esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Visual SLAM Method with Adaptive Feature Extraction
## 4 Experiment
### Feature Extraction  
With the aim of verifying the effectiveness of our suggested method, we compare it with the original ORB feature extraction algorithm and randomly select images from the three datasets of KITTI, TUM, and EuRoC for testing. From each image, we extract 1000 feature points. Figure 6 depicts the feature extraction results. The ORB algorithm extracts features with a large number of aggregations, as can be observed intuitively. This is because it selects feature points according to the response value, so the detected feature points will be clustered in areas of strong texture. The feature points are concentrated in  
Figure 5: Sample image of the dataset. The KITTI dataset is in the top row, followed by the TUM RGBD dataset in the second row, and the EuRoC dataset that was taken in the factory scene is in the final row.  
a small part of the area, which makes us unable to fully utilize the image information. In extreme situations, if all feature points are concentrated on the same point and applied to SLAM, there may be situations where the camera pose cannot be solved.  
In addition, the traditional ORB feature extraction algorithm uses a fixed threshold for feature extraction, which cannot adapt well to environmental characteristics. In this paper, a series of adaptive methods are proposed and combined with the quadtree algorithm and the AGAST algorithm for feature extraction. The obtained feature points are basically distributed equally throughout the whole image, as seen in the left column of the figure, which can provide high-quality feature points for SLAM.
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 4 Performance Evaluation
### Training Assessment Methodology and KPIs  
The training performance for both _TRL_ and _Non-TRL_ modes is assessed by evaluating the policies available every \(10^{3}\) steps during the training. Each evaluation consists of applying the policy during a simulation of 40 days. For each day, there is a different offered load pattern for each tenant, thus embracing a rich set of offered load conditions for both tenants. This allows evaluating the trained policies across a large range of states.  
The following KPIs are defined to quantify the performance of the model:  
* Average aggregated reward per evaluation (\(R\)): It is computed as the average of the aggregated reward of the two tenants throughout one evaluation.
* Standard deviation (\(std(m,W)\)): The standard deviation of \(R\) at the \(m\)-th training step measured over the window of the last W training steps.
* Training duration: Number of training steps until the convergence criterion is achieved. This criterion considers that convergence is achieved in the training step \(m\) that fulfills \(std(m,W)<std_{th}\), where \(std_{th}\) is a threshold.
# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 3 Theoretical Frameworks
### DNN-Based Wi-Fi Location Recognition
#### 3.2.3 Online Stage  
In online positioning, we only need to propagate the preprocessed real-time observation \(x^{L}=(RSS^{\prime}_{1},RSS^{\prime}_{2},\ldots,RSS^{\prime}_{n})\) from bottom to top into the trained DNN in the offline phase, without additional screening and clustering. The propagation process is shown as Formula (4), and \(y^{1}\), \(y^{2}\) and \(y^{3}\) are the output of each hidden layer.  
