esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Histopathological Classification Factors for Deep Learning-Based Breast Cancer Prediction
## 1 Introduction
### Problem Statement  
Different magnification factors, such as low, medium, and high magnification power, are a crucial part of the histopathology study. Low-power microscopes offer a wide field of view and can be used to examine significant areas of tissue quickly. However, they provide little detail and cannot be used to identify individual cells or structures. Medium-power microscopes offer more detail than low-power microscopes but cannot cover as large an area as high-power microscopes. They are ideal for identifying small structures within tissue samples. High-power microscopes offer the most significant level of detail but can only examine minimal areas at a time. High-power microscopes are ideal for identifying individual cells or structures within tissue samples. Pathologists benefit from different magnification factors to see different cell representations and spot abnormalities. The information gleaned from the various magnification levels is typically used by pathologists to develop a general understanding of the health status of the histopathological image under study. Similar to a pathologist, this study examines how well the learning algorithm apprehends information from the various magnification levels of histopathological images. Using the most appropriate magnification factor that achieves the highest learning rate is crucial to improving the model's accuracy. Inappropriate magnification factors may be considered noisy, which degrades the model's accuracy. Hence, the questions this study endeavors to address are: Do machine learning algorithms also benefit from the existence of different magnification factors? Which magnification factor represents high-quality data, and which one represents noisy data? The above questions are answered utilizing the BreakHis [21] dataset, which contains four magnification factors, i.e., 40x, 100x, 200x and 400x.  
The remainder of this work is organized as follows: Section 2 reviews related works in the literature and contrasts their conclusions with the findings of this work. The background information required to understand the context of this work is explained in Section 3. Section 4 describes the methodology employed in this research paper in detail. Detailed result analysis and an explanation of this work's findings are provided in Section 5. Section 6 reweighs the conclusions of this work to those presented in the literature review. Finally, closing remarks are given in Section 7.
# Clutter Cancellation and Long Time Integration for GNSS-Based Passive Bistatic Radar
## 4 Simulation Results and Discussion  
### Simulation Results  
In this section, the proposed method is tested by using MATLAB software to simulate the radar system and Table 1 provides the parameter settings of the radar system. The target echo pulses with different motion status are provided in order to verify the effectiveness of the proposed method.  
In the simulation, GPS L1 signal is adopted as the transmitter of opportunity. The reason of this choice is that it represents the worst case in terms of signal power. The position of the satellite in the radar coordinate system is (5.59,7.19,18.2) \(\times\)\(10^{6}\) m and its velocity is (60,100, \(-\)20) m/s. Two multipath signals are considered in the simulation. Table 2 illustrates the related parameters of the signals. When considering that Doppler frequency is related to the bistatic angle which is varying during the CPI, the coordinate form of Doppler frequency calculation is given here, as follows:  
\[f_{d}=\frac{1}{\lambda}\bigg{(}\frac{P_{TX}-P_{t}}{\parallel P_{TX}-P_{t} \parallel}+\frac{P_{RX}-P_{t}}{\parallel P_{RX}-P_{t}\parallel}\bigg{)} \Bigg{[}\begin{array}{l}v_{tx}\\ v_{ty}\\ v_{tz}\end{array}\Bigg{]} \tag{31}\]  
where \(\parallel\cdot\parallel\) denotes the modular operation and \(\big{[}v_{tx},v_{ty},v_{tz}\big{]}^{T}\) is the target instantaneous velocity vector relative to the receiver.
## 3 Analyzing Challenges
### Energy Consumption  
Some learning algorithms, including deep learning, utilize iterative learning processes [23]. This approach results in high energy consumption. Nowadays, the deep learning method is used to design HLI-based agents because of its high accuracy and its similarity to the human brain in the decision-making process. Deep learning models require a high computational power of GPUs. In [11], it was shown that these models are costly to train and develop from financial and energy consumption perspectives. During the operation of an HLI-based agent, the agent may use a predefined plan for learning multiple models concurrently to support self-awareness. Therefore, high computational power is required to support more abilities of cognition. This means that enough energy  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Challenges** & **No.** & **Challenges** \\ \hline
1 & Problem Identification and Formulation & 2 & Energy Consumption \\
3 & Data Issues & 4 & Robustness and Reliability \\
5 & Cheating and Deception & 6 & Security \\
7 & Privacy & 8 & Fairness \\
9 & Explainable AI & 10 & Responsibility \\
11 & Controllability & 12 & Predictability \\
13 & Continual Learning & 14 & Storage (limited memory) \\
15 & Semantic and Communication & 16 & Morality and Ethics \\
17 & Rationality & 18 & Mind \\
19 & Accountability & 20 & Transparency \\
21 & Reproducibility & 22 & Evolution \\
23 & Beneficial & 24 & Exploration and Exploitation \\
25 & Verifiability & 26 & Safety \\
27 & Complexity & 28 & Trustworthy \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of challenges.  
