the mainlobe of the transmitting and receiving antennas will no longer be aligned. This will have a serious impact on the communication performance. Therefore, the position error of the UAV fundamentally affects the antenna gain. As mentioned above, we only consider the antenna pointing misalignment between the radar UAV and the communication UAV. In Figure 2, we show the angle error \(\theta_{r}\) when the radar UAV communicates with the communication UAV.  
For a clearer representation, we only draw the geometric relationship between the communication UAV and the radar UAV in Figure 2 when only the position error on the \(y\)-axis exists. This is because the position error on the \(x\)-axis does not affect the gain of the transmitting antenna. Based on current research [29; 30; 31], there is \(\Delta y\sim\mathcal{N}\left(0,\sigma_{\Delta y}^{2}\right)\) where \(\sigma_{\Delta y}^{2}\) is the variance of \(\Delta y\). Based on the geometric relationship shown in Figure 2, there are \(\theta_{r}=\arctan\left(\frac{\Delta y}{\Gamma_{1}}\right)\) and \(\Delta y\ll L_{1}\). We can obtain \(\theta_{r}\approx\frac{\Delta y}{L_{1}}\) and \(\theta_{r}\sim\mathcal{N}\left(0,\frac{\sigma_{\Delta y}^{2}}{\left(L_{1} \right)^{2}}\right)\). If the angle that the \(i\)-th radar UAV deviates from the communication UAV is \(\theta_{r}^{i}\), for simplicity,let \(\theta_{t}^{i}\sim\mathcal{N}\left(\overline{\theta_{t}^{i}},\left(\sigma_{int}^{i} \right)^{2}\right)\) and \(\theta_{r}\sim\mathcal{N}\left(\overline{\theta_{r}},\sigma_{mr}^{2}\right)\). The influence of UAV position fluctuation on antenna gain \(G_{t}^{i}\) and \(G_{r}\) will be analyzed in Section 2.2.
## 4 Overall Design and System Analysis
### System Overall Design  
The main idea is to use dive buoys, floating buoys, or unmanned boat platforms to establish an integrated underwater information tube, providing CPNT functions, combining acoustic communication with networking and positioning features, as shown in Figure 2.  
There mainly exist two application modes for \(U^{2}CPNT\) scenarios, cooperative mode and non-cooperative mode. For the cooperative mode, the underwater targets carry a beacon and work in coordination with the positioning system to achieve location by actively transmitting signals or responding to external signals. In non-cooperative mode, the underwater targets will be located mainly by passive acoustic detection. Due to the high  
Figure 2: \(U^{2}CPNT\) System with subsurface buoys as base station, floating buoys, manned/unmanned boats or ground station as control station, proving CPNT service for underwater moving targets.  
accuracy, real-time performance, and reliability of cooperative positioning, its application demands in civilian fields have become increasingly widespread. In this study, we will focus on the cooperative mode; a detailed description is as follows.  
In cooperative mode, the underwater target carries a beacon terminal, which is a cooperative target.  
* During the positioning process, as the underwater targets pass through the deployment area, the subsurface buoys will receive beacon signals and transmit the signal to the surface control center. If the signal is received by more than four buoys with the same time label, the surface control center will be able to calculate the target's position and transmit it to underwater vehicles;
* The positioning calculation can be performed on underwater targets if they possess the position information of the buoys;
* During the communication process, both upstream and downstream information flow through the link alongside with the positioning signal between the surface control center and underwater moving targets.  
Following the analysis of \(U^{2}CPNT\) scenarios, we propose the following definition, system composition, system function and other elements of \(U^{2}CPNT\) system.  
1. Definition \(U^{2}CPNT\) is an underwater network system that is able to provide an integrated CPNT service for underwater fixed or mobile nodes.
2. System Composition A \(U^{2}CPNT\) system consists of subsurface buoys (base station), moving targets (beacons), and a surface control center.
3. System Functions A \(U^{2}CPNT\) system possesses the following functions: * Providing information transmission service through underwater acoustic communication network; * Providing positioning and tracking service for underwater moving targets; * Providing position and time calibration service for underwater equipment.
4. System Support To ensure the reliability and performance of the \(U^{2}CPNT\) system, several supporting measures are needed: * Subsurface buoy position calibration during deployment and working phase; * Subsurface buoy status monitoring during whole system lifetime.
