esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Artificial Intelligence-Based Models for Classifying Defective Photovoltaic Cells
## 3 Results and Discussion
### Convergence and Results of the Models
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Classification Success** & \
\begin{tabular}{c} **Time Spent on** \\ **Hyperparameter Tuning (hr)** \\ \end{tabular} &
\begin{tabular}{c} **Time Spent Testing 50,000 (17,502** \\ **with MLP/100 CNN) Samples (hr)** \\ \end{tabular} \\ \hline KNN & 0.7061 & 0.0017 & 0.0267 \\ SVM & 0.7607 & 0.0223 & 0.0196 \\ RF & 0.7308 & 2.1552 & 8.8071 \\ MLP & 0.7488 & 0.9633 & 15.5442 \\ CNN & 0.8160 & & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results in the five models. Figure 7: Comparison of classification success versus the number of iterations for the five models [23] and authors elaboration. _SVM_ was the model that presented the second-highest efficiency (76.07%), and it was the model with reasonably lower times (search for parameters and training). One of the main goals of sorting was to detect bad cells (group 3). In this sense, Table 3 shows the results of the classification of cells in group 3. In the same way, Table 3 shows the results of the misclassification between groups. Focusing only on the classification of group 3, _SVM_ obtains success results of 90.55%, while _MLP_ obtains 82.24%, _RF_ obtains 77.46%, _KNN_ 70.02%, and the worst result is for _CNN_ with 69.56%. It could also be observed that there was hardly any confusion between cells of group 1 with group 3 or cells of group 2 with group 3. In the first case, _KNN_ and _RF_ did not present confusion (0%), while _MLP_, _SVM_, and _CNN_ presented 0.18%, 0.77%, and 2.17%, respectively. In the second case, _KNN_, _SVM_, and _RF_ presented a value below 1.5%, while _MLP_ presented a value of 6.87% and CNN the highest value, 8.65%. Greater confusion appears between groups 1 and 2. As can be seen, the misclassification of cells in group 1 as group 2 varied between 10.17% for _KNN_ and 25.25% for _RF_. In the case of misclassification of cells in group 2 to group 1 was when the _CNN_ performed better than the other algorithms, with 35.86% for _CNN_, reaching 76.78% in the case of _SVM_. This inaccuracy was due to the similarity between some cells, as can be seen in Figure 6.
## The Methods of Establishing RSP Model and RSHARQ
### A Reliability System with HARQ and CRC Technique (RSHARQ)  
In order to verify the BFRS model, we develop a reliability system with HARQ and the CRC technique (RSHARQ). The system flowchart is shown in Figure 3. The details of RSHARQ are shown below.  
Figure 3: The flowchart of RSHARQ.  
Define one packet with 4 flits, and each flit has 5 bits. In 20 bits, transmission information occupies 16 bits, while the rest is for urgent words or CRC words. In order to improve the system reliability, the lines for CRC words or urgent words should not be influenced by crosstalk. Each flit has 4 information bits and a special bit.  
In the first operation cycle, the system sends 16 bits information and 4 bits CRC words. If the CRC check is right, the transmission is successful. If not, the system sends a packet with each flit's Hamming word, which has a length of 12 bits. If the ECC detects a multi-error, the system will execute ARQ for each flit. If the CRC check is still false with ECC correcting, the system will execute ARQ.
## 3 Project Overview and Data Preprocessing
### Data Acquisition and Processing
(5) The penetration depth (p) also shows a high degree of overlap in the distribution among various classes of surrounding rock, making it difficult to effectively distinguish between different rock grades. After analysis, it was observed that the average penetration depth in the II surrounding rock type was significantly lower than that in IIIa, IIIb, and IV. The II rock type has higher strength, necessitating a low penetration depth and high rotation speed during excavation on site. In contrast, the IV rock type, being soft and weak, requires a relatively minor penetration depth to avoid the risk of collapse. As for IIIa and IIIb, these rock classes are more stable and capable of withstanding more prominent engineering disturbances, and they exhibit the highest penetration depth. (6) The field penetration index (FPI) can effectively identify II surrounding rock but has a lower discriminative ability for IIIa, IIIb, and IV surrounding rock. (7) The torque penetration index (TPI) and specific excavation energy (SE) can quickly identify the II and IIIa surrounding rock types. However, they face difficulty when distinguishing between the IIIb and IV rock types due to the high overlap. According to a statistical analysis of the datasets encompassing various features, we derived the subsequent final parameters, which included the thrust (F), torque (T), net penetration rate (PR), field penetration index (FPI), specific excavation energy (SE), and tunnel penetration index (TPI). The parameters can be considered as preliminary indicators that can be used to distinguish between the various grades of the surrounding rock. Using these datasets as an example, the correlation between the features of the datasets was analyzed; the correlation analysis of these features is presented in Figure 3.  