\[\left\{\begin{array}{l}y^{1}\Big{(}x^{L}\Big{)}=\frac{1}{1+e^{-(u_{1}x^{L}+b_ {1})}}\\ y^{2}\Big{(}y^{1}\Big{)}=\frac{1}{1+e^{-(u_{2}y^{2}+b_{2})}}\\ y^{3}\Big{(}y^{2}\Big{)}=\frac{1}{1+e^{-(u_{3}y^{2}+b_{3})}}\\ \end{array}\right. \tag{4}\]  
The output feature \(y^{3}\) is then passed into softmax output layer. According to Formula (5), we calculate the posterior probability \(P(l_{i}|L,out),i=1,2,\ldots,z\) of the unknown point \(L\) being at each fingerprint point \(l_{i}\):  
\[P(l_{i}|L,out)=\frac{e^{(-w_{i}^{out}y^{3}-l_{i}^{out})}}{\sum\limits_{j=1}^{ z}e^{(-w_{j}^{out}y^{3}-l_{j}^{out})}},i=1,2,\ldots,z \tag{5}\]  
During the process of floor recognition, the user's floor is the same as the floor with the maximum probability. During the estimate of positioning, the top \(K\) fingerprint points of maximum probability are selected as neighboring points to calculate the coordinates of the unknown point. In theory, neighboring points should be distributed around the unknown point. Taking the geometric relationship into consideration, we calculate the center of neighboring points and take the geometric distance between the center and each neighboring point as the weight factor. The larger the distance, the less significant the relationship between the neighboring point and its center, and the smaller the effect of the neighboring point on the unknown point. Neighboring points that are significantly far away from the center are removed. The coordinates of the unknown point are then calculated by combination weights of geometric distance and posterior probability of selected neighboring points. Figure 4 shows the process of combination weight determination, which is based on the following:  
\[(X_{center},Y_{center})=\frac{\sum\limits_{i=1}^{K}\Big{(}X_{l_{i}},Y_{l_{i}} \Big{)}}{K} \tag{6}\]  
\[Distance_{i}=\Big{(}\Big{(}X_{l_{i}}-X_{center}\Big{)}^{2}+\Big{(}Y_{l_{i}}- Y_{center}\Big{)}^{2}\Big{)}^{\frac{1}{2}},i=1,2,\cdots,K \tag{7}\]  
Figure 3: Deep neural network (DNN) structure used for location recognition.  
\[W_{i}=\frac{1/Distance_{i}+P(l_{i}|L,out)}{\sum\limits_{j=1}^{K}\left(1/Distance_{j}+P \left(l_{j}|L,out\right)\right)},i=1,2,\ldots,K \tag{8}\]  
\[(X_{L},Y_{L})=\sum\limits_{i=1}^{K}\left(W_{i}\left(X_{l_{i}},Y_{l_{i}}\right)\right) \tag{9}\]  
where \((X_{center},Y_{center})\) is the center coordinates of the neighboring points; \(W_{i}\) is the combination weight of the neighboring point \(l_{i}\) with respect to the unknown point \(L\).
## 3 Experiments and Analyses
### Experiment Setup  
The quality of the proposed image fusion scheme is evaluated against the other seven popular multi-focus fusion methods. These multi-focus fusion methods consist of popular spatial-based image fusion methods, like Laplacian energy (LE) [8]; four states of art MST methods, including disperse wavelet transform (DWT) [30], dual-tree complex wavelet transform (DT-CWT) [31], curvelet (CVT) [13], non-subsampled contourlet (NST) [9]; two sparse representation methods, including sparse representation with a fixed DCT dictionary (SR-DCT) [21] and sparse representation with a trained dictionary by K-SVD (SR-KSVD) [32]. The objective evaluation of fused image includes edge intensity (EI) [33], edge retention \(Q^{AB/F}\)[34], mutual information (MI) [35; 36], and visual information fidelity (VIF) [37]. Then it compares the dictionary construction time of the proposed method with K-SVD [32], which is the most popular dictionary construction method. All experiments are implemented using Matlab, version 2014a; MathWorks: Natick, MA, 2014. and Visual Studio, version Community 2013; Microsoft: Redmond, WA, 2013. mixed programming on an Intel(R) Core(TM), version i7-4720 HQ; Intel: Santa Clara, CA, 2015. CPU @ 2.60 GHz Laptop with 12.00 GB RAM.
