esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Application of ML Algorithms in CDSM/SuperCDSM Experiments  
A semi-supervised ML approach to dark matter search was proposed in [6]. This paper used a convectional auto-encoder and semi-supervised convolutional neural network (CNN) to directly detect dark matter. A deep convolutional neural network (DCNN) was used in [7] to solve the tasks of retrieving Lagrangian patches from which dark matter halos will condense. The results of the investigation showed that, if the proposed methods were properly tuned, they can outperform likelihood-based methods. The recurrent neural network (RNN) was implemented on the trigger FPGA to maximize the sensitivity to the low-mass dark matter of the SuperCDMS SNOLAB experiment [8]. By doing so, the energy estimator based on the combined information of filtered traces from individual detector channels was improved. With the performed modifications, the trigger threshold was lowered by 22%. The deep learning method was proposed in [9] to map the 3D galaxy distribution in hydrodynamic simulations and the underlying dark matter distribution. In this research, as two-phase CNN was used to generate fast galaxy catalogs, and the results were compared with traditional cosmological techniques. The proposed method outperformed the traditional techniques. Gradient-boosted trees were used in [10] to model dark matter halo formation. In [11], the Bayesian optimization for likelihood-free inference (BOLFI) algorithm was used to reconstruct the two-dimensional position and to determine the size of the interaction charge signal. The results showed that the BOLFI algorithm provides improved accuracy of 15% in reconstruction when compared in the case of events at large radii (R > 30 cm, the outer 37% of the detector). The investigation also showed that the proposed algorithm provided smaller uncertainties compared to other methods.
# Differential Evolution: A Survey and Analysis
## 3 Parameter Control
### Adaptive Parameter Control
#### 3.2.3 Self-Adaptive Differential Evolution (SaDE)  
Self-adaptive differential evolution (SaDE) is simultaneously applied to a pair of mutation techniques \"DE/rand/1\" and \"DE/current-to-best/2\" [52]. The adaptation technique of the parameter consists of two chunks: the probability of the adaptation \(p_{i}\), where \(i\) = (1, 2), and the DE parameters \(F_{i}\) and \(Cr_{i}\). The probability of producing a mutation vector based on the two strategies approaches is 0.5 and this is updated every 50 iterations using the following method:  
\[p_{1}=\frac{ns_{1}\ (ns_{2}+nf_{2})}{ns_{2}(ns_{1}+nf_{1})+ns_{1}(ns_{2}+nf_{2})}\]  
\[p_{2}=1-p_{1}\]  
where \(ns_{i}\) and \(nf_{i}\) are the numbers of offspring vectors constructed by the \(i\)th \(i\) = (1, 2) strategy that was a success or failure in the selection process over the last 50 generations. It is assumed that this adaptation process can progressively develop the most appropriate mutation strategy at diverse learning phases for a given problem. The mutation factors \(F_{i}\) are autonomously created at each iteration based on a normal distribution, \(NR\) with a mean of 0.5 and a standard deviation of 0.3,  
\[F_{i}=NR(0.5,\ 0.3)\]  
The crossover rates \(Cr_{i}\) are autonomously formed based on a normal distribution with a mean of \(Cr_{m}\) and a standard deviation of 0.1. The mean \(Cr_{m}\) approaches 0.5, is changed every 25 iterations and is set to be the mean of the effective \(Cr\) over the previous 25 generations.  
\[Cr_{i}=NR(Cr_{m},\ 0.1)\]  
\[Cr_{m}=\frac{1}{k}\sum_{k=1}^{k}Cr_{suc}(k)\]  
where \(K\) is the counters of effective \(Cr\) values and \(Cr_{suc}(k)\) indicates the \(k_{\text{th}}\) value.  
To accelerate the convergence, a local search technique (Quasi-Newton method) is applied to respectable individuals after 200 generations. SaDE has been further developed by applying five mutation strategies to resolve a group of constrained problems [60].
## 4 Materials and Methods
### Instrumentation
#### 4.2.2 Experimental Set-up  
The schematic diagram and photograph of the steady-state SRR measurement set-up is demonstrated in Figure 6.  
