esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Reliable Massive Capacity SSDs through a Flash Aware RAID-Like Protection +
## 4 Design of mSAPL
### Data Placement in a Stripe  
mSAPL adopts the latest flash memory friendly stripe construction policy, called eSAP [14], to minimize parity update overhead for all user data, regardless of the protection strength. Conventional RAID approaches impose excessive parity update overhead of flash memory storage, due to out-of-place characteristics. eSAP reduces the parity overhead through dynamically constructing a stripe in the order of write requests, regardless of the logical number of user data. In this way, a stripe is formed with only newly written data, so there is no need to read in order to generate new parity. However, a partial stripe is constructed according to the size of the data write request and the stripe may not be completed. The data of the partial stripe must be protected with a parity, called Partial-stripe Parity (PP), before the full stripe is completed. The PP write consumes more user space, which increases the GC cost and reduces the performance and lifetime due to additional writes. For large-capacity SSDs with a large number of flash chips, the cost of such parity management is higher. mSAPL proposes a new way to deal with partial stripe parity. The method reduces the GC overhead of the flash memory storage and it is described in the following section.
## 3 Results and Discussion
### Turbulent Kinetic Energy and Dissipation Rate Profiles  
As essential turbulent physical quantities describing the flow state, it is necessary to discuss further the capabilities of the six low-Reynolds number \(k\)-\(\varepsilon\) models for predicting turbulent kinetic energy and dissipation rate of the transitional ventilation flow with the Coanda effect. Figures 6 and 7 illustrate the profiles of turbulent kinetic energy and dissipation rate. It is shown that most of the models predict two peak values of turbulent kinetic energy and dissipation rate in the boundary layer and shear layer (0.9 \(<\) y/L \(<\) 0.94) because  
Figure 5: Comparison of the position of the maximum dimensionless x-velocities of the jet measured by PIV and modeled by six low-Reynolds number \(k\)-\(\varepsilon\) models. (**a**) Re = 1000; (**b**) Re = 1750; (**c**) Re = 2500.  
of the great velocity gradients in these two regions. The LS model predicts tremendous values of turbulent kinetic energy and dissipation rate near the wall. The best experimental setup reveals that the turbulent dissipation rate rises to an unknown constant value approaching the wall [38]. The turbulent kinetic energy is equal to 0 because of the no-slip condition. Thus, the boundary conditions of the LS model's turbulent kinetic energy and dissipation rate (\(\left(\partial k/\partial y\right)_{w}=0\) and \(\varepsilon_{w}=0\)) may be inappropriate (see details in Table 2). The LB model predicts a too-low level of turbulent kinetic energy (close to 0) and a too-high level of turbulent dissipation rate in the recirculation region (y/L < 0.9), which leads to the inaccurate prediction of velocity, as mentioned in Section 3.1. At x/L = 0.07, the AKN model cannot capture the second peak of turbulent kinetic energy. Moreover, at Re = 2500, the YS model cannot capture that, either. The turbulent kinetic energy and dissipation rate predicted by the CHC model seems more realistic than other models because this model can actually capture the two peaks of \(k\) and \(\varepsilon\) without unreasonably large values.
###### Abstract  
This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models' accuracy and applicability. In addition, a simple ranking system was used to evaluate the models' performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.  
ground vibration; blasting operation; boosting-CHAID: support vector machine; input selection published maps and institutional affiliations.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 4 Experiments
### Experimental Results  
The detection results are categorized into four types of samples: true positives (TPs), false positives (FFs), false negatives (FNs), and true negatives (TNs). Precision, defined as the ratio of correct detections to the total number of instances classified as positive, quantifies the accuracy of positive predictions. Recall, on the other hand, measures the proportion of actual positive instances that were correctly identified out of the total number of actual positives. There is typically a trade-off between precision and recall; an increase in one often leads to a decrease in the other. To evaluate the overall performance of a detection system, average precision (AP) and mean average precision (mAP) are utilized. The precision-recall (PR) curve is constructed by plotting precision against recall at varying thresholds. The AP is calculated as the area under the PR curve, while the mAP represents the mean of the AP scores across all classes.
# A General Framework Based on Machine Learning for
## 2 Background
### Machine Learning Techniques  
Throughout this research, we produced four Machine-Learning-based algorithm selection strategies. These algorithm selectors are based on four different classifiers available in Microsoft Azure ML. In all cases, we used the default configuration of each of these techniques as provided by the platform. The four Machine Learning techniques used are briefly described below. For specific details on the implementation of such methods, the reader is welcome to consult Microsoft Azure ML technical documentation ([[https://docs.microsoft.com/en-us/azure/machine-learning/](https://docs.microsoft.com/en-us/azure/machine-learning/)]([https://docs.microsoft.com/en-us/azure/machine-learning/](https://docs.microsoft.com/en-us/azure/machine-learning/)) accessed on 16 March 2021).  