should be supplied for executing the HLI-based agent. In order to mitigate this problem, four solutions were given in the literature, as given below:  
* Investing in new paradigms with low energy consumption for HLI, such as quantum computing [24].
* Finding modern mathematical frameworks to find learning models with lower calculations, which leads to lower energy consumption [25].
* Sharing models to prevent energy consumption. A researcher can share a model with other researchers around the world.
* In an AI-based system, if there is no way to decrease the load of computations of learning processes, energy harvesting techniques can be used to return the wasted energy [26; 27].
## 2 Materials and Methods
### Physical Parameters
#### 2.2.4 Coefficient of Static Friction  
The coefficient of static friction is defined as the ratio of the maximum frictional force and the normal force exerted between the material and its contact surface before sliding. This parameter reflects the frictional characteristics of the contact surfaces between materials. Typically, the sliding friction angle is employed to characterize the frictional behavior when granular materials undergo relative sliding on a contact surface, with its tangent value corresponding to the coefficient of static friction [19]. The coefficient of static friction plays a critical role in the design of agricultural machinery, particularly seeding equipment, as it directly influences seed flow properties and the smoothness of seed dispensing.  
The coefficient of static friction was measured using the inclined plane method. When the angle of the inclined plane was smaller than the sliding friction angle, the Sesbania seeds remained relatively stationary on the inclined plane. When the inclination angle exceeded the sliding friction angle, the seeds began to slide down. The test setup consisted of the material plate for testing, a digital inclinometer (accuracy: 0.01\({}^{\circ}\)), and a self-made inclined plane lifting platform, as illustrated in Figure 5. The material plate was securely fastened to the inclined plane, and the handle adjusted the inclination angle.  
During the experiment, tweezers were used to position the Sesbania seeds along the longitudinal axis of the test material plate. As the slope angle increased progressively, seeds began to slide on the test material plate once the slope angle exceeded the sliding friction angle. The slope was fixed at this point, and the inclination angle was measured using an inclinometer to determine the sliding friction angle. Test values showing a tendency to roll were excluded, and only results corresponding to pure sliding motion were considered  
Figure 4: Material plate of Sesbania seeds.  
Figure 5: Device used to measure static friction coefficient: (1) material plate; (2) inclined plane; (3) digital inclinometer; (4) lifting device; and (5) handle.  
valid. Ten groups of valid data were collected for each type of test material plate (PMMA, SAE 304 stainless steel, PLA, and 6061 aluminum alloy) and the material plate of Sesbania seeds. The coefficient of static friction was calculated using Formula (5).  
\[f_{\mathrm{s}}=\mathrm{tan}\varphi \tag{5}\]  
where \(f_{\mathrm{s}}\) is the coefficient of static friction; \(\varphi\) is the sliding friction angle (in \({}^{\circ}\)).  
The coefficients of static friction between the Sesbania seeds and PMMA, SAE 304 stainless steel, PLA, and 6061 aluminum alloy, as well as between the seeds themselves, were determined to be 0.601, 0.466, 0.498, 0.448, and 0.677, respectively.