Figure 5: (**a**) Schematic diagram of designed probe, (**b**) perpendicular cross section of probe near proximal end as per the proposed design, (**c**) fabricated prototype of the probe, (**d**) beveled tip of the probe at proximal end, (**e**) arrangement of fibers on the tip of probe at proximal end (after fabrication)  
The set-up consists of three major parts: a light source (LS), a probe (P) and a light detection system (D). Here the light source (LS) provides a laser light of center wavelength 450 nm. This light source includes a PL450B (THORLABS) laser diode on a mount connected to a constant DC current supply to drive the diode and a temperature controller. The laser diode emits constant power output which is chopped at 200 Hz by a chopper. The probe (P) is used to deliver the light from the light source to the surface of the sample and transmit the collected backscattered light at different spatial locations on the surface of the sample to the detector (D). The design and arrangement of fibers in the probe is explained in Section 4.2.1. The proximal end (P1) of the probe is kept onto the surface of the sample. One of the fibers (Fiber number 74 in Figure 5e), approximately at the center of the proximal end (P1) is coupled to the chopped the light source to irradiate the sample. Distal ends (P2) of remaining fibers are connected to a brass plate, which is mounted on an x-axis translation stage connected to a stepper motor (M1). Light coming out from the distal end of each fiber (except fiber number 74) is collected by a photomultiplier tube (PMT) (Hamamatsu R928), which is also mounted on a y-axis translation stage through another stepper motor M2. The relative motions of the x-axis and y-axis translation stages couple light sequentially from each of the fibers to PMT. The signal from PMT is fed to a lock in-amplifier (Model no: SRS-830). A reference signal obtained from the chopper power supply is also fed to the lock-in amplifier. The stepper motors (M1 and M2), as well as the lock-in amplifier are interfaced to the computer and a LABVIEW based program has been developed to automate the entire system and collect signal from each of the output fibers.
## 2 Dynamic Model of FPSE
### Root Locus Model  
Secondly, a root locus analysis model is considered, which solves the linearized EOMs as an eigenvalue problem. The root locus analysis is a well-known graphical method in control and stability theory for examining how the roots of a system change with the variation of a certain system parameter [6; 22]. The roots are calculated by solving a fourth-order characteristic equation obtained from Equation (7) in the state space, given as:  
\[\left[\begin{array}{c}\dot{z}_{1}\\ \dot{z}_{2}\\ \dot{z}_{3}\\ \dot{z}_{4}\end{array}\right]=\left[\begin{array}{cccc}0&0&1&0\\ 0&0&0&1\\ -K_{pp}&-K_{pd}&-C_{pp}&-C_{pd}\\ -K_{dp}&-K_{dd}&-C_{dp}&-C_{dd}\end{array}\right]\left[\begin{array}{c}z_{1} \\ z_{2}\\ z_{3}\\ z_{4}\end{array}\right] \tag{9}\]where \(z_{1-4}\) are the state variables, defined as \(z_{1}=x_{p}\), \(z_{2}=x_{d}\), \(z_{3}=\dot{z}_{1}\), and \(z_{4}=\dot{z}_{2}\).  
The real part of the root indicates whether the vibration responses are diverging or converging, while the imaginary part corresponds to the operating frequency. The divergence of piston motion implies an engine operation forming a limit cycle and producing a power output [23]. The root locus analysis considers all terms of the linearized EOMs including the cross-coupling terms. Therefore, this method is rather complex and requires more theoretical and mathematical backgrounds compared to the simple vibration model above.
## 4 Classification of MAC Protocols Based on Channel
### Random Selection Based
#### 4.11.1 Ogc Mac  
A novel OFDM MAC based on contention is proposed for reliable, efficient, fast, collision-free resource block allocation. The protocol reduces the use of contention windows (CWs) and improves resource allocation efficiency by proposing a new CCH architecture. Multi-carrier burst contention (MCBC) [99] inspired its creation, which reduces the size of CWs and reduces resource wastage by sequential channel allocation with the traditional TDMA approach. The bandwidth of CCH is divided into sub-channels by OFDM. A group contention strategy is proposed to overcome resource wastage along with a greedy approach. The RBs are chosen using the RB selection policy [100], where RBs are randomly selected from the groups formed from the subdivision of frame, to minimize collision when broadcasting a beacon to one-hop neighbor. A MAC announcement is initiated before a beacon is transmitted and embedded in the beacon. The RBs with a collision flag are considered collisions that occurred in the previous frame and are released. The nodes know the status of their two-hop neighbor and broadcast accordingly.  