* Multiclass Logistic Regression (MLR) is a well-known method used to predict the probability of an outcome and is particularly famous for classification tasks. The algorithm predicts the probability of occurrence of an event by fitting data to a logistic function.
* Multiclass Neural Network (MNN) is a set of interconnected layers in which the inputs lead to outputs using a series of weighted edges and nodes. A training process takes place to adjust the weights of the edges based on the input data and the expected outputs. The information within the graph flows from inputs to outputs, passing through one or more hidden layers. All the nodes in the graph are connected by the weighted edges to nodes in the next layer.
* Multiclass Decision Forest (MDF) is an ensemble learning method for classification. The algorithm works by building multiple decision trees and then by voting on the most popular output class. Voting is a form of aggregation, in which each tree outputs a non-normalized frequency histogram of labels. The aggregation process sums these histograms and normalizes the result to get the \"probabilities\" for each label. The trees that have high prediction confidence have a greater weight in the final decision of the ensemble.
* Multiclass Decision Jungle (MDJ) is an extension of decision forests. A decision jungle consists of an ensemble of decision Directed Acyclic Graphs (DAGs). By allowing tree branches to merge, a decision DAG typically has a better generalization performance than a single decision tree, albeit at the cost of a somewhat higher training time. DAGs have the advantage of performing integrated feature selection and classification and are resilient in the presence of noisy features.  
It is essential to highlight that, although we used Microsoft Azure ML for this work, the rationale is to separate the Machine Learning algorithms from the rest of the components of the algorithm-selection process. Then, any other application such as IBM Watson Machine Learning Service, Google Cloud Machine Learning, or Weka could be used for this purpose. In our particular case, it was a personal preference, given our previous experience with the platform.  
Figure 2: Distribution of the values of the seven features that characterize the CSP instances, grouped by set.
## 2 HST-Net Model
### Backbone Network with Transformer-CSP
#### 2.1.2 Transformer-CSP Module and Self-Attention Mechanism  
Drawing inspiration from the transformative capabilities of the transformer network and the CSP architecture, the proposed Transformer-CSP module enhances the model's capacity to assimilate contextual information and more nuanced feature representations. It incorporates a self-attention mechanism, which enables the model to weigh the importance of different features in the input, providing the network with a more global understanding  
Figure 2: Backbone network structure.  
of the input data. By assigning varying degrees of attention to different features, the model can focus more on the relevant parts of the input and less on the less relevant parts, enhancing the overall feature representation.  
The SPPF module incorporates spatial pyramid pooling to address multi-scale object detection. By conducting pooling operations of diverse sizes on the same feature map, it extracts features at multiple scales, thus endowing the model with the capability to adapt to a myriad of object detection tasks. Three sets of anchor boxes are defined for P3/8, P4/16, and P5/32 layers, aiding in the precise localization of objects across different scales.  
The final C3 module demonstrates a notably swift execution speed, attributed to its employment of a reduced 20 \(\times\) 20 feature grid. In contrast, the initial C3 module, characterized by a very small stride and a correspondingly large grid, executes at a considerably slower pace [34]. The Conv-BatchNorm_Normalization-Leaky_Relu (CBL) module consists of the Conv, batch normalization (BN), and leaky rectified linear unit operations (Leaky_Relu), which results in a large computational load. In order to improve the detection accuracy, this paper presents a novel Transformer-CSP module by introducing the self-attention mechanism. The Transformer-CSP module is shown in Figure 3.
# A Distributed Hybrid Community Detection Methodology for Social Networks
## 2 Background
### Hybrid Approaches  
The real-world social networks natively include two different information modalities, the network topology structure and the user content information that could both be used for the identification of meaningful, in terms of social context, communities. The methods and algorithms that make the valid assumption that the community extraction process should be determined by the combination and not merely by either the network connectivity structure or the node attribute information are classified as hybrids and can consequently be divided to probabilistic inference-based models and heuristic measure-based models.  
