esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Experimental Campaign of Pozzuoli Solfatara
An evident CO\({}_{2}\) peak, in scans performed at an elevation angle of 6\({}^{\circ}\), was even clearer in the same fumarolic vent. This fact was also due to the reflection of the laser beam off the rockfaces enclosing the vent [21; 37; 38]. Figure 3b, instead, illustrates CO\({}_{2}\) flux time-series from the fumarolic system. Here, the cross-correlation analysis, applied to sequences of visible images of the moving plume that were co-acquired at a high rate (30 Hz) by a digital optical camera, was applied to achieve the vertical plume transport velocity. The overall error in the calculated plume velocity was in the range from 10 to 28%, leading to a total error of CO\({}_{2}\) fluxes from 15 to 33% [21]. Thanks to the high time resolution (around 10 min) of the system, it was possible to follow important intraday dynamics, nearly impossible to measure with conventional instruments for several reasons such as environmental difficulties, high costs, instrumentation ineffectiveness, and the larger amount of ambient air carbon dioxide that is present along the line of sight between the spectrometer, target (plume), and the source of photons (sun, magma, and lava fragments). The strong changes over timescales of tens of minutes are clear in the Pisciarelli hydrothermal site such as from a peak emission of 4.4 Kg/s (at 18:23 h local civil time on 15 October) down to 1.9 Kg/s after about 60 min. Apart from these fast variations, our data indicate a general constancy of CO\({}_{2}\) output over timescales of a few days. The mean carbon dioxide emissions for 15 and 16 October 2014 were quite similar (2.63 \(\pm\) 0.98 and 2.52 \(\pm\) 0.84 Kg/s), corresponding to the total daily outputs of 256 \(\pm\) 89 and 218 \(\pm\) 71 tons, in that order. Furthermore, the carbon dioxide fluxes measured by BILLI were in overall agreement with other estimations carried out with in situ data obtained with a portable gas analyzer [21]. Nevertheless, our system has the following advantages: (i) external light sources are not required; (ii) measurements can be performed without risk from a remote area; (iii) the spatio-temporal resolution is finer; and (iv) the morphology of the plume can be obtained in some minutes. Moreover, the big difference between the lidar echoes coming from the plume and ambient air allows the operator to disentangle the carbon dioxide of volcanic origin, and therefore to quantify the volcanic carbon dioxide flux. Such measurements are new in geophysics, and promise to contribute to volcanological research. Even though, at this experimental stage, a considerable number of further developments are needed (e.g., system miniaturization, automation, independent measurements of in-plume wind velocity, and long-distance remote sensing), the system performed the first measure of CO\({}_{2}\) concentration and flux in a volcanic plume [21].
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Camera Parameter Constraints
#### 2.1.1 Method Content  
[PERSON] et al. [12] learn weak-perspective camera (WPC) parameters from a given monocular image and project the predicted 3D pose into 2D space. Such projection loss  
Figure 1: Illustration of depth ambiguity of 3D HPE.  
can be used to update the position of the 3D joints. [PERSON] et al. [13] introduce the mixture density network (MDN) to generate multiple alternative 3D poses that mimic the depth ambiguity problem. Based on the WPC model, they compared the 2D re-projection of multiple 3D pose hypotheses to select the 3D pose with the highest similarity. The WPC model-based approach mentioned above may greatly simplify the imaging model, which can easily lead to the failure of some pose estimations. To address this issue, as shown in Figure 2, [PERSON] et al. [14] designed a camera distance-aware model to obtain the absolute depth from the camera to the target, allowing the RootNet to adapt to various camera parameters flexibly. The existing unsupervised framework, generative adversarial network (GAN) [15], has excellent generalization capabilities. [PERSON] et al. [16] exploit a GAN-based architecture to predict camera parameters from the input image and regard the predicted camera parameters as a weak-perspective projection matrix. The model performs weakly supervised learning by globally scaling the estimated 3D pose and re-projecting it into 2D space to determine the deviation of the predicted pose from the original 2D input.