The protocol improves throughput by eliminating the hidden terminal problem and by avoiding merger and access collision. By proper resource utilization, resource wastage is minimized. The major disadvantage of this protocol is CW overload, causing delays in packet delivery. The network scalability in dense scenarios is not considered.
# Induction of Highly Dynamic Shock Waves in
## 3 Results
### FEM Simulation of Shock Waves Occurring after Tool Engagement and Chip Breakage  
In order to investigate the causes of the dynamic excitations and workpiece deformations, FEM simulations with high temporal resolution were carried out for the process of chip separation. In particular, the temporal change of the strain fields in the y-direction was examined, which is shown in Figure 12. It can be observed that expansion waves are released from the left side of the workpiece over a longer period of time, propagating in the direction of the upper right side of the workpiece (maximum drawn as black and white dashed lines). The maximum strain values of these shock waves are clearly above the values of the measured strains in Figure 11. This is due to the fact that a significantly longer time span (60 ms) has elapsed in the measurement in contrast to the time step in the simulation (0.5 \(\upmu\)s).  
Figure 11: Image of the tool and the workpiece as well as the corresponding dynamic strain result after the last tool impact.  
Figure 12: FEM simulation of shock waves after chip breakage for different time steps. The black and white dotted line is located on the maximum of the strain releasing from the lower left and symbolizes the strain front propagating through the material.  
The calculation of the propagation velocity of the shock wave is done in Figure 13. For this purpose, the maximum in the area of the shock wave (black/white dashed line) is evaluated and the amount of the displacement vector is added to the last time step. The shock wave speed cs results from the gradient in the plotted path-time diagram and is cs = \(3318\pm 452\frac{m}{s}\). According to [PERSON] [15], the sound velocities in structural steel are 5850 \(\frac{m}{s}\) in the longitudinal direction and 3230 \(\frac{m}{s}\) in the transverse direction. The propagation velocity of the simulated strain front thus corresponds within the uncertainty with the transverse propagation velocity of a sound wave in structural steel.  
In the last time step of the simulation (Figure 12, bottom right) it can be seen that the observed shock wave runs to the top right against the edge of the workpiece. From this boundary surface, the wave is likely to be reflected back and forth in the workpiece until it is completely damped.  
This explains why dynamic movements of the strain fronts are maintained over longer periods of time and can still be detected by speckle photography 300 ms after the last tool impact.
# A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection
###### Abstract
By creating a dedicated model for lung cancer, it is possible to leverage domain-specific knowledge and optimize the architecture to capture the unique characteristics and patterns present in lung WSIs. This approach is crucial because lung cancer diagnosis requires a high level of accuracy and specificity, and a specialized model can potentially outperform generic models in this specific domain. Moreover, training complex networks, such as ResNet, is a computationally expensive and time-consuming task. Thus, this paper aims \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Methods** & **Datasets** & **Results** \\ \hline [PERSON] et al. (2021) [27] & ResNet-50 using original WSI as input & 9662 lung cancer WSIs & AUC of 0.9594 (ADC) and 0.9414 (SCC). \\ \hline [PERSON] et al. (2022) [28] & Custom CNN & 5000 histopathology images per category (benign, ADC, SCC) & F1-score of 0.811 (ADC); precision of 1.00, recall of 0.943, F1-score of 0.971 (benign); precision of 0.774, recall of 0.998, F1-score of 0.872 (SCC); \\ \hline [PERSON] et al. (2023) [29] & Pre-trained CNNs (GoogLeNet, ResNet-18, and SuffleNet V2) and custom CNN & 5000 histopathology images per category (benign, ADC, SCC) & ResNet-18 F1-scores: 0.98 for ADC, 1.00 for benign, 0.98 for SCC. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Proposed approach to detect lung cancer according to a given WSI (adapted from [3]). to introduce a novel and optimized CNN architecture for the accurate detection of lung cancer in WSIs. Additionally, the performance of existing CNN designs for this task is analyzed and compared. The proposed model has the potential to significantly improve the efficiency and accuracy of the WSI assessment, which is a crucial step in cancer diagnosis and therapy, while reducing the amount of time and computational cost needed to train a model. The outcomes of this work are foreseen to be implemented in a medical system of WSIs analysis. This paper is divided into three more sections in addition to the Introduction: the second section, Materials and Methods, presents a detailed description of the dataset used in this study, as well as the tested and developed architectures of the neural networks employed to achieve the research objectives. In the third section, Results and Discussion, the characteristics of the training environment are illustrated, and the outcomes of training the neural networks (NNs) with varied numbers of epochs are discussed and analyzed. The fourth section, Conclusion and Future Work, concludes with the main findings, limits of the study, and future steps.
# Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities
## 4 Problem Formulation
### Metrics Formulation
#### 4.2.2 Performance Metrics  
The _device registration time_ is the time taken for the registration of specified devices with repositories. Note that, in the OWL-based approach, besides the time taken for storing descriptions of the capabilities of the devices, the device registration time also includes the time for reasoning against both the device descriptions and the background knowledge for deriving inferred facts. There are two kinds of characteristics that will be collected using this metric. The first is the device registration time for the two approaches with specified test background knowledge representations \(B_{i}\in B,1\leq i\leq k\) versus an increasing number of devices. The second is the device registration time for a specified number of devices in the OWL-based approach versus the test background knowledge representations \(\{B_{1},B_{2},\ldots,B_{k}\}\). The objective of collecting these metrics is to compare the device registration times in the two approaches against the increasing number of devices and to show how the registration time of the specified devices varies with as the completeness of the background knowledge increases in the OWL-based approach.  
For each query, the _query response time_ is the processing time of the query required by the query processor of the system. We compared the average query response times of a specified number of queries in the two approaches with a specified test background knowledge representation \(B_{i}\in B,1\leq i\leq k\) versus an increasing number of devices. Additionally, we compared the average query response time of a specified number of queries against a specified number of devices versus the test background knowledge representations \(\{B_{1},B_{2},\ldots,B_{k}\}\) in the OWL-based approach.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results
### Simulation Results and Analysis
This distinction was further corroborated by the sharpness of the peaks in the 3D intensity profiles (Figure 8b,d,f), where the MVDR and CNN results exhibited significantly narrower focal lobes compared to the DAS. Notably, while the presence of two damages introduced increased noise in Figure 8b (DAS) and Figure 8f (CNN), the MVDR results (Figure 8d) maintained nearly noise-free imaging quality. These observations confirm that the CNN method outperformed DAS in noise suppression and spatial precision but did not yet achieve the robustness of MVDR in fully eliminating interferences between multiple damage signals. From the quantitative comparison in Table 1, it is evident that the API and SNR values obtained using the CNN method were similar to those of the single damage scenario. This similarity was further verified by examining the amplitude contours along the horizontal line at the location of the two injuries, as shown in Figures 9 and 10. The CNN method exhibited a narrower main lobe width and lower side lobe levels compared to the DAS method. The results show that the MVDR method can effectively suppress the interference of the damage signals with each other, and the CNN method, although it reduces the noise level compared with the DAS method, is still affected by the interference between the damage signals and is not yet able to achieve the performance of MVDR. Figure 8: Imaging results of three imaging methods on dual-damage simulation data: (**a**) DAS, 2D, (**b**) DAS, 3D, (**c**) MVDR, 2D, (**d**) MVDR, 3D, (**e**) CNN, 2D, (**f**) CNN, 3D.
# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 4 Experimental Results and Discussion
For the complexity of the ResNet50-based network, by reducing the network depth, the classification accuracy is observed to be increased. The two-stage Model IV with the shortest depth has the best accuracy, the complete four-stage Model II has the least accurate result, and the three-stage Model III ranks in the middle. All three transfer learning models had an accuracy above 0.76. This suggests that complex networks do not necessarily mean better accuracy when it comes to classification tasks on a simple skin diseases dataset with only a few classes. Instead, it shows that simplifying the network can obtain enough accuracy and also reduce the effort of training. The confusion matrix of Model IV with the best accuracy using transfer learning and the confusion matrix of Model II with the worst accuracy without transfer learning are shown in Figure 5.  
Next, the effects of the types of augmentation techniques on the accuracy of skin disease classification were evaluated using Model IV. With no augmentation techniques applied at all, the model yields a 0.7835 accuracy. When a random scale with a factor from 0.6 to 1.4 is applied, an average accuracy of 0.7785 is obtained. When a random rotation from 0 to 360 degrees is applied, the model shows 0.7620 accuracy. As for when random reflection in the left-right direction and top-bottom direction is applied, the accuracy is 0.7735. An accuracy of 0.7765 is obtained when applying random horizontal and vertical shear from 0 to 45 degrees. With all the above types of augmentation combined, the model gives a 0.7430 accuracy. The comparison of augmentation types is summarized in Table 2. It can be seen that the worst result of 0.7430 comes from using the augmentation techniques combined, using no augmentation techniques at all yields the best accuracy of 0.7835, and all other augmentation techniques used reduce the classification accuracy. The results suggest that for skin disease classification, using basic image manipulation techniques for data augmentation does not necessarily help in improving the classification performance of the networks and should be used cautiously. In Table 2, confusion matrices of Model IV without using data augmentation and using combined data augmentation are shown in Figure 6.  
Figure 5: (**a**) Confusion matrix of Model IV using transfer learning; (**b**) confusion matrix of Model II trained from scratch. Figure 6: (**a**) Confusion matrix of Model IV without data augmentation; (**b**) confusion matrix of Model IV using combined data augmentation. Experiments were carried out to compare the choices of different mini-batch sizes ranging from 5 to 80, and the results are summarized in Table 3. It can be seen that the smallest mini-batch size of 5 has the lowest accuracy of 0.7560, while a mini-batch size of 40 yields the best accuracy of 0.7905. The corresponding confusion matrices are shown in Figure 7. The results show the accuracy can be minorly improved by choosing a proper mini-batch size.
## 3 Methodology
### Techniques for Explainable AI  
We utilized two Explainable Artificial Intelligence (XAI) techniques, SHAP (SHapley Additive exPlanations) version[0.46.0] and LIME (Local Interpretable Model-agnostic Explanations) version[0.2.0.1], to elucidate the predictions of our machine learning models (Figure 2).  
SHAP (SHapley Additive exPlanations) is grounded in cooperative game theory and offers a cohesive methodology for elucidating the outputs of any machine learning model [27]. It calculates Shapley values, which denote the contribution of each characteristic to the forecast, so ensuring consistency and local correctness in the explanations. SHAP provides global interpretability by consolidating local explanations, facilitating the evaluation of feature significance throughout the entire dataset [28]. This work calculated SHAP values for the trained models to measure the influence of each internal feature on efficiency forecasts. The SHAP library's visualization tools, including summary plots and dependence plots, enabled a thorough comprehension of the impact of features on model outputs. This facilitated the identification of critical elements influencing photovoltaic efficiency and offered insights into the model's decision-making process. LIME (Local Interpretable Model-agnostic Explanations) aims to deliver localized explanations by approximating a complex machine learning model with an interpretable model centered on a particular prediction [29]. It disturbs the input data surrounding the instance of interest and monitors alterations in the predictions, so creating a local surrogate model that is readily interpretable, such as linear regression or a decision tree. Utilizing LIME, we elucidated individual predictions of the efficiency model, emphasizing the characteristics that most significantly influenced a particular prediction and the nature of their impact. This localized interpretability is essential for comprehending anomalies or particular instances where the model's forecast is crucial for decision-making.