The probabilistic inference-based models are practically mixed probabilistic generative models that combine the network topology and the user content information in order to infer the potential subjacent network hierarchy. The most considerable representatives are the CESNA (Community extraction from Edge Structure and Node Attributes) [13] and its alternatives [9; 27; 29], the BAGC (Bayesian Attributed Graph Clustering) [30], the GBAGC (Generalized Bayesian Attributed Graph Clustering) [26] and Metacode [28]. Generally, those methods try to infer the probable generated community distribution by combining the network linking generation and the node attribute classification. However, even if those approaches seem prosperous in terms of efficiency, as it is underlined in [7], they are not only deeply sensitive to the initial values and the corresponding input data type representations but also require exhaustive statistical analysis of the network topology properties for the model definition.  
The heuristic measure-based models consider the topological properties and the content attributes of each social graph node as input to a heuristic function which outcome determines the posterior community structure. The most significant heuristic measure-based representatives include, but not limited, to models that:  
* Handle the network connectivity information, expressed in vectors, as part of each corresponding user node information [22; 23; 31];
* Construct content edges by selecting the top K neighbors of each vertex using the node attribute information contained [10; 13];
* Optimize a unified objective function [24; 32]; and
* Leverage the well-known Swarm Intelligence methods such as BA (Bat Algorithm) [14; 17], FA (Firefly Algorithm) [14; 16] and PeSOA (Penguins Search Optimization) [15] to define bio-inspired metaheuristics schemes that use the evolution mechanisms to proceed to the detection of underlying communities.  
Those hybrid approaches have been pragmatically proven [10; 13; 15; 17; 24] to generate more qualitative community hierarchy structures to the classic approaches' outcome. However, they have shown strong limitations performance-wise, regarding the size of the networks that are capable of handling, and sensitivity regarding the processing type of the information, especially regarding the binary and the categorical data types [7].
## 3 Experimental Results and Discussion
### Basic Physical Parameters
#### 3.2.2 Effect of Two Cooling Methods on Static Compressive Strength  
The experimental results of the compressive strength of the two concretes in water cooling are shown in Tables 8 and 9.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Temperature/\({}^{\circ}\)C** & **Hight/mm** & **Mass/g** & **Strength/Mpa** \\ \hline  & 100.1 & 1089 & 32.5 \\
25 & 99.7 & 1097 & 31.0 \\  & 99.6 & 1084 & 30.9 \\ \hline  & 99.3 & 1015 & 28.7 \\
200 & 100.5 & 1026 & 33.5 \\  & 99.8 & 1087 & 29.6 \\ \hline  & 99.7 & 985 & 17.5 \\
400 & 100.2 & 1026 & 16.9 \\  & 100.4 & 972 & 26 \\ \hline  & 99.6 & 968 & 11.6 \\
600 & 99.3 & 925 & 9.8 \\  & 99.9 & 936 & 12.6 \\ \hline  & 100.5 & 826 & 3.5 \\
800 & 99.8 & 834 & 2.6 \\  & 100.1 & 857 & 3.9 \\ \hline \end{tabular}
\end{table}
Table 8: The static compressive strength of ordinary concrete in water cooling.  
Through the analysis of the experimental data, compared with air cooling in the same conditions, the strength of ordinary concrete declined by 19.4%, 22.2%, 25.5%, and 35% at 200, 400, 600, and 800 \({}^{\circ}\)C, respectively, while the strength of basalt fiber-reinforced concrete declined by 9.9%, 27.2%, 12.5%, and 35.5% at 200, 400, 600, and 800 \({}^{\circ}\)C, respectively. The static compressive strength of concrete after cooling in the air is greater than that after cooling in water.  
After the concrete subjected to high temperature was cooled by water, a temperature difference existed between the interior and exterior of the concrete, and the temperature difference was larger with the increase of the temperature. Therefore, the static compressive strength decreased rapidly after the high-temperature concrete was cooled by water. When the temperature reached 600 \({}^{\circ}\)C, the bound water inside the concrete began to precipitate, the concrete cooled by water underwent rehydration, and therefore the compressive strength was enhanced partly. However, due to further expansion of the temperature difference, the static compressive strength of the high-temperature concrete continued to decline.  
Compared with Figures 10 and 11, the failure patterns of the two concretes under water cooling are shown as follows:It can be seen from the contrast between Figures 12 and 13, the fracture of the two kinds of concrete specimens for the water-cooled is bigger than that for the air-cooled at the same heating temperature. This is attributed to the effect of water cooling, which results in the formation of a large number of severe cracks inside the specimen during rapid cooling.