## 6 Performance Evaluation
### Simulation Results  
The performance metrics like the stability period, half-life time, average residual energy, network lifetime and PDR are analyzed against two parametric benchmarks viz. network size and number of communication rounds. We considered the number of communication rounds up to 4000 in our simulation.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Network Area (M \(\times\) M) & \(100\times 100\), \(150\times 150\), \(200\times 200\), \(250\times 250\), \(300\times 300\), \\  & and \(350\times 350\) \\ Number of sensor nodes (\(N\)) & 150 \\ \(E_{n}\), \(E_{int}\) and \(E_{adv}\) & 1.5, 2 J and 2.5 J \\ \(R_{normal}\), \(R_{int}\), \(R_{adv}\) & 40\%, 35\%, 25\% \\ MAC layer & IEEE 802.15.4 \\ \(E_{dec}\) & \(50\times 10^{-9}\) J/bit \\ \(\varepsilon_{fs}\) & \(10\times 10^{-12}\) J/bit/m\({}^{2}\) \\ \(\varepsilon_{mp}\) & \(0.0013\times 10^{-12}\) J/bit/m\({}^{4}\) \\ Energy consumed during aggregation \(E_{A}\) & \(5\times 10^{-9}\) J/bit \\ Energy consumed by CH during data fusion & \(5\times 10^{-12}\) J/bit \\ (Data + Overhead) Packet size & 4096 bits \\ \hline \hline \end{tabular}
\end{table}
Table 8: Simulation Table.
## 1 Introduction
However, there is a gap regarding the inventory of ETL in Cephalonia, especially the historical ones, which are poorly recorded and mapped. The filling of this gap is considered to be of high importance since this area has suffered from the strongest and most destructive earthquake in the Ionian Sea and from the most destructive earthquake sequence in recent history of Greece: the 1867 earthquake and the August 1953 seismic sequence, respectively, with adverse effects on the local population, the building stock and the natural environment [21, 23]. Moreover, Cephalonia is a highly touristic island, with millions of visitors spending time in various landslide-prone locations, especially along the coast. In this context, we used all available sources of information to create an inventory of landslides, which have been induced from destructive earthquakes generated not only during the instrumental period, but also during the historical pre-instrumental period in Cephalonia (Figure 2). The compilation of an ETL inventory constitutes a preliminary step toward the assessment of the landslide susceptibility in one of the most seismic active areas of the world, for which, however, no relevant information has been published so far. Landslide susceptibility mapping is an important tool that contributes to the mitigation of the adverse effects of disasters induced by ETL not only in Cephalonia but also worldwide. In addition, it is another significant step towards more effective and responsible urban and land-use planning and related policies, procedures, guidelines [24] and improvements in seismic and landslide hazard and risk assessment and mitigation. In this study, we assess the landslide susceptibility of the island in order to initially detect and then highlight the areas susceptible to ETL. This assessment is composed of several steps, including the development of landslide causal factors datasets, the implementation of the Analytic Hierarchy Process (AHP) adapted to the needs of the study, the calculation of the landslide susceptibility index (LSI) and finally the validation of the resulting LSI map (Figure 2). Figure 1: The Cephalonia Island is located at the northwestern part of the Hellenic Arc, east of the Cephalonia Transform Fault Zone (CTFZ). The island belongs to the zone III of the current Greek Building Code with a Peak Ground Acceleration (PGA) value of 0.36 g for a return period of 475 years [22].
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Data Processing
#### 2.3.5 Feature Extraction and Reduction  
To determine the number and validity of features, various software packages were employed to extract features from the physiological signals and Beauty Assessment Scale. This process yielded 31 eye movement signal features, 8 EEG features, and 8 beauty assessment features, for a total of 47 features. During the preprocessing stage, it was confirmed that the beauty assessment data could be comprehensively analyzed alongside the physiological continuous variables (eye movement and EEG). Consequently, data normalization was applied separately to both the beauty assessment and physiological data, standardizing dimensions and mitigating specific effects [68]. The calculation method is outlined in Formula (4) below:  
\[z_{i}=\frac{x_{i}-\mu}{\sigma} \tag{4}\]  
where \(\mu\) represents the mean and \(\sigma\) denotes the standard deviation, both calculated from the entire signal \(X\), with \(x_{i}\in X\) being the individual data points collected from a subject.  