As depicted in Figure 3, a strong correlation exists between the features. However, employing all of the features as input features may lead to redundancy and potential contradictions, thereby impacting the accuracy of the output results. Moreover, excessive input variables can impede efficient model training and impose higher data recording requirements, rendering the model less practical for real scenarios. Figure 3: Correlation analysis of the independent features.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 1 Introduction
Firstly, we establish a physical model for the high-speed solenoid valve and extract the physical indices from its dynamic driven current. Then, the wavelet packet decomposition is used to extract the energy features of the frequency bands from the current change rate curve of the high-speed solenoid valve. Afterwards, the physical indices and energy features are combined into a comprehensive feature vector. Finally, considering the performance superiority of random forest in terms of fault prediction and diagnosis of various systems [23; 24; 25; 26; 27], it is utilized for failure type prediction of the high-speed solenoid valve using the obtained comprehensive feature vector. In this way, the performance of the data-driven prediction method can be improved even when the physical model cannot be accurately constructed. The main contributions of this paper are as follows:  
* The physical model of the high-speed solenoid valve is established to extract the physical indices from its dynamic driven current curve, and the energy features of the frequency bands are obtained by wavelet packet decomposition from the current change rate curve of the high-speed solenoid valve. Then, the comprehensive feature vector composed of the above physical indices and data features is proposed to characterize the working performance of the high-speed solenoid valve in braking systems. * Random forest classifier is used to predict the failure type of the high-speed solenoid valve based on the proposed comprehensive feature vector at different life cycles, and the relationship between the prediction accuracy and the life cycle is explored. * Experiments on an accelerated aging test dataset composed of 60 high-speed solenoid valves and comparisons with the other related algorithms validate the effectiveness and superiority of the proposed method. The remainder of the paper is organized as follows: In Section 2, the structure of the high-speed solenoid valve and its failure mechanism are analyzed, based on which its physical model is established. Section 3 presents the proposed failure type prediction method for the solenoid valve, and the experiment platform as well as the dataset is introduced. In Section 4, the effectiveness of the proposed method is validated through experimental results. Finally Section 5 draws the conclusion.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on Hyperspectral Images
#### 3.2.1 Main Methods
In 2012, [PERSON] [195] used HyMap airborne hyperspectral image data to study the classification of materials used in urban roads and the state of asphalt road conditions. He mainly distinguished asphalt roads, cement roads, and gravel roads, and based on this result, reported three road conditions: good, medium and poor asphalt roads. However, the experimental results were limited by the spatial resolution of the dataset, and a large number of unclassified pixels were not analysed by the method. Therefore, further studies are required to improve the methods used for reducing the number of unclassified pixels. Currently, some publicly available airborne hyperspectral datasets such as Pavia Centre and University area hyperspectral datasets and Indian Pine hyperspectral datasets [196; 197] are also used for road classification and recognition. In 2012, [PERSON] et al. [196] proposed a directional morphology and semi supervised feature extraction to classify three hyperspectral datasets. The classification accuracy of the roads was the highest, reaching more than 97%; however, this classification accuracy was closely related to the number of training samples and extracted features. [PERSON] et al. [197] studied the extraction of road centrelines from high-resolution images based on shape features and multiple adaptive regression splines. This method combined the shape features and spectral information to extract road segments from high-resolution images and then used multivariate adaptive regression spline functions to extract the road centrelines. The method was applied to the Pavia Centre hyperspectral dataset, and an extraction accuracy of 99% was obtained for the road centreline extraction. This method was based on uniform surface properties; hence, it was suitable only for high-resolution images and not for low-resolution images. In addition, the main limitation of this method was that the threshold in the method must be determined manually. In addition to spaceborne and airborne hyperspectral imagers, UAV hyperspectral imaging systems that have gradually emerged in recent years have garnered increasing attention owing to their low cost and high spatial resolutions. These systems are mainly used for road condition detection and road material identification in specific areas [15; 198]. However, their operating efficiency is lower than that of the spaceborne and airborne platforms, because of their low flight altitude. A summary of road extraction using hyperspectral images is shown in Table 5, which indicates that hyperspectral imaging systems of different platforms have different characteristics for road extraction due to spatial resolution. Spaceborne hyperspectral imagers are primarily used to extract main roads, while airborne hyperspectral imagers can be used for road quality assessment and road condition monitoring.
