esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mixed Method with Effective Color Reduction
## 5 Conclusions  
BS+ATCQ is a recently proposed color quantization method which combines two previous methods that solve the same problem: BS and ATCQ. BS is a well-known color quantization technique. Moreover, ATCQ is a recent method that represents the pixels of the image by ants that build a tree structure. This article proposes to replace ATCQ by ITATCQ, generating another color quantization method called BS+ITATCQ. In essence, ITATCQ applies ATCQ operations iteratively, improving the resulting image as the iterations progress. When ITATCQ was proposed, it was shown that the said algorithm could obtain better images than ATCQ. However, now this work has shown that the method that combines BS with ITATCQ obtains better results than BS+ATCQ. Computational results show that the new method generates better images than BS and ITATCQ applied independently and also improves the images generated by BS+ATCQ. In addition, the method was compared to 11 other color quantization techniques and obtained good results. BS+ITATCQ obtains better images than the splitting methods compared, although it consumes more time, as is usually the case when comparing the execution time of splitting methods and clustering-based methods. On the other hand, it can obtain better images than most of the clustering-based methods analyzed and with less time consumption. The following are available online at [[http://www.mdpi.com/2076-3417/10/21/7819/s1](http://www.mdpi.com/2076-3417/10/21/7819/s1)]([http://www.mdpi.com/2076-3417/10/21/7819/s1](http://www.mdpi.com/2076-3417/10/21/7819/s1)), Table S1: Average execution time (milliseconds) of other color quantization methods--palette size 32, Table S2: Average execution time (milliseconds) of other color quantization methods--palette size 64, Table S3: Average execution time (milliseconds) of other color quantization methods--palette size 128, Table S4: Average execution time (milliseconds) of other color quantization methods--palette size 256. This research was funded by the Samuel Solorzano Barruso Memorial Foundation of the University of Salamanca grant number FS/102015. The author declares no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} ABC & Artificial bee colony \\ ATCQ & Ant-tree for color quantization \\ BS & Binary splitting \\ BSKM & K-means applied to the result of Binary splitting \\ FA & Firefly algorithm \\ ITATCQ & Iterative ant-tree for color quantization \\ KM & K-means \\ MC & Median-cut \\ MSE & Mean squared error \\ NQ & Neuquant \\ OC & Octree \\ SFLA & Shuffled-frog leaping algorithm \\ VB & Variance-based \\ WATCQ & [PERSON]'s method combined with ant-tree for color quantization \\ [PERSON] & [PERSON]'s method \\ \end{tabular}   
_Appl. Sci._**2020**, _10_, 7819
## 3 Results and Discussions
### Monitored Data  
Table 3 shows the average temperatures measured on the ceiling of each room sited below the green roof's six plots, for summer (July) and winter (February) conditions, in periods during which the air-conditioning system was working. The monitoring results point out a general tendency to attain lower temperatures when the green coverage is higher, i.e., P1 (_Halimione Portalucoides_). Indeed, this plot shows that ceiling temperatures were generally 1-3 \({}^{\circ}\)C lower with respect to the other plots in summer and 1-2 \({}^{\circ}\)C higher during winter, hence representing a benefit for both the summer and winter seasons. Moreover, it must be noted that the LAI has a positive influence on the green-roof thermal behaviour; P1 and P6 have, in fact, higher LAI, unlike P3 and P4. In addition, another factor that could have influenced the obtained results is represented by the light colour of the plants' leaf surface, which enabless a higher amount of solar radiation to be reflected. The results shown in Table 3 also highlight the influence of the different type of plants. In particular, _Halimione Portalucoides_ (P1 and P6) reduces temperature peaks more consistently. Therefore, this type of plant seems to be more suitable for lowering the summer temperature values and increasing the winter temperature peaks. Anyway, as reported in Table 3, it should be pointed out that during the summer season, a mean temperature of about 26.6 \({}^{\circ}\)C has been recorded by the thermocouples placed on the rooms' ceilings under P1 (_Halimione Portalucoides_), with maximum