## 5 Results and Analysis
### Development of Landslide Susceptibility Maps  
After training the SVM, BLR, and ADTree machine learning models with the training dataset and validating them with the validation dataset, we ran the models and obtained outputs as weights (landslide susceptibility indexes, LSIs). LSIs were assigned to each pixel of the study area to construct the landslide susceptibility maps. There are a variety of classification methods in ArcGIS, including manual, equal interval, natural break, quantile, geometric interval, and standard deviation used [16; 35; 98; 99; 100]. We selected the most classification used methods to create the landslide susceptibility maps such as natural breaks, quintile, and geometric intervals for reclassifying the LSIs. In the natural breaks method, no jump is detected in the values [101]. However, the quintile and geometric interval methods essentially split the distribution of susceptibility values into equal divisions, with similar proportions of the total area attributed to each class [102].  
As selecting the method used to reclassify the LSIs depends on the LSI histogram [99; 103; 104], we prepare the histogram of the three mentioned methods based on the landslide pixel against susceptibility classes. A histogram is a better that most of landslide pixels have been placed in high susceptibility (HS) and very high susceptibility (VH) classes. Then, we chose the quintile classification method for the landslide susceptibility map derived using the SVM and ADTree models, and the geometrical interval method for the model based on the BLR (Figure 5). Each map has five susceptibility classes: very low susceptibility (VLS), low susceptibility (LS), moderate susceptibility (MS), high susceptibility (HS), and very high susceptibility (VHS) (Figure 6).  
Figure 5: Histograms used to prepare the landslide susceptibility maps for three classification methods (natural break, quantile, and geometrical interval): (**a**) SVM, (**b**) BLR, (**c**) ADTree.
# Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
## 4 Experimental Results
### Numerous Results  
#### 4.2.1 Baseline Results  
For an input picture of size \(C_{in}\times H\times W\), if we assume that the output of convolution is \(C_{out}\) and the kernel size is \(D_{H}\times D_{W}\), then it has \(C_{in}\times D_{H}\times D_{W}\times C_{out}+C_{out}\) parameters. In the dynamic convolution kernel, it is composed of dynamic attention and dynamic convolution. If the input and output of the first fully connected layer are \(\widehat{C}\) and \(C_{out1}\), the output of the second fully connected layer is \(k\), and the output of pooling is \(\lambda^{2}\times H\times W\). Therefore, the attention needs \(\widehat{C}\times C_{out1}+C_{out1}\times k+C_{out1}+k\) parameters and the parameters of dynamic convolution are \(k\times C_{in}\times D_{H}\times D_{W}\times C_{out}+k\times C_{out}\). Similarly, for the FLOPs of the network, the FLOPs required by each attention is \(O(\alpha(x))=\lambda^{2}\times H\times W\times\widehat{C}+2\times\widehat{C} \times C_{out1}+2\times C_{out1}\times k\) and the FLOPs of dynamic convolution is \(O(f^{\prime}(x))=(2\times k\times C_{in}\times D_{H}\times D_{W}+k-1)\times H\times W\). For DyNet, when solving attention parameters and FLOPs, \(\widehat{C}=C_{in}\). As the network depth increases, the value of \(\widehat{C}\) is larger. However, for EAM-DyNet, the size of \(\widehat{C}\) is always equal to k. As the network gets deeper, the value of \(\widehat{C}\) of DyNet is much larger than k, so the parameters and FLOPs are also higher than EAM-DyNet. For example, EAM-DyNet's parameters and FLOPs were reduced by 1.38 M and 6.15 M, respectively, relative to DyNet on MobilenetV1. OnMobilenetV2, the parameters and FLOPs of EAM-DyNet were reduced by 2.27 M and 8.92 M relative to DyNet. However, the accuracy of EAM-DyNet was increased by 0.43% and 0.56% compared to DyNet on two baselines. The results of the top-1 accuracy on the baseline are shown in Table 1, in addition to comparing the parameters and FLOPs of DyNet MobilenetV1 \(\times\) 1.0, EAM-DyNet MobilenetV1 \(\times\) 1.0, DyNet MobilenetV2 \(\times\) 1.0, and EAM-DyNet MobilenetV2 \(\times\) 1.0. The comparison results are shown in