## 2 Materials and Methods
### Estimation of Feeding Rate  
To better evaluate _A. salina_ feeding behavior, a parallel test with all the same conditions was performed, except for the quantity of brine shrimps. One _A. salina_ specimen was added in each 10 mL glass test tube for each treatment (0-1-10-10\({}^{2}\)-10\({}^{3}\)-10\({}^{4}\) MPs/mL) in triplicate. To assess the ingestion rate and the influence of MPs presence on its trend, counts after 24-48-96 and 168 h were made for each replicate under a Leica DM6B microscope at 40\(\times\) magnification in a [PERSON] chamber (hemocytometer). To obtain the ingestion rate as microalgae, the cells/larvae/h method was used as previously described by [PERSON] [70]. The provided pure culture of _D. salina_ was counted daily before the solutions replacement. An additional positive control was used to evaluate the growth of _D. salina_ without brine shrimps feeding. Moreover, based on intestinal and MPs surfaces measurement, an estimation was carried out on the images obtained at each time point to calculate through the use of ImageJ software [69], the percentage of intestinal tract occupied by the MPs, and consequently subtracted from _D. salina_ ingestion.
## 4 Dataset and Experimental Setups
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Pulse waveform & Gaussian pulse \\ \(-\)10 dB bandwidth & 400 MHz \\ Pulse repeated frequency & 32 kHz \\ Average number & 128 \\ Fast-time sampling rate & 16 GHz \\ Slow-time sampling rate & 15 Hz \\ Sampling points & 4096 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the developed UWB impulse radar. Figure 3: The block diagram and appearance photo of the used UWB impulse radar: (**a**) the block diagram and (**b**) appearance photo. the wall, and human targets are on the other side of the wall. Scenario 3 is the actual ruin, which is a collapsed building. There is a lot of wood, steel, and bricks in the ruins. The UWB impulse radar is placed on the top floor, and human targets are hidden in the ruins. Each of the 3 scenarios has a measuring duration of 40 s, so as to ensure that the collected data contain multiple cycles of respiratory signals. We have collected 26,939 samples in Scenario 1, 6542 samples in Scenario 2, and 2845 samples in Scenario 3. The collected dataset has a total of 36,326 samples. Each sample contains 600 impulse responses, and the fast-time sampling points is 800, so the size of each sample is 800 \(\times\) 600. The ground truth for every target is set to five range bins. The label of each sample is a vector of size 800, where target range bins are set to 1, and non-target range bins are set to 0. In order to train the model and analyze the results, these samples are randomly divided into two datasets according to the ratio of 8:2, namely, the training set and the validation set. All the implementations of the method are developed on PyTorch. The training parameter mini-batch size and total epoch number are set to 32 and 200. The learning rate is set as 0.001 initially and decreases 10% every 10 epochs. All the experiments are run on one NVIDIA GeForce GTX3090 Ti GPU card (with a 24 GB memory) with CUDA for acceleration.
# Optimized Parameter Search Approach for Weight Modification Attack Targeting Deep Learning Models
## 3 Methodology
### Modification of the Selected Weights  
Once the critical parameters are selected, noise is added to obtain the desired misclassification. That modification is incorporated step-by-step to observe the effect of such manipulation on the chosen weights. Moreover, adding less noise in several steps makes the attack more optimized since the manipulated weights will be more similar to the original ones, i.e., the attack will be less perceptible. The magnitude of the noise added in each step is defined by the parameter \(\epsilon\), and the number of steps is indicated by the parameter \(s\). Therefore, the upper limit of the difference between the original weight value and the modified one is \(s\cdot\epsilon\).  
As mentioned above, the gradient indicates the impact each input feature has on the prediction of the target neural network. In the training phase of a neural network, its weight gradients are calculated. Those values are subtracted from the previous value of the parameters, causing model learning. In other words, the subtraction of the gradient generates model learning while the addition would imply a step backwards in the learning (i.e., the model would unlearn). Therefore, when a value of the same sign of the gradient is subtracted from the weight, it makes that weight gain more important in the prediction, while addition would make that parameter lose strength.  
With this in mind, assuming that the attacker wants to misclassify one of the clusters, they can follow two different strategies. On the one hand, one can weaken the impact of the most-influential weights of the target cluster by adding the multiplication of the selected noise by the sign of the corresponding gradients. With previous modifications, the attacker would make those weights have a lower impact on the prediction, achieving misclassification of the cluster. On the other hand, the second strategy strengthens the most-influential parameters associated with the rest of the clusters. This is accomplished by subtracting the multiplication of the noise by the sign of the corresponding gradients. In this way, these weights would gain impact in the prediction, transferring data from the target cluster to the other clusters.  