The standardization process was carried out independently for three distinct data types (eye movement, EEG, and beauty assessment). Subsequently, principal component analysis (PCA) was conducted on 47 signal features using SPSS 24.0 (IBM, Armonk, NY,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Skewness** & **Kurtosis** & \begin{tabular}{c} **Minimum** \\ **Value** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Naturalness & \(-\)0.752 & \(-\)0.306 & 1 & 5 & 3.80 & 1.156 \\ Diversity & \(-\)0.198 & \(-\)0.684 & 1 & 5 & 3.14 & 1.113 \\ Harmony & \(-\)0.244 & \(-\)0.761 & 1 & 5 & 3.32 & 1.135 \\ Singularity & 0.092 & \(-\)0.722 & 1 & 5 & 2.85 & 1.142 \\ Orderliness & \(-\)0.201 & \(-\)0.752 & 1 & 5 & 3.20 & 1.130 \\ Vividness & \(-\)0.202 & \(-\)0.487 & 1 & 5 & 3.24 & 1.064 \\ Culture & \(-\)0.185 & \(-\)0.636 & 1 & 5 & 3.09 & 1.104 \\ Agreeableness & \(-\)0.185 & \(-\)0.692 & 1 & 5 & 3.32 & 1.170 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Beauty assessment data symmetric distribution test table.  
USA) [69; 70; 71]. The results indicate that the Bartley sphere test is statistically significant (\(p<0.01\)), with a Kaiser-Meyer-Olkin (KMO) value of 0.772, confirming the efficacy of PCA. The cumulative contribution rate of the extracted eigenvalues was found to be 90.775%; for details, please refer to Table 1 in the Appendix A. After calculating and comparing the weights of each feature, 36 features (depicted in Figure 8), which exhibited a strong correlation with mood, were selected.
# The PIV Measurements of Time-Averaged Parameters of the Synthetic Jet for Different Orifice Shapes
## 3 Results and Discussion
### The Velocity of the Synthetic Jet
#### 3.1.2 Impact of the Dimensionless Stroke Length on the Velocity  
The increase in the dimensionless stroke length had a significant impact on the maximum velocity of the SJ. Independent of the orifice shape, the maximum velocity increased with the decrease of the dimensionless stroke length. The higher dimensionless stroke length for the same characteristic velocity and the same characteristic length scale (see Equation (6)) is realized with decreasing frequency of the actuator and thus the higher peak-to-peak deflection of the loudspeaker diaphragm [38; 39]. The dimensionless stroke length can be identified as the ratio of the length at which the air was pushed out from the actuator cavity and the diameter of the orifice and governs the distance of the vortex ring moving away from the jet exit [18]. The centerline dimensionless time-averaged velocity was higher near the orifice at \(L_{0}=2\) than at \(L_{0}=10\), but it decreased faster with the distance from the orifice. The dimensionless time-averaged velocity profile at the distance \(x/d=10\) and \(L_{0}=2\) and 10 are presented in Figures 9 and 10 respectively. For the SJ radial velocity profiles at the axial distance equal to \(x/d=10\), the distributions for various orifice shapes are similar to Gaussian profile. For \(L_{0}=2\), the \(U_{c}/U_{e}\) was equal to 0.58 for the circular and square orifices and equal to 0.50 for the slot, while at \(L_{0}=10\), for all radial velocity profiles, the ratio of \(U_{c}/U_{e}\) was equal to 0.71.