Table 2. From the experimental results, it was found that the EAM-DyNet reduced the number of network parameters and the FLOPs value compared to the DyNet. In addition, the accuracy of EAM-DyNet was lower than that of DyNet. Among them, the CGR method was better on the CIFAR dataset, and the RCR method on the ImageNet100 dataset was better. For MobilenetV1 \(\times\) 1.0, the accuracy of the CIFAR dataset can be increased by 0.45% and the accuracy of the Imagenet dataset could be the largest increase by 0.43%. For MobilenetV2 \(\times\) 1.0, it greatly reduced the number of network parameters and increased the characterization ability of the network.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments
### ML Models
#### 2.4.3 Artificial Neural Network  
The ANN is the most commonly used ML tool. It uses a large number of connected neurons and simulates the learning process of a biological brain. ANN architecture achieves powerful representation abilities and can approximate complex nonlinear functions. The network is comprised of three main layer types: input layer, hidden layer(s), and output layer. To obtain the best approximation, the large number of parameters in the neurons are trained with the input data through several iterations with the objective of minimizing the defined loss function. The workflow in the ANN consists of forward pass and backward propagation. In this study, the forward network architecture [44] was applied with the vector of damage features, \(x\), as the input layer and two hidden layers with \(n\) neurons. The output variable \(y\) was stacked in a matrix after 1-hot encoding according to the four damage states. The input vector was multiplied by the weight vector \(w\) to obtain the output \(z\) of layer-1 as follows,  
\[z=w^{T}x+b, \tag{15}\]  
where \(b\) is a bias term and \(T\) represents transpose. Subsequently, \(z\) is used as the input to the activation function \(\sigma(.)\) to produce the predicted outputs. In this study, the _sigmoid function_ (Equation (6)) was used as the activation function. After the predicted outputs were computed, the total loss of the ANN can be computed based on the defined loss function \(L(.)\) (Equation (8)). The results of two different ANN models were reported in this study, namely ANN\({}_{10}\) and ANN\({}_{100}\) with \(n=10\) and 100 neurons, respectively.
## 2 Modelling of SMIB with AVR and PSS Damping Controllers  
A dynamic mathematical modelling of the SMIB system with an exciter and PSS is required to extract its small signal stability performance. PSS extends the angle stability by modulating the exciter output signal to damp low frequency oscillations. The linearised model of the SMIB system and AVR controller is shown in Figure 1.  
As shown in Figure 1, the dynamic equations for the machine equipped with an exciter, representing the SMIB, can be derived with expressions (1)-(6) as follows:  
\[\Delta\dot{\omega}_{l}=-\frac{K_{D}}{2H}\Delta\omega_{r}-\frac{K_{1}}{2H} \Delta\delta-\frac{K_{2}}{2H}\Delta\psi_{fd}-\frac{K_{21}}{2H}\Delta\psi_{1d} -\frac{K_{22}}{2H}\Delta\psi_{1q}-\frac{K_{23}}{2H}\Delta\psi_{2q}+\frac{1}{2 H}\Delta T_{m}, \tag{1}\]  
\[\Delta\dot{\delta}= 2\pi f_{o}\Delta\omega_{r}, \tag{2}\]  
\[\Delta\dot{\psi}_{fd}=\text{A32}\Delta\delta+\text{A33}\Delta\psi_{fd}+\text{ A34}\Delta\psi_{1d}+\text{A35}\Delta\psi_{1q}+\text{A36}\Delta\psi_{2q}+\text{b32} \Delta\psi_{fd}, \tag{3}\]  
Figure 1: Block diagram of the SMIB with an exciter. \[\Delta\dot{\psi}_{1d} =A42\Delta\delta+A43\Delta\psi_{fd}+A44\Delta\psi_{1d}+A45\Delta\psi_{1q }+A46\Delta\psi_{2q}, \tag{4}\] \[\Delta\dot{\psi}_{1q} =A52\Delta\delta+A53\Delta\psi_{fd}+A54\Delta\psi_{1d}+A55\Delta \psi_{1q}+A56\Delta\psi_{2q},\] (5) \[\Delta\dot{\psi}_{2q} =A62\Delta\delta+A63\Delta\psi_{fd}+A64\Delta\psi_{1d}+A65\Delta \psi_{1q}+A66\Delta\psi_{2q}, \tag{6}\]  