# An Improved Fuzzy Vector Signature with Reusability
## 3 Definitions
### Security Models
#### 3.2.3 Reusability  
Reusability means that an adversary cannot generate a valid signature without knowing the target input source of data, even if the adversary is given verification keys and signing parameters correlated with the target input data. In addition, the adversary can get valid signatures that are verified with the target verification key or other (correlated) verification keys via signing oracles. We say that an FVS scheme is reusable if the advantage that any PPT adversary \(\mathcal{A}\) wins against the challenger \(\mathcal{C}\) in the following game is negligible in \(\lambda\):  
* **Setup**: \(\mathcal{A}\) selects correlated random variables \((W_{1},\ldots,W_{q})\in\mathcal{W}^{q}\) and gives it to \(\mathcal{C}\). \(\mathcal{C}\) chooses \((w_{1},\ldots,w_{q})\in(W_{1},\ldots,W_{q})\) and runs \(\mathbf{Setup}(1^{\lambda},w_{j},n,d,\ell,t)\) for \(j=1,\ldots,q\). Then, \(\mathcal{C}\) gives \(\left\{\mathsf{VK}_{w_{j}},\mathsf{SP}_{j}\right\}_{j=1}^{q}\) to \(\mathcal{A}\).
* **Signing query**: \(\mathcal{A}\) issues a random variable \(W^{\prime}\) correlated with \((W_{1},\ldots,W_{q})\), a message \(m_{k}\), and an index \(j\in[1,q]\) of the signing parameter \(\mathsf{SP}_{j}\). \(\mathcal{C}\) chooses \(w^{\prime}\gets W^{\prime}\) correlated with \((w_{1},\ldots,w_{q})\), and runs \(\mathbf{Sign}\left(\mathsf{SP}_{j},w^{\prime},m_{k}\right)\). \(\mathcal{C}\) sends the resulting signature to \(\mathcal{A}\).
* **Output**: \(\mathcal{A}\) outputs \((m^{*},\sigma^{*})\) such that \(\sigma^{*}\) was not the output of \(m^{*}\) queried. If \(\mathbf{Verify}\left(\mathsf{VK}_{w_{j}},\sigma^{*},m^{*}\right)=1\) for some \(j\in\left\{1,\ldots,q\right\}\), \(\mathcal{A}\) wins the game.  
**Definition 3** (Reusability).: _An FVS scheme \(\Pi_{f}\) is reusable in chosen message attacks if, for any PPT adversary \(\mathcal{A}\) making at most \(q_{s}\) signature queries, there is a negligible function \(\
u(\lambda)\) such that_  
\[\mathsf{Adv}_{\Pi_{f},\mathcal{A}}^{\text{RELI}}(\lambda)\triangleq\Pr\big{[} \mathcal{A}\,\text{wins}\big{]}\leq\
u(\lambda).\]
## 3 Results and Discussion
### Evolution of Mechanical Properties
#### 3.3.1 Compressive Strength  
The evolution of the compressive strength of the inner shaft lining concrete on Day 1, Day 3, and Day 7 after pouring is depicted in Figure 8. As shown in the graph, the compressive strength of the shaft lining concrete increased significantly with an increase in age within the first 7 d after pouring, and the increase rate gradually decreased with an increase in age. At the same age, the compressive strength of the inner shaft lining increased with thickness, whereas the increase rate has no apparent association with the thickness. This is because the thick lining ensures higher internal moisture content and higher temperature at the same curing time, and the good curing environment increases the growth rate of concrete strength. When the thickness increased from 1.0 m to 1.2 m, the compressive strength increased by more than 1.8%; meanwhile, when the thickness increased from 1.2 m to 1.5 m, the compressive strength increased by more than 0.5%. The compressive strength of the shaft lining concrete with three thicknesses ranged between 37.33 and 57.01 MPa. Finally, the evolution equation of compressive strength with age within the first 7 days after pouring was fitted based on the experimental data (Table 4).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Compressive Strength (MPa)** & **Time (d)** & **Thickness (m)** & **Fitting Equation** & **R-Squared** \\ \hline
1 & y & x & 1.0 & \(y=-1.0417x^{2}+10.667x+27.705\) & 1 \\ \hline
2 & y & x & 1.2 & \(y=-1.1383x^{2}+12.218x+26.920\) & 1 \\ \hline
3 & y & x & 1.5 & \(y=-0.6108x^{2}+6.6083x+40.672\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evolution equation for compressive strength with age within the first 7 d.  
Figure 7: Evolution of the specific heat capacity of the shaft lining with age on Day 1, Day 3, and Day 7 after pouring.