## 3 Results and Discussion
### Affective Sensory Analysis  
The most relevant finding was that _wine A_, which was the most liked in Poland, was the least liked by Spanish consumers (Table 3). Polish consumers showed a satisfaction degree of \(\sim\)6.0 for the sweetness, sourness, and astringency of the _wine A_; however, Spanish consumers showed low satisfaction for most of the sensory attributes of this wine, going from its color (4.9) to afterstate (5.3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attribute** & **ANOVA \({}^{\ddagger}\)** & _Wine A_ & _Wine B_ & _Wine C_ \\ \hline \multicolumn{5}{c}{**Tukey Multiple Range Test \({}^{\ddagger}\)**} \\ \hline
**Appearance** & & & \\ Color & ** & 5.0 b & 9.5 a & 8.0 a \\
**Odor** & & & \\ Alcohol & ** & 5.5 ab & 6.0 a & 5.0 b \\ Fruity & ** & 5.0 b & 6.0 ab & 7.0 a \\ Floral & NS & 2.0 & 2.0 & 2.0 \\ Vegetable & ** & 2.5 b & 4.5 a & 3.5 ab \\ Spicy & ** & 2.0 b & 4.0 a & 4.0 a \\ Animal & NS & 2.00 & 2.50 & 2.50 \\ Mineral & ** & 0.5 b & 3.0 a & 0.0 b \\ Toasted & ** & 0.0 b & 0.5 b & 4.0 a \\
**Basic Taste** & & & \\ Sweetness & ** & 1.5 b & 2.0 ab & 3.5 a \\ Sourness & ** & 6.0 a & 6.5 a & 4.0 b \\ Bitterness & ** & 4.0 a & 2.5 b & 1.5 b \\
**Flavor** & & & \\ Alcohol & ** & 5.5 b & 6.5 ab & 7.0 a \\ Fruity & ** & 4.5 b & 6.0 a & 6.0 a \\ Floral & ** & 2.0 b & 2.0 b & 3.0 a \\ Vegetable & ** & 2.0 b & 5.0 a & 4.0 ab \\ Spicy & ** & 1.5 b & 2.0 b & 4.5 a \\ Animal & NS & 1.0 & 1.0 & 1.0 \\ Mineral & ** & 1.0 b & 6.0 a & 1.5 b \\ Toasted & ** & 0.5 b & 0.5 b & 4.5 a \\ Astringency & ** & 4.5 ab & 5.5 a & 4.0 b \\ Afterstate & ** & 3.5 c & 6.0 b & 7.5 a \\ \hline \hline \end{tabular} \({}^{\ddagger}\) NS = not significant at \(p>0.05\); ** and ***, significant at \(p<0.01\) and \(0.001\), respectively. \({}^{\ddagger}\) Values (mean of 8 trained panelists) followed by the same letter, within the same row, were not significantly different (\(p>0.05\)), according to [PERSON]’s least significant difference test.  
\end{table} 
Table 2: Descriptive sensory analysis of the studied wines as affected by the type of vessel used for the aging step (_A_: aged in clay-_tinaja_ with non-permeable inner coating; _B_: aged in clay-_tinaja_ without inner coating; C: aged in oak barrels).
## 4 Application Studies
### Correlation between Criticality and Pipe Parameters  
Correlations between the OVERALL criticality with the pipe diameter, flowrate, and degree of nodes (\(DoN_{pipe}\)) are shown in Figures 9 and 10 for Networks 1 and 2, respectively. Here, the pipe-based degree of nodes (\(DoN_{pipe}\)) is defined as the average DoN for the start and end nodes of the pipe, as expressed in Equation (7).  
\[DoN_{pipe,i}=\frac{NodeConstatri_{i}+NodeConEnd_{i}}{2}, \tag{7}\]  
Here, \(DoN_{pipe,i}\) is the degree of nodes for pipe \(i\); and \(NodeConstatri_{i}\) and \(NodeConEnd_{i}\) are the number of pipes connected to the start node and end node of pipe \(i\), respectively.  
Analyzing Figures 9 and 10, a consistent trend is observed between the OVERALL criticality and pipe diameter and flow. That is, the pipes with a bigger diameter and servicing a larger flow tend to be more critical. These components are the main transmission lines located close to the source. Thus, a disturbance occurring at this location will likely incur large losses that require more attention. In addition, the pipes serving large-demand nodes also yield high criticality although they have a small diameter. These observations are commonly noticed in both networks.  