where \(H\) refers to the constant of inertia; \(\delta\) and \(\omega\) express the rotor angle and speed, respectively; \(\psi_{fd}\) denotes the machine field flux; \(\psi_{1d}\) refers to the machine flux of the \(d\)-axis shock absorber; \(\psi_{1q}\) refers to the flux of the \(q\)-axis 1 st shock absorber; and \(\psi_{2q}\) refers to flux of the \(q\)-axis 2 nd shock absorber; \(K_{D}\) refers to the damping torque coefficient; \(K_{1}\) and \(K_{2}\) express the synchronising torque component and torque component resulting from variation in the main field flux linkage, respectively; \(K_{21}\), \(K_{22}\) and \(K_{23}\) express the torque components resulting from the variation in the flux linkage of the \(d\)-axis shock absorber, flux linkage of the \(q\)-axis 1 st shock absorber and flux linkage of the \(q\)-axis 2 nd shock absorber, respectively. The PSS lead lag controller structure contains a washout block with time constant \(T_{W}\) to reduce exaggeration of damping during critical circumstances, as shown in Figure 2. Constants \(T_{1}\) and \(T_{2}\) are used for phase compensation and gain constant \(K_{stb}\) to provide the required damping over the range of frequencies of low frequency oscillations. PSS gives a supplementary signal to the AVR that produce electrical torque in the direction of the speed deviation of the rotor. Phase compensation supports a phase offset in the phase lag between the exciter input and the electrical output. The input signal to PSS is the rotor speed, electrical power generated in the machine or a combination of these.
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 2 Introduction to Blockchain
### The Classification of Blockchain  
Blockchain functions as a distributed shared ledger or database, and possesses key characteristics such as decentralization, transparency, and immutability, which ensure its trustworthiness and reliability. Currently, based on its permission scope and access control, blockchain can be classified into public chains [13], private chains [14], and consortium chains [15]. Each type of blockchain has its unique features. Public chains, with their high degree of decentralization and transparency, are widely applied in scenarios such as cryptocurrencies. Private chains, with their lower degree of decentralization and enhanced privacy, provide efficient management and data protection across various sectors. Consortium chains, combining the characteristics of both public and private chains, strike a balance between multi-party collaboration and partial decentralization. They offer significant advantages in traceability and auditability, making it easy to track the source and historical records of transactions [16]. A comparison of the features of different types of blockchains is shown in Table 1 below.  
As shown in Table 1, public blockchains typically rely on proof-based consensus mechanisms and are fully decentralized in terms of governance without the need for third-party institutions to intervene. For example, Ethereum and Bitcoin are typical public blockchain platforms that are primarily utilized in the financial sector. In contrast, private blockchains focus more on privacy and security and often use more robust consensus algorithms. Among them, the Raft mechanism is a common choice, which ensures data consistency and security by selecting a leader node to coordinate the transaction operations of other nodes. Private blockchains are primarily used in internal networks within enterprises or departmental networks and operate under a fully centralized governance structure [17].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Consensus Algorithm** & **Degree of Decentralization** & **Representative Applications** \\ \hline Public Blockchain & PoW, PoS, DPoS & Fully Decentralized & BTC, ETH \\ \hline Private Blockchain & Paxos, Raft & Centralized & Viewstamped, Replication (VR) \\ \hline Consortium Blockchain & PBFT, BFT, Kafka & Partially Decentralized & Hyperledger Fabric, FISCO-BCOS. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of features across different types of blockchains.  
Figure 2: Block structure diagram.  
Representative application platforms include Viewstamped Replication (VR). Consortium blockchains, on the other hand, feature a partially decentralized structure. They integrate the transparency and decentralization characteristics of public blockchains with the security and data consistency present in private blockchains. Common consensus algorithms for consortium blockchains include PBFT, BFT, and Kafka. Popular open-source platforms used in consortium blockchains include Hyperledger Fabric and FISCO-BCOS.