# Research on Generalized Hybrid Probability Convolutional Neural Network
## 5 Experiment and Results
### Result Analysis  
In the comparison algorithm, the MLP classifier performed intensive training on the mapped subclass features, and the accuracy was higher than the original accuracy. However, because the MLP classifier could easily fall into a local optimum during the training process, the overall performance was not good, and the CNN-PNN, CNN-SVM, CNN-ELM and GHP-CNN classifiers had better results. The GHP-CNN avoided some loss of separability information caused by the hypersphere normalization in the traditional PNN and retained the optimal classification surface under the Bayesian criterion. The classification ability of the GHP-CNN was obviously better than the other algorithms. In addition, the accuracy of the classification of unknown categories did not reach the classification accuracy of subclasses, because the generalization ability of the mapping of unknown subclass features was not as good as the generalization ability of the mapping of subcategory features. The separability of mapping features was not good enough, and the features of different categories were mutually integrated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(s=90\) & \(t=3\) & \(t=4\) & \(t=5\) & \(t=6\) & \(t=8\) & \(t=10\) \\ \hline VGG-16-E & 90.00 & 85.75 & 77.20 & 76.00 & 75.88 & 74.30 \\ \hline CNN-PNN & 88.33 & 86.00 & 78.00 & 74.33 & 74.25 & 73.10 \\ \hline CNN-SVM & 91.33 & 85.50 & 78.20 & 75.70 & 74.63 & 71.00 \\ \hline CNN-ELM & 92.67 & 88.30 & 79.00 & 76.30 & 75.37 & 73.10 \\ \hline GHP-CNN & 95.00 & 89.25 & 80.60 & 77.33 & 76.33 & 75.50 \\ \hline \end{tabular}
\end{table}
Table 4: Classification accuracy corresponding to different algorithms after the unknown categories with the CIFAR-100 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(s=246\) & \(t=3\) & \(t=4\) & \(t=5\) & \(t=6\) & \(t=8\) & \(t=10\) \\ \hline ResNet-152-E & 90.15 & 86.51 & 84.83 & 81.32 & 79.47 & 76.14 \\ \hline CNN-PNN & 90.62 & 86.47 & 84.43 & 80.15 & 77.81 & 75.40 \\ \hline CNN-SVM & 89.40 & 87.74 & 85.42 & 81.36 & 78.62 & 77.48 \\ \hline CNN-ELM & 84.42 & 81.30 & 75.31 & 72.45 & 71.17 & 70.09 \\ \hline GHP-CNN & 92.32 & 91.25 & 87.91 & 85.41 & 82.74 & 80.73 \\ \hline \end{tabular}
\end{table}
Table 5: Classification accuracy corresponding to different algorithms after the unknown categories with the Caltech-256 dataset.
## 2 Methods
### Determination of the Prediction Horizon  
This section describes the methodology used to determine the prediction horizon (second part of this contribution).  
Choosing the right HP for the optimal EMS is not trivial. For micro-CHP systems there is no analytical method to determine the HP. In general, the HP value depends on the dynamics of the process. For micro-CHP systems, therefore, the HP will depend mainly on the volume of TES and the profile of thermal energy demand, which are the two factors that determine the filling and emptying times of the TES.  
Choosing the right HP is crucial. An HP that is too long unnecessarily increases the computation time and may make the implementation of the optimal EMS impractical. On the other hand, if the HP is too short, the optimal EMS will not find the best strategy (from an economic point of view). Despite its importance, in the literature on micro-CHP systems the determination of the correct prediction horizon has received hardly any attention. Among the papers included in the literature review presented in the Introduction (Section 1), only in [PERSON] et al. [18] is a brief justification of the chosen HP provided.  
To determine the HP, a preliminary study was carried out, i.e., prior to the running of the 46 simulations. The starting hypothesis in this study was that the higher the HP, the greater the savings, up to a certain value beyond which the savings no longer increase. The plausibility of this hypothesis is justified by the fact that seeing the possible behavior of the system in a longer future time offers more degrees of freedom to operate.  
A total of 161 simulations were conducted. In each of them, the demand supply was simulated over a sequence of days, with the micro-CHP system operated by the optimal EMS. These sequences varied in length (3, 4, or 5 days) and in type of day (winter, summer, transition, or a mix of these three). All simulations were performed under the same scenario (spark gap PL, FiT0, BOL, CZw, EDn) with SOC = 50% and the micro-CHP system initially turned off. Each simulation was repeated several times, increasing the HP until the savings stabilized.  
Figure 2 presents a representative sample of the results. In winter, the heat demand is high, the TES empties quickly, and the HP can be small. In summer, the heat demand is low (only for DHW), the TES empties slowly, and the HP needs to be large. Based on the results of this preliminary study, HP = 80 was chosen for winter days, HP = 120 for transition days, and HP = 480 for summer days. Subsequently, during the running phase of the 46 simulations, in some of them, these HP values had to be increased to achieve stabilized savings. In some cases, this increase in HP reached 360 samples for winter days and 420 for transition days. Below, Section 4 will discuss the results regarding the prediction horizon in more detail.