## 6 Security Analysis
### Key Sensitivity Analysis  
In the proposed scheme, the most important private key is the system parameters and initial conditions. To analyze the sensitivity, the message signal \(H\) was transmitted with two close initial conditions \(\mathbf{u^{\prime}}_{1}(0)=(1,0,0,0,0)^{\mathrm{T}}\) in the senor node and \(\mathbf{u^{\prime}}_{2}(0)=(1,0,0,0,0.1)^{\mathrm{T}}\) at the sink node. The CMPS process is presented in Figure 14. The results demonstrate that the proposed algorithm has good sensitivity to the private key.  
Figure 13: Blood sugar signal transmission process.
# Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks
## 2 Related Works
### Scientific Publications
In [21] the authors develop an automated image-processing methodology to detect, count and estimate the size of citrus fruit on individual trees, using images captured from an unmanned aerial vehicle (UAV) from a commercial citrus grove. To carry out this, a Faster R-CNN Deep-Learning model was used. The obtained result shows an average standard error of 6.59% between visual counting and fruit detection by the model. In [22] the authors use Mask R-CNN, YOLOv2 and YOLOv3 for wine grape cluster detection. The dataset contains RGB images. The authors conclude that the Mask R-CNN network shows the best results as compared to the YOLO networks. In [23] a deep-learning approach for pixel-wise fruit detection and segmentation based on instance segmentation with the Mask R-CNN algorithm is presented using RGB and HSV images of the scene obtained from an orange grove under natural lighting conditions. The authors find that inclusion of HSV data improves the precision to 0.9753 from 0.8947, after use of RGB data, and obtained the overall F1 score of 0.89 using RGB + HSV. In [24], the authors combine visual processing color-opponent theory in humans with one-stage deep-learning networks to detect ripe soft fruit (strawberries) in orchards. Theyuse the RetinaNet algorithm with the ResNet-18 network as feature extractor, achieving a F1 score of 0.793 in controlled conditions and in the real-world dataset F1 score of 0.744. As is clear from the previously analyzed articles and the summary shown in Table 1, these recent studies applying object-detection methods to the study of fruit cover a very diverse area. Most of them use different versions of the YOLO algorithm and adaptations of it, because these applications need to detect objects quickly in videos, a task for which the YOLO algorithm has been shown to be very efficient. However, in certain cases, it is necessary to detect more accurately the objects in an image, in this case static RGB images, the other widely used algorithms are the Mask R-CNN that allows the segmentation of instances, and the Faster R-CNN algorithm. The proposal developed in this article is based on automating a manual process that takes up to 10 min to complete. As it is a process that does not need to be performed at high speed, it is proposed to use the Faster R-CNN object-detection algorithm to detect objects in real time, which has already been widely used [25; 26; 27; 28].
## 3 Results
### Controller Performance Evaluation Index  
There are two main indices for evaluating the performance of the dissolved oxygen controller. One is the assessment of the underlying control strategy. Indices include the integral of absolute error (_IAE_), the integral of squared error (_ISE_), the maximal deviation from set point (_Dev_\({}^{\max}\)) and the variance of error (_Var_\({}_{e}\)). The four indices are calculated by Equation (28) through to (31) as shown below  
\[IAE_{i}=\int\limits_{t=7}^{t=14}|e_{i}|dt \tag{28}\]  
\[ISE_{i}=\int\limits_{t=7}^{t=14}e_{i}^{2}dt \tag{29}\]  
\[Dev_{i}^{\max}=\max|e_{i}| \tag{30}\]  
\[Var(e_{i})=\frac{ISE_{i}}{T}-{(\frac{IAE_{i}}{T})}^{2} \tag{31}\]  
The aeration cost can be calculated using aeration energy (_AE_), which will be the economic indicator. _AE_ is mainly used in the last three units of the biochemical reaction tanks. _AE_ can be obtained by using the oxygen transfer function (\(K_{\text{L}}\)a) of the three units as shown in Equation (32)  
\[AE=\frac{S_{O}^{*}}{T\cdot 1800}\int\limits_{t=7}^{t=14}\sum\limits_{i=1}^{t=5}V _{i}\cdot K_{L}a(t)dt \tag{32}\]  
where, \(S_{O}^{*}\) is the saturation value of dissolved oxygen, \(V_{i}\) is the volume of each unit, and \(T\) is the calculation period of _AE_, in this case \(T\) = 7 days.  
Generally, the smaller the value of the above evaluation indices, the better the performance of the controller is. Results of the evaluation indices are shown in Tables 1 and 2. We can see that, under the different weather conditions, the RBFNNPID control strategy reduced the values of the above evaluation indices, compared with the traditional PID control strategy, indicating that the control performance of the system has been effectively improved, and the cost of aeration has also been reduced.