## Energy-QoS Trade-Offs in Mobile Service Selection  
**[PERSON] * and [PERSON] ***  
Intelligent Systems & Networks Group, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2 BT, UK  
* Authors to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS] (E.G.); [EMAIL_ADDRESS] (R.L.); Tel.: +44-207-594-6173 (R.L.); Fax: +44-207-594-6274 (R.L.).  
_Received: 28 February 2013; in revised form: 5 March 2013 / Accepted: 12 April 2013 / Published: 19 April 2013_  
**Abstract:** An attractive advantage of mobile networks is that their users can gain easy access to different services. In some cases, equivalent services could be fulfilled by different providers, which brings the question of how to rationally select the best provider among all possibilities. In this paper, we investigate an answer to this question from both quality-of-service (QoS) and energy perspectives by formulating an optimisation problem. We illustrate the theoretical results with examples from experimental measurements of the resulting energy and performance.  
**Keywords:** system load; measurements; QoS-energy trade-offs; optimum distribution
## 1 Introduction
[19], is another popular method that ranks features according to their ability to preserve the multi-cluster structure. Both LSFS and MCFS are often considered baseline methods. [PERSON] et al. [33] devised a feature selection technique that considers the variability score of the feature to measure the feature's importance. Recently, [PERSON] et al. [17] presented a maximally non-redundant feature selection (MNFS) method in which features and their pairwise mutual information compression index (MICI [27]) values are used as the nodes and corresponding edge weights of a graph. The densest subgraph of the constructed graph is identified, and the corresponding features are selected as the maximally non-redundant subset. [PERSON] et al. [31] and [PERSON] et al. [38] presented an unsupervised feature selection method that returns the appropriate size of the feature subset. [PERSON] et al. [32] employed a floating forward-backward search on the densest subgraph. Similarly, [PERSON] et al. [12] proposed a variation of MNFS by clustering around the features of the densest sub-graph and termed it as the densest subgraph finding with the feature clustering (DSFFC) method. However, by providing a minimum number of features, there is a possibility of adding irrelevant features to the selected feature set. Therefore, the paper presents a novel unsupervised feature selection method. The proposed method consists of two phases; the first phase is the formation of the densest feature subgraph using mutual information for feature selection and the second phase involves dynamic clustering of features using shared nearest neighbors as the criteria. The cluster representatives that are mutually exclusive to the feature subgraph are added to the selected set of features. To experimentally evaluate the proposed method, five standard UCI datasets have been considered and compared against five existing feature selection methods in terms of two performance parameters, namely ACC and MCC. The organization of the remaining paper is as follows. In Section 2, related work is briefed along with the preliminary concepts. The proposed method is discussed in Section 3. Section 4 presents the experimental setup followed by the performance analysis of the proposed method in Section 5. Finally, the conclusion is drawn in Section 6.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Dataset Preprocessing and Augmentation
#### 3.2.2. Dataset Preprocessing  
Data preprocessing is a key step to ensure data quality and model performance. Initially, data cleaning involves removing or correcting erroneous and inconsistent records in the dataset. This is to ensure the accuracy of model training and evaluation, preventingincorrect data from leading the model to learn incorrect patterns. Following this, normalization or standardization processes involve transforming all features to the same scale for more effective learning and generalization by the model. Common methods include min-max normalization and \(Z\)-score standardization. The mathematical expression for \(Z\)-score standardization is as follows:  
\[x^{\prime}=\frac{x-\mu}{\sigma} \tag{7}\]  
Here, \(x\) represents the original feature value, \(\mu\) and \(\sigma\) represent the mean and standard deviations of the feature values, and \(x^{\prime}\) is the standardized feature value.  
Handling missing values is also a crucial aspect of data preprocessing. For missing data, various methods can be employed, such as interpolation, filling with mean values, or simply deleting related records. Finally, feature engineering involves selecting and constructing features according to the needs of the model. In financial data analysis, especially for stock price prediction, calculating moving averages or other technical indicators as input features for the model might be necessary.