peaks of 27 \({}^{\circ}\)C, that lies within the suggested range for the indoor comfort in summer conditions [72]. The same cannot be stated for the other plots, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plots**} & \multirow{2}{*}{**Plant Species**} & **Water Storage Layer** & **Maximum Green** & \multicolumn{2}{c}{**T\({}_{\text{avg}}\) (\({}^{\circ}\)C) of the Ceiling**} \\ \cline{3-5}  & & **Thickness (cm)** & **Coverage (\%)** & **February** & **July** \\ \hline P1 & _Halimione Portalucoides_ & 10 & 100\% & \(18.1\pm 2.1\) & \(26.6\pm 0.2\) \\ P2 & _Rosmarinus Officialis_ & 10 & 85\% & \(16.0\pm 1.8\) & \(27.5\pm 0.9\) \\  & _Prostratus_ & 10 & 58\% & \(15.8\pm 1.4\) & \(26.9\pm 1.1\) \\ P3 & _Crichum Martitimum_ & 15 & 38\% & \(17.9\pm 1.2\) & \(30.8\pm 0.8\) \\ P4 & _Rosmarinus Officialis_ & 15 & 100\% & \(17.6\pm 1.2\) & \(28.5\pm 0.6\) \\  & _Prostratus_ & 15 & 100\% & \(19.0\pm 1.1\) & \(28.2\pm 0.3\) \\ P6 & _Halimione Portalucoides_ & 15 & 100\% & \(19.0\pm 1.1\) & \(28.2\pm 0.3\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Monitoring results of the green roofs six plots. where, even with a 100% green coverage, ceiling temperatures of about 27-28 \({}^{\circ}\)C were registered, with maximum peaks going beyond 30 \({}^{\circ}\)C.  
The above-discussed outcomes demonstrate the importance of selecting a proper plant species during the green roof design phase.
## 3 Proposed Methodology  
The framework proposed in this study comprises a handcrafted bag-of-features (BoF) feature extraction model, which is followed by a deep neural network (DNN) classification model. The BoF model is cascaded to extract features, which are then further refined and classified using the proposed DNN model. To pre-process the data, we first identify repeated keywords and remove artifacts, such as articles, prepositions, adverbs, and helping verbs. The performance of the proposed model was evaluated using a dataset of 3512 reviews, collected and labeled based on customer ratings posted in the review section of an e-commerce website. The performance metrics of the proposed model are presented in the Results section. Figure 1 provides a comprehensive overview of the detailed architecture of the proposed framework, while Figure 2 presents the layered details of the DNN model. The BoF clusters the keywords in \(k\) different sets to generate a vocabulary of features through k-means algorithm. The keywords are categorized into \(k\) classes on the basis of their similarity measures. The iterative process is adopted to optimize the clusters given  
Figure 1: Framework of the proposed model. Figure 2: Proposed DNN model. in Equations (1) and (2). The total of \(k\) cluster centroids are initially selected randomly, represented with \(\mu_{j}\), with \(j\) ranging from 1 to \(k\). \[\mu_{j}=\frac{\sum_{i=1}^{m}(\chi_{j}^{i})}{m} \tag{1}\]  
\[\mu_{j}=\arg min_{u}||\chi^{i}-\mu_{j}||^{2} \tag{2}\]  
where \(\chi_{j}^{i}\) denotes the \(i_{\text{th}}\) keywords belonging to cluster \(j\), while the \(\mu_{j}\) denotes the centroid of cluster \(j\). The \(\chi^{i}\) in Equation (1) denotes the \(i_{\text{th}}\) keyword belonging to cluster \(j\). The symbol \(m\) in the denominator of Equation (1) denotes the cardinality of cluster \(j\). The difference between successive centroids converges at \(i_{\text{th}}\) and \(i-1\) iteration iteratively. The cluster centroid collectively represents the vocabulary set. The histogram of the vocabulary centroid in the raw feature data represents the final feature vector. \[\zeta_{v}=\left\{ \begin{array}{ll}P+0,&\text{if}\quad\psi(\chi^{n},\mu_{j}) \geq\rho\\ P+1,&\text{otherwise}\end{array} \right. \tag{3}\]  
where \(\zeta_{v}\) denotes \(v_{\text{th}}\) feature value of the histogram feature vector. The \(\chi^{n}\) denotes the \(n_{\text{th}}\) keywords of the review data. The function \(\psi\) calculates the distance of the \(m_{\text{th}}\) keywords and the \(n_{\text{th}}\) centroid. The symbol \(\rho\) in Equation (3) denotes the minimum difference value of the \(v_{\text{th}}\) centroid and the keywords. The final BoF feature \(\zeta\) is then fed to the proposed DNN for classification. The DNN model initial layer consists of 64 hidden neurons, which convolve with the input features vector \(F\). The \(\text{C}_{k}^{1}\) in Equation (4) represents the \(k_{\text{th}}\) hidden convoluional kernel, while the \(b_{k}\) denotes the bias vector of the respective \(k_{\text{th}}\) kernel.