The second approach could lead to worse results because it can modify some weights without control. For example, assuming a dataset \(A\) containing three different clusters, \(A=C_{1}\cup C_{2}\cup C_{3}\), where \(W_{i}\) is the selected weights assigned to \(C_{i}\), and \(C_{1}\) is the targeted cluster to misclassify. Then, the second method would modify \(W_{2}\) and \(W_{3}\), strengthening those weights and converting elements from \(C_{1}\) to \(C_{2}\) or \(C_{3}\). However, items from \(C_{2}\) could convert to \(C_{3}\) and vice versa since strengthening of \(W_{2}\) and \(W_{3}\) affects to \(C_{1}\cup C_{3}\) and \(C_{1}\cup C_{2}\), respectively. That would not happen with the first method because it is more focused on \(C_{1}\), reducing the impact of \(W_{1}\), which directly affects \(C_{1}\).
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 2 Methodology and Foundations
### Deep Reinforcement Learning  
DRL involves two fields of knowledge, namely RL and DL. RL is a machine learning (ML) approach appropriate for decision-making problems that need automatic handling based on trial and error. An RL agent periodically interacts with an environment by taking actions and receiving a reward (related to observations of the environment's states) that indicates if the action was good or not [25; 108]. RL can be understood as a Markov decision process (MDP) comprising a space of states \(S\), a space of actions \(A\), and an immediate reward function \(R(s_{t},a_{t},s_{t+1})\)[109]. RL algorithms intend to find an optimal policy for maximizing the long-term reward in the environment by considering its states and the actions available per state.  
RL algorithms can be model-based or model-free. A model-based RL algorithm [110] learns an optimal policy by having access to an environment's model (a function able to predict the state, actions, and rewards) or obtaining it purely from experience. Model-free RL algorithms learn an optimal (stochastic or deterministic) policy (also known as on-policy algorithm) or optimal Q-value function (also known as off-policy algorithm) [26; 27]. Actor-critic [20], state-action-reward-state-action (SARSA) [111], and proximal policy optimization (PPO) [112] exemplify on-policy RL algorithms. Q-learning [113] is the most popular  
Figure 2: Resource management phases.  
off-policy RL algorithm. For further information about RL and its algorithms, we refer the reader to [20; 114; 115].  
RL algorithms realize many interactions to achieve an optimal policy according to design requirements. The increasing number of iterations generates an expensive process due to the amount of information stored and the computational cost required. To overcome this challenge, ML proposes DRL that combines RL and DL to resolve high-dimensional and infinite-state problems [116]. DRL uses RL to train DNNs (e.g., FNNs and RNNs) that timely learn optimal policies [25; 117]. Some of the most relevant DRL algorithms are deep Q-network (DQN--also known as deep Q-learning) [118; 119], double DQN (DDQN) [120; 121], deep Q-learning with prioritized experience replay (prioritized DQN) [122], Dueling DQN [123], and distributional DQN [124]. DRL and RL algorithms have been proposed in the 5G network in applications such as SDN routing [22], Internet of Things (IoT) [125], HetNets [126], and unmanned aerial vehicle (UAV) [127]. For further information about DRL and its algorithms, we refer the reader to [25; 27; 128].  
The next Sections detail research papers that use RL and DRL to perform one or various resource management phases in NSL.
## Materials and Methods
### Classification Models
#### 2.4.2 Extreme Learning Machine  
The extreme learning machine (ELM) is a kind of machine learning system or method based on a feed-forward neural network, which can overcome the problems of slow training speed or over-fitting [35]. According to empirical risk minimization theory, the initialized input weight and offset of the network model can be set randomly and the corresponding output weight can be obtained through a one-step calculation. The ELM network model is also divided into three layers for training and learning, namely the input layer, hidden layer, and output layer. ELM is considered to have potential advantages in terms of learning speed and generalization ability over most methods. Some improved versions of ELM have obtained a deep structure, by introducing an auto-encoder to construct or stack hidden layers, and can carry out feature learning [22], which makes ELM more suitable than others for processing multi-class samples.
## 4 Numerical Results
### Case Studies and Discussions
#### 4.2.3. The CVPP's Optimal Schedule in an Extreme Operating Scenario: Plan 2 of the BC Contract
Figure 16: The CVPP’s optimal schedule if the BC contract has Plan 2 and an upward reserve capacity of 4 MW. Figure 15: The CVPP’s optimal schedule is if the BC contract has Plan 2 and an upward reserve capacity of 3 MW. It is easy to see that as the level of relaxation increases, ESS will also participate more in providing upward reserve. Consequently, CVPP can allocate more capacity to the DA market, thereby reducing losses in this market (Table 4). By contrast, the profits that CVPP expects to obtain from the BE market have mostly stayed the same. This can be explained by the fact that the capacity of RESs is not inherently redundant. Therefore, the participation of ESS in reserve provision only causes the capacity allocation of RESs to the BE market to shift to the DA market partially but does not mean increasing the CVPP's upward reserve capacity.