# A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup
## 2 Background
### Prefix Caching and Prefix Expansion
Although prefix expansion resolves the prefix caching problem, it restricts the cache performance compared to the scheme which caches the original prefix without expansion. Even though multiple IP addresses match the same non-leaf prefix, each of their expanded prefixes should be cached. Thus, it results in more capacity misses in the same size of cache. In addition, the expanded prefix is more specific, so it exploits less address-spatial locality than the original prefix. For example, consider the IP addresses, \(IP_{1}\) and \(IP_{4}\). If the original LMP \(p\) was cached on \(IP_{1}\) lookup, it could be reused on \(IP_{4}\) lookup. However, the more specific prefix \(p_{1}\) is cached instead and that prefix cannot be used for \(IP_{4}\). Furthermore, the static prefix expansion has an additional disadvantage of increasing the size of the FIB besides the above shortcomings. Figure 1: Example of the problem when caching non-leaf prefix \(p\), supposing two consecutive packets arrive. While the destination of the first packet is \(IP_{1}\), that of the second packet is assumed to be one of \(IP_{2}\), \(IP_{3}\), and \(IP_{4}\).
## 3 Results
### Indices for Phytoplankton Bloom Characterization
#### 3.2.3. CyaBI with In Situ Cyanobacterial Biomass  
Similar to the CSA Index, the CyaBI indicator values of 1 correspond to negligible to no-bloom events, whereas a value of 0 indicates extensive blooms. The inputs for the CyaBI include satellite-derived chl-a and turbidity as well as in situ N\({}_{2}\)-cyanobacteria biomass. The most substantial blooms according to the CyaBI with in situ cyanobacterial biomass (corresponding to the lowest indicator values) were in the Hara and Kolga bays, Kihelkonna Bay, and Moonsund Sea (CyaBI = 0) (Figure 9). These results are similar to the CSA results, as is expected because the Hara and Kolga bays and Moonsund Sea had very limited in situ biomass data. Considerable variation in the results was observed between the different years, and extreme values were calculated for the bays in different years. The highest indicator values were calculated for Eru-Kasmu Bay, Matsalu Bay (CyaBI = 1), and the NW Gulf of Riga (0.97). For the whole period (2016-2022), the median CyaBI was highest in Kihelkonna Bay (0.76), Moonsund Sea (0.71), and the Hara and Kolga bays (0.65). The lowest median CyaBI values were calculated for Liivi Bay (NW) (0.39), Eru-Kasmu Bay (0.42), and Narva-Kunda Bay (0.43) (Figure 9). Narva-Kunda and Eru-Kasmu lie on the Northern coast, in the Gulf of Finland, and are heavily influenced by waves and river inputs. The three bays with the lowest values had a lot of available in situ cyanobacteria biomass data. The cyanobacteria biomass parameter gives an indication of the actual biomass in the water column and thus supplements the surface measurements of the RS data, giving more reliable results. Different results for these three bays between CSA and the CyaBI indicate a benefit of the addition of a  
Figure 9: PII, CSA, in situ CyaBI, and satellite-derived CyaBI indices in Estonian coastal areas. Boxes represent the IQR of index values, the middle line represents the median, the whiskers represent the minimum and maximum values, and black dots represent outliers.  
biomass parameter into the status assessments. Similar to the CSA, all three water bodies in the Gulf of Riga had relatively low values of the CyaBI (in situ).
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
## 4 Application Example Analysis
### Case 2: Power Performance Optimization Assessment  
A wind farm equipped with 33 1.5 MW wind turbines interconnected to the grid in 2016 is situated in a mountainous region of southwestern China. The average wind speed and power generation of multiple turbines within the site area gradually decline over time, primarily influenced by terrain and environmental variations. To address this concern, the manufacturer fitted a vortex generator to the blade roots of three underperforming turbines in October 2021, aiming to improve the wind energy conversion efficiency of the blades. According to the results of finite element simulation analysis, the vortex generator could theoretically increase the turbine power output by 4.8%.  
The improvement in the power performance of the three inefficient turbines before and after the modification was evaluated based on the KPCA-AdaBoost method trained with operational data from January 2020 to December 2020. The trends in power performance changes of these turbines from October 2021 to November 2021 are illustrated in Figure 8. The evaluation results are presented in Table 1, showing an enhancement in the performance of the three inefficient turbines, ranging from 4.60% to 5.38%. These improvements align closely with the anticipated theoretical improvements, with an uncertainty error in the range of \(\pm\)0.5%. The results demonstrate that the proposed method accurately quantifies  
Figure 7: Trends in power generation performance of turbine 24# before fault discovery.  
the power performance of wind turbines, with assessment uncertainty significantly lower than that of conventional power curve modeling approaches.