## 3 Experimental Design
### Experimental Procedure  
The robotic arm is maneuvered so that its end effector is parallel to the fuel tank's receiving port, with the receiving interface positioned at the center of the image. This point is assumed to be the origin of the robotic arm for the experiment. The process from capturing the target position with the camera to the end effector reaching the target point constitutes one complete docking operation. The target recognition and docking process are shown in Figure 7. The following experiments are conducted:  
* a1: Target detection is performed using the OpenCV (version 4.5.2.54) method. The success of the target recognition is judged. If successful, proceed to a2; if the target is not recognized, the experiment is considered a failure.
* a2: If a1 successfully recognizes the target, pose estimation is performed on the target's center point. The robotic arm moves the end effector to the target position for docking. The experiment is considered successful if docking is completed smoothly; otherwise, it is a failure.
* a3: The position and posture of the simulated fuel tank are changed. Under the condition of successful a1, experiment a2 is conducted until docking fails, determining the successful docking range.  
Figure 5: Visual guidance docking platform.  
Figure 6: Experimental procedure diagram.  
* a4: The position and posture of the simulated fuel tank are changed, and experiment a1 is repeated until failure, establishing the applicable range for target recognition.
* a5: The above experiments based on the OpenCV method are replaced with the YOLOv8-based method, and experiments a1 to a4 are conducted accordingly.
# Spatialization and Analysis of China's GDP Based on NPP/VIIRS Data from 2013 to 2023
## 5 Discussion  
### Comparative Analysis Results of GDP  
#### 5.1.1 Overall Accuracy Evaluation  
The official GDP data are derived from economic statistical methods, which cannot show the internal differences of statistical units and do not involve rich spatial information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\beta\) & \(Z\) & **GDP Trends** & **Pixel Ratio/\%** \\ \hline \(\leq\)\(-\)0.01 & \(\leq\)\(-\)1.96 & Markedly decrease & 0.27 \\ \(\leq\)\(-\)0.01 & \(-\)1.96–1.96 & Slightly decrease & 0.72 \\ \(-\)0.01-0.01 & \(-\)1.96–1.96 & Unchanged & 0.46 \\ \(>\)0.01 & \(-\)1.96–1.96 & Slightly increase & 2.99 \\ \(>\)0.01 & \(>\)1.96 & Significantly increase & 95.57 \\ \hline \hline \end{tabular}
\end{table}
Table 4: GDP trend division and pixel statistics.  
Figure 5: The pixel-level spatialized density maps of GDP.  
The GDP spatial data calculated by NTL data can directly reflect the distribution of the GDP, and the calculation is simple and the data are objective. Overall, there are differences between the official GDP of the government and the GDP obtained by NTL. The specific situation is shown in Table 5. It can be seen that the fitting values using light data in 2013, 2015, 2018, 2021, and 2023 are slightly larger than the official GDP values. There are differences in the data, with an RE of 0.04% to 1.95%. The data fitting is good, especially in 2023, with an RE of only 0.04%.
# Connectivity Analysis in VANETs with Dynamic Ranges
## 1 Introduction  
The advent of Vehicular Ad Hoc Networks (VANETs) has ushered in a new era of intelligent transportation systems, promising safer and more efficient roadways through real-time inter-vehicle communication. This technology is poised to revolutionize the automotive industry by enabling vehicles to share critical information, such as traffic updates, collision warnings, and emergency alerts, within the confines of limited communication ranges. Yet, as with any promising technology, VANETs confront formidable challenges that merit rigorous investigation.  
Foremost among these challenges is the ever-shifting landscape of VANET network topology. The nature of vehicular networks, characterized by rapidly moving nodes and dynamic formations, engenders a highly variable communication environment. This dynamism, while inherent to VANETs, introduces a significant obstacle to sustaining reliable communication links. The intermittent disruptions in connectivity threaten to compromise the very safety and efficiency that VANETs are designed to enhance.