## 4 Performance Evaluation
### End-to-End Transmission Cost  
End-to-end transmission cost is defined as the ratio of total number of packets in bits sent by all nodes to the total number of useful packets received by the sink. This can be written as [24]  
\[Transmission\ Cost=\frac{T}{U\cdot N} \tag{6}\]  
where \(N\) represents the average number of hops in paths, \(T\) shows the total bits each node transmits, and \(U\) denotes the number of useful bits that the sink received. Equation (6) reflects both communication overhead and energy-efficiency.  
GF-HOP transmits the data packet to the next hop node that is closest to the sink without consideration of PRR. Consequently, PRR between two nodes is not sound. This leads to high transmission cost, as the number of retransmissions increases radically, as shown in Figure 6(a). To reduce the transmission cost, retransmission tasks of the helper node is very important. In GF-HOP with CBF, the number of retransmissions considerably diminishes, as it receives the data packet through helper nodes that have good PRR to the receiver node. However, CBF has problems stemming from inefficient use of control packets, such as RTS/CTS/ACK since CBF only considers PRR of the sender when selecting the helper node. GF-HOP with DRDT has a relatively high energy-efficiency compared to GF-HOP with CBF. DRDT effectively transmits the data and control packets since the helper node is selected by considering the PRR of both the receiver and the sender nodes.  
There is not much difference between GF-ETX with CBF and GF-ETX with DRDT as shown in Figure 6(b) because GF-ETX finds a next hop with the value of the product of PRR and distance. Therefore, the performance of GF-ETX with CBF and that of GF-ETX with DRDT are restricted by transmitting the data packet through the better route with good PRR. However, in GF-ETX with DRDT, performance is enhanced by helper nodes in some parts with poor PRR region.  
Figure 6: Number of helper nodes used at different PRR.
## 3 CFD Modelling as Digital Twin
### CFD Models for Filling and Emptying Processes
#### 3.3.1 Turbulence Equations
A significant detail regarding the values of the dissipation frequency at the pipe walls is that \(\omega\) depends inversely on the dynamic viscosity of the fluid in such a way that the \(\omega\) values are higher at the pipe walls where the air is involved as compared to the areas close to the walls where the water phase is concerned, demonstrating an adequate stratification of the turbulence conditions of both fluids.
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 4 Validation
### Real-World Experiment  
For a real-world experiment, we validate the proposed MFOLC in an outdoor environment. The quadrotor we used in the experiment is shown in Figure 4a. It is a small quadrotor designed by our team. Its power system consists of four sets of Emax RS2205 motors, kangkong 5045 three-blade propellers, and an XRotor 30A electronic speed controller (ESC). The flight controller is also self-developed, based on an STM32F407 microprocessor, as shown in Figure 4b. The onboard angular velocity sensors are three ADXRS646 micro  
Figure 3: Evolution of (**a**) attitude tracking; (**b**) angular velocity tracking; and (**c**) control torques, with angular velocity measurement noise.  
electro-mechanical system (MEMS) gyroscopes. The quadrotor is powered by a 3-cell LiPo battery and has a total mass of 0.9 kg. Figure 4c presents the outdoor flight picture.  
The attitude control loop ran at 200 Hz, and the control parameters were as follows: \(k_{p}=2\), \(\mathbf{K}_{L}=\text{diag}(0.8,0.8,0.8)\), \(\mathbf{K}=\text{diag}(1.225,1.325,1.35)\), and \(\tau=0.01\). By using an anemometer, the wind speed at the flight field was 3.7 m/s from the east. The quadrotor performed a route flight and the proposed MFOLC tracked the target attitude provided by the position controller, and the target angular velocity and acceleration were always set to zero.  
The corresponding experimental results are illustrated in Figure 5. Overall, the MFOLC exhibits a good attitude command tracking performance, while the yaw angle tracking error is relatively large. This is because the yawing torque is produced by the weak reactive torque form each rotor. To maintain hovering and other axial attitude stabilizations, the yawing torque produced by speed difference of rotors is much smaller than rolling and pitching torques, resulting in relatively large attitude tracking errors.