Interesting results are observed from the relation between the criticality and \(DoN_{pipe}\) for the different types of networks. For Network 1, which is densely looped, there is a slight trend that the pipes with higher \(DoN_{pipe}\) are more critical (Figure 9c). Meanwhile, Network 2 with branch-sectors shows an inverse-relation between \(DoN_{pipe}\) and the criticality (Figure 10c). This response is related to the network characteristic, that is, most pipes in a looped network have a high \(DoN_{pipe}\), and disruption of this pipe will affect a considerable loss in the network. For a less looped network or branch-typed network, a pipe with a low \(DoN_{pipe}\) in the branch-sector serving a bigger demand will have a significant effect to the network, as there is no alternative flow path. Reviewing these results, the diameter and flowrate of a pipe can be used as primary predictors to assess pipe criticality, while \(DoN_{pipe}\) can be applied for secondary consideration.  
Figure 10: Relation of OVERALL criticality with (**a**) diameter, (**b**) flowrate, and (**c**) \(DoN_{pipe}\) for Network 2.  
Figure 9: Relation of OVERALL criticality with (**a**) diameter, (**b**) flowrate, and (**c**) \(DoN_{pipe}\) for Network 1.
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 3 Theoretical Background of Deep Learning Methods Used for Self-Driving Cars
### Convolutional Neural Network  
Convolutional Neural Network (CNN) is by far one of the most popular deep neural network architectures, usually consisting of an input layer, one or more convolution and pooling layers, a full connection layer, and an output layer at the top (see Figure 5).  
Convolution layer is the core component of CNNs although the specific structures of CNNs may be different. The convolution kernel (i.e., filter matrix) is convolved with a local region of the input image, namely  
\[y_{j}=\sum w_{ij}*x+b_{j}, \tag{1}\]  
where the operator \(*\) represents two-dimensional discrete convolution operation; \(w\) represents the filter matrix and \(b\) is the bias parameter; \(x\) is the input feature map and \(y\) represents the output of the feature map. The convolution kernel is generally initialized as a small matrix of \(3\times 3\) or \(5\times 5\). In the training process of the network, the convolution kernel will be constantly updated through learning and finally get a reasonable weight.  
Figure 5: The network structure of Convolutional Neural Network.  
The output of the convolution operation is usually run by a nonlinear activation function. The activation function can help better to solve the linear inseparable problem. Functions such as Sigmoid, Tanh, and ReLU are usually used as activation functions, which are listed as follows:  
\[\left\{\begin{array}{l}\text{Sigmoid}:R=\frac{1}{1+e^{-g}}\\ \text{Tanh}:R=\frac{\phi^{*}-e^{-g}}{\phi^{*}+e^{-g}}\\ \text{ReLU}:R=\max\left(0,y\right)\end{array}\right. \tag{2}\]  
when the training gradient descends, ReLU is preferred as it has a faster convergence rate than traditional saturated nonlinear functions. In addition, the output of the convolution operation is modified by a pooling function. Mean-pooling and max-pooling are commonly used, which can keep more information about the background and the texture of the image respectively.  
The success of the Convolutional Neural Network is due to its three important characteristics: local receptive fields, shared weights, and spatial sampling. The shared weight reduces the connection between the layers of the network and reduces the possibility of over-fitting. The pooling layer (i.e., subsampling layer) can reduce the dimension of the middle hidden layer and reduce the calculation amount of the next layer, and provide rotation invariance.  
The CNN has yielded outstanding results in computer image and general image classification tasks recently. Because many self-driving cars technologies rely on image feature representation, they can be easily realized based on CNNs, such as obstacle detection, scene classification, and lane recognition.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 5 Experimental Results  
This section details the interference detection and segmentation experiments on the collected Sentinel-1 measured data to verify the efficiency of IQDN and the accuracy of SISNet. Furthermore, qualitative and quantitative metrics are involved in the evaluation of the performance of diverse intelligent algorithms on interference detection and segmentation. Real SAR images from the Sentinel-1 satellite are built as the MID and PAIS datasets and are used in the following experiments.