## 3 Results and Discussion
### Acidity of the Analyzed juices and Tea Infusions
As a result of adding juices and citric acid solutions to the infusions, a decrease in the pH value was observed. The most minor percentage decrease, ranging from 2.2% to 7%, was observed in infusions with quince juice and the corresponding citric acid solution. This is related to it having the lowest titratable acidity. More significant pH drops ranging from 28.7% to 39.8% and 19.6% to 32.6% were observed for adding lemon juice and Japanese quince, respectively, along with the corresponding citric acid solutions. The lemon and Japanese quince juices used in the research were characterized by similar titratable acidity; therefore, their effect on lowering the pH of the infusions is also similar. Reducing the pH value after adding additives with low pH and high titratable acidity is natural and is confirmed in the literature. However, a comparison of the obtained results with the literature data does not lead to any conclusions because the additives used by other authors had different forms (solutions of citric, nitric, and ascorbic acids of various, often unknown concentrations; lemon juice of unknown acidity) and were added in various amounts [21, 25, 39, 42].
# A Reinforcement Learning Algorithm for Automated Detection of Skin Lesions
###### Abstract
The location and shape to get the fine-grained segmentation is established by using the continuous action parameters set. Figure 1: The skin sample lesion images taken from the ISIC 2017 dataset, which have different class differences within due to the various variations, such as severe (**a,d,l**), blurred boundary (**b,d–f,h,l**), inhomogeneity (**b,i,f,h,k**), hair appearance (**g,c**), poor contrast (**h,j**). The difficulties mentioned above are present in the skin lesion datasets. These datasets are used for the automated segmentation process. Deep Q-Networks (DQN) [24] is one of the most often utilized DRL algorithms. However, it is only restricted to solving the discrete action space problems. We use the Deep Deterministic Policy Gradients (DDPG) [25] algorithm for solving the continuous action space. DDPG is a learning method that simultaneously learns a policy and Q-function. Before employing the Q-function to learn the policy, it uses the off-policy data and Bellman's equation. To the best of our knowledge, this is the first attempt to represent and solve the skin lesion image segmentation problem as an MDP using DDPG. Since the DDPG is heavily dependent on searching the correct hyperparameters for the current task, we choose the action bundle, a suitable hyperparameter for the algorithm, thus increasing its stability. The following are the main contributions of this work:  
* The skin lesion image segmentation is proposed as an MDP. It is solved with the DDPG algorithm, similar to how the physicians delineate the lesion image ROIs. * The proposed skin image segmentation executor is based on the quadratic Bezier curve (QBC) and uses the action bundle as a hyperparameter to further improve the _Acc_ of the segmentation process. * We use a modified experience replay memory (ERM) to train the segmentation agent efficiently. The ERM helps in efficiently utilizing the previous experiences by learning multiple times. Figure 2: Segmentation masks generated from our RL algorithm on the PH2 dataset. * We perform a quantitative statistical analysis of our skin lesion segmentation results to show the reliability of our segmentation method and compare our results to the current state-of-the-art approaches. The structure of the article is as follows: Section 2 describes the current state-of-the-art methods, Section 3 presents our proposed RL method, overview, and the details of the experimental setup; Section 4 presents the results and discussion of our method. Finally, we conclude our article in Section 5.
# Mitigation against DDoS Attacks on an IoT-Based Production Line Using Machine Learning
## 2 Materials and Methods
### DDoS Attacks  
The distributed architecture of IoT networks is their vulnerability. Any IoT device located in the environment is a possible point of failure and can be exploited for cyberattacks, including DDoS attacks [18].  
Attacks on IoT devices are growing more quickly than the number of connected devices. Compromised devices usually become part of a botnet, which can then be exploited to trigger a DDoS or another type of attack. The main threat to the network and network services is harmful traffic from IoT devices that are part of botnets [19]. To perform a DDoS attack, the IoT device does not even have to be compromised and can become part of a DDoS attack.  
In general, the role of a distributed denial of service attack is to perform a coordinated attack that affects the availability of the victim's services. Many distributed nodes are involved in carrying out an attack, and the target network or machine is flooded with a number of requests, which reduces the performance of the system or the entire network. Usually, a DDoS attack exploits protocols such as UDP, TCP, domain name system (DNS), or ICMP and various types of DDoS attacks such as SYN flood, ICMP flood, or HTTP Get Flood are based on this [20].  