# Haze-Aware Attention Network for Single-Image Dehazing
## 4 Experiments
### Datasets and Metrics  
We used the PSNR and SSIM to evaluate the performance of our HAA-Net. We trained and tested the network on five datasets:RESIDE-Indoor [47], Haze4K [48], RTTS [46], RESIDE-Outdoor [47], NH-HAZE [49], and Dense-Haze [50]. Specifically, the RESIDE-Indoor dataset has a total of 13,990 image pairs. We trained our model using 13,000 of those pairs and then tested the model on an additional 990 images from the RESIDE-Indoor set. We also conducted training and testing on the RESIDE-Outdoor dataset, which is larger and offers a more diverse set of data. This process fully demonstrates the model's generalization capabilities. The Haze4K dataset comprises 4000 image pairs, with 3000 used for training and the remaining 1000 for testing. Compared to RESIDE-Indoor, Haze4K includes both indoor and outdoor scenes, thus making it more realistic. The RTTS dataset consists of 1000 real haze images, which is ideal for assessing the generalization of our model trained on RESIDE-Indoor and Haze4K. It differs significantly from the other two datasets, thus providing a challenging and effective benchmark for evaluating the performance of HAA-Net. The NH-HAZE dataset is made up of 55 image pairs, with 50 pairs used for training and 5 pairs for testing. This setup thoroughly demonstrates our model's ability to handle fog with uneven distribution and varying densities. The Dense-Haze dataset comprises 55 pairs, including hazy images of varying sizes and densities along with their corresponding GT images. We utilized 50 pairs for training and reserved 5 pairs for testing, thereby validating the robustness of our model.
## 4 Discussion  
### Uncertainty of the GPP Prediction Model  
This study constructs a GPP prediction model based on remote sensing data, including NDVI, EVI, NIRv, SIF, and PAR, with a relative root mean square error (rRMSE) of 0.22, demonstrating a high annual GPP prediction accuracy. The remote sensing indices reflect instantaneous observations, whereas GPP represents the cumulative value obtained from continuous observations over eight days. The relationship between instantaneous observations at different time points and the eight-day GPP is still unclear, which constitutes one source of model uncertainty. [PERSON] et al. (2018) simulated instantaneous SIF and daily average GPP at five grassland sites across a wide latitude range, finding a more significant linear relationship between SIF observed by satellites at noon and daily average GPP. How to utilize instantaneous SIF data to assess the total carbon sequestration on a daily or multi-day basis has become a key problem that the scientific community needs to address. Factors such as atmospheric conditions and canopy structure directly influence the energy  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline  & \multicolumn{3}{c}{**DR**} & \multicolumn{3}{c}{**XR**} & \multicolumn{3}{c}{**NR**} & \multicolumn{3}{c}{**SR**} \\ \cline{2-13}  & \multirow{2}{*}{**Rainy**} & **Cool** & **Warm** & **Rainy** & **Cool** & **Warm** & **Rainy** & **Cool** & **Warm** & **Rainy** & **Cool** & **Warm** \\  & & **Dry** & **Dry** & **Rainy** & **Dry** & **Rainy** & **Dry** & **Dry** & **Dry** & **Dry** \\ \hline SPEI03 \(<-1\) & 0\% & \(-22\)\% & \(-4\)\% & 1\% & \(-10\)\% & 5\% & \(-2\)\% & \(-10\)\% & \(-14\)\% & 5\% & 2\% & \(-5\)\% \\ SPEI03 \(\geq-1\) & 0\% & \(-1\)\% & 1\% & 0\% & 3\% & \(-1\)\% & 0\% & 0\% & 1\% & \(-1\)\% &absorption and allocation in leaves, thereby increasing the complexity of the SIF-GPP relationship. There is substantial evidence for the positive correlation between vegetation indices like NDVI and GPP [54]. These indices can supplement the explanatory capacity of SIF regarding GPP (see Table 2), although their specific contribution rates require further analysis. Surface environmental factors such as temperature [55] and relative humidity [56] also significantly affect the GPP of rubber forests; however, due to the length and quality of observational data, this study has not included them in the modeling considerations. This study employs a multilayer perceptron (MLP) to construct a GPP prediction model based on five remote sensing indices, aligning with the data-driven machine learning model established by [PERSON] et al. [57]. The model demonstrates high accuracy in ecosystem simulation. However, the data-driven machine learning model developed in this study involves a linear simplification of the relationship between parameters and GPP during data processing, necessitating further research to clarify the underlying mechanisms.