Distributed reflective denial of service (DRDoS) is one type of DDoS attack. To overwhelm a victim machine or network, DRDoS and DDoS attacks use a flood of connection requests that run from multiple IP addresses every second. Conversely, DRDoS attacks do not start attacks from compromised computers, but instead often use legitimate IoT devices to \"reflect\" or enhance traffic to the victim using modified packets with spoofed IP addresses. An attack is reflective when the attacker, to hide their identity, makes use of a potentially legitimate third party to resend attack traffic to the victim [21].  
The subject of the tested network infrastructure is the addition of IoT devices to the production line; IoT devices have made the production line more vulnerable to DDoS attacks. Significant problems in the production process were caused by direct DDoS attacks as well as DRDoS attacks, which were conducted through IoT devices directly on the components of the production line and thus disrupted the production process.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### SMS of Telecom Customers Factor Analysis
#### 5.3.2 Research Hypothesis Testing: KMO and Bartlett Tests of Sphericity  
The test results of KMO and Bartlett for SMS data are shown in Table 13. The KMO measures of sampling adequacy were 0.567 > 0.5, and the value of Sig was 0.000 < 0.05. It was concluded that the data were suitable for factor analysis.
## 2 Background
### IoV Security Challenges  
The IoV architecture is a complex framework designed to facilitate seamless communication and interaction among vehicles, infrastructure, and various other entities. It comprises several layers, including perception, network, and application layers, each serving distinct functions to ensure efficient data collection, transmission, and processing. The perception layer is responsible for gathering data from sensors and devices, while the network layer handles data communication through technologies like 5G and edge computing. The application layer processes this data to provide services such as traffic management, navigation, and infotainment [12].  
Although IoV has great potential, its design leaves a lot of security flaws. The great interconnectedness and data flow across many companies have, as a result, crucial effects,including their vulnerability to cyberattacks. These attacks can show up as malware infections and data breaches, as well as more sophisticated ones, including DoS and man-in-the-middle attacks. Guaranteeing the availability, confidentiality, and integrity of data will help to maintain the dependability and credibility of IoV systems.  
Managing the large volumes of data generated by the IoV in a safe way presents still another challenge. User identities and automobile locations are very important, so it is necessary to protect this data from unauthorized access and keep users' anonymity. Furthermore, IoV networks' dynamic and distributed character makes it more difficult to create robust security policies and quickly identify and deal with security concerns [13].  
Furthermore, adding to interoperability and compatibility difficulties are the great variety of IoV components, which include several automotive models, communication protocols, and sensor types. These issues might create security flaws that hackers could find useful. Establishing consistent security measures and guaranteeing broad adoption can help one to properly handle these challenges [14]. Table 1 summarizes those challenges.
## 3 Methodology
### The Proposed ConvResBiGRU-SE Model
#### 3.3.1 Convolutional Block  
When employing CNNs, a particular array of components is generally utilized. CNNs are frequently employed in learning under supervision, wherein each neuron is interconnected with every neuron in the following layer. The value entered by the neurons is transformed into an output value via the activation function of the neural network. The  
Figure 4: Comprehensive and expanded architecture of the proposed hybrid deep residual model.  
Figure 3: Fixed-length sliding window method employed in this study.  
efficiency of this function is dictated by two principal factors: sparsity and its capacity to regulate the diminished gradient flow to the network's layers below.  
CNNs often utilize pooling for dimensionality reduction, implementing both maximum and average pooling functions, referred to as max-pooling and average-pooling, respectively.  
This study employs a convolutional block (ConvB) to derive low-level features from unprocessed sensor data. The ConvB consists of four layers: one-dimensional convolutional (Conv1D), batch normalization (BN), exponential linear unit (ELU), and max-pooling (MP) layers, as illustrated in Figure 4. The Conv1D layer employs several trainable convolutional kernels to extract diverse features, with each kernel generating a distinct feature map. The BN layer enhances stability and accelerates the training operation. The ELU activation function enhances the model's representational capacity by introducing non-linearity while avoiding vanishing gradient issues. The MP layer reduces the spatial dimensions of the feature maps by retaining the most salient features within each pooling region.