esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 EKF-Based Approach for AE Source Localization during Pencil Lead Breaks Tests
Figure 4: Time domain (**left**) and time–frequency domain (**right**) characteristics of the AE signals detected by sensors 1 to 4 when the source is located at PLB1(15, 30). Figure 5: Line profile of the AE scalograms where the time of arrival is determined at the maximum magnitude of the wavelet transforms of the detected AE hits (i.e., PLB1) considered at the average frequency \(\overline{f}=282.63\) kHz. Table 1 gives the results related to the 18 Hsu-Nielsen excitation points. Errors corresponding to the different coordinates are expressed as:  
\[\varepsilon_{x_{s}}=\frac{|x_{s}-\hat{x}_{s}|}{|x_{s}|}. \tag{17}\]  
\[\varepsilon_{y_{s}}=\frac{|y_{s}-\hat{y}_{s}|}{|y_{s}|} \tag{18}\]  
where \((x_{s},~{}y_{s})\) are the coordinates of the real impact positions. \((\hat{x}_{s},~{}\hat{y}_{s})\) are the coordinates of the PLB locations determined using the proposed algorithm. On the other hand, Figure 7 depicts actual and estimated source locations. As it can be seen from Table 1, this algorithm generates results with a good accuracy, where the maximum error to estimate the coordinates of impact locations did not exceed \(\sim\)5 mm. This result shows the robustness and the effectiveness of the proposed approach. Note that errors can be attributed to the positions at which the PLB is performed as well as the sensors positions. Figure 6: Group velocity determination based on the Monte Carlo simulation. Figure 7: Location of the different AE hits emitted by the 18 pencil lead breaks performed on the aluminum surface on the basis of the abovementioned algorithm. In the light of these preliminary results, it becomes possible for us to carry out measurements on real defects created during a mechanical tensile test performed on the same sample. The advantage of the proposed EKF-based algorithm is linked to its ability to take into account uncertainties related to time of arrival and group velocities in order to efficiently fusing multi-sensors data for an accurate AE source localization. The flexibility in adding or removing information within the matrix structure at the data processing step is very useful to characterize dispersive media where different propagating modes are involved. Finally, we note that the computational time on regular personal computer for each source location using the proposed algorithm took less than 0.06 s, which makes the proposed algorithm appropriate for real-time AE applications.
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 3 Proposed Method
### Attribute and Structure Decoding Stage  
In the attribute decoder, a reversed version of the two-layered GCN-based encoder is employed to approximate the node attribute information based on the learned final latent representation \(\mathbf{Z}_{final}\). The attribute decoding process is formulated below:  
\[\mathbf{X}^{rec}=\sigma\bigg{(}\widetilde{\mathbf{A}}\,\sigma\bigg{(}\widetilde{\mathbf{ A}}\mathbf{Z}_{final}\,\mathbf{W}_{4}+\mathbf{b}_{4}\bigg{)}\,\mathbf{W}_{5}+\mathbf{b}_{5}\bigg{)} \tag{10}\]  
Here, \(\sigma(\cdot)\) denotes the activation function, \(\mathbf{W}_{4}\) and \(\mathbf{W}_{5}\) are the learnable weights, \(\mathbf{b}_{4}\) and \(\mathbf{b}_{5}\) are the learnable biases, and \(\mathbf{X}^{rec}\) represents the reconstructed node embeddings through the attribute decoder. The attribute reconstruction error is then computed as \(\mathcal{R}_{A}=||\hat{\mathbf{X}}-\mathbf{X}^{rec}||_{\mathbf{F}}\).  
To extract latent connections between nodes based on the final latent representation \(\mathbf{Z}_{final}\), the structure reconstruction decoder utilizes a neural-based graph structure learning approach to unveil the relationships among nodes in the data and reconstruct the adjacency matrix. The proposed graph learning layer is illustrated as follows:  
\[M_{1}=Relu\Big{(}\mathbf{Z}_{final}\,\mathbf{W}_{gl1}+\mathbf{b}_{gl1}\Big{)} \tag{11}\]  
\[M_{2}=Relu\Big{(}\mathbf{Z}_{final}\,\,\mathbf{W}_{gl2}+\mathbf{b}_{gl2}\Big{)} \tag{12}\]  
\[\mathbf{A}^{r}=sigmoid\Big{(}M_{1}M_{2}^{\mathsf{T}}\Big{)} \tag{13}\]  
\[\mathbf{A}^{rec}=\Big{(}\mathbf{A}^{r}+\left(\mathbf{A}^{r}\right)^{T}\Big{)}/2 \tag{14}\]  
Here, \(\mathbf{W}_{gl1}\) and \(\mathbf{W}_{gl2}\) are learnable weights of the same dimension (where 'gl' denotes graph learning), used for embedding the final latent representation \(\mathbf{Z}_{final}\), while \(\mathbf{b}_{gl1}\) and \(\mathbf{b}_{gl2}\) are learnable biases. Equation (14) is introduced to ensure the reconstructed adjacency matrix is bi-directional. The structure reconstruction error is then computed as \(\mathcal{R}_{S}=||\mathbf{A}-\mathbf{A}^{rec}||_{\mathbf{F}}^{2}\). The objective function of the proposed autoencoder model is formulated in the same way as (5):  
\[\mathcal{L}=(1-\alpha)\mathcal{R}_{S}+\alpha\mathcal{R}_{A} \tag{15}\]  
with \(\alpha\) as the balancing parameter.
# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 4 Proposed ERCP Clustering Algorithm  
This section describes the ERCP protocol, which is designed to optimize the network's lifetime and throughput. The ERCP method works on two levels. The cluster head election and rotation constitute the initial stage of the clustering method. The second level involves the delivery of data to the sink node via an inter-cluster routing method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name of the Algorithm** & **Advantages** & **Disadvantages** \\ \hline LEACH & Enhances energy efficiency by Periodically rotating the cluster heads. & Unbalanced energy consumption due to the random selection of the cluster heads, and it is not addressed the reliability issue. \\ FR-LEACH & Enhances energy efficiency by utilizing energy factor & The load balancing issue is not fully addressed and the reliability issue is not considered. \\ DARE-SEP & Enhances energy efficiency by utilizing energy and distance factors. & The load balancing issue is not fully addressed and the reliability issue is not considered. \\ HMBCR & Enhances energy efficiency by utilizing energy, distance, and load factors. & The reliability issue is not considered. \\ ECRP & Enhances energy efficiency by utilizing energy and distance factors. & The load balancing issue is not fully addressed, and the reliability issue is not considered. \\ CRCGA & Enhances energy efficiency by utilizing energy, distance, and load factors. & The reliability issue is not considered. \\ CPMA & Enhances energy efficiency by utilizing energy and distance factors. & The load balancing issue is not fully addressed, and the reliability issue is not considered. \\ Greedy \& GA & Enhances energy efficiency by utilizing energy and distance factors. & The load balancing issue is not fully addressed, and the reliability issue is not considered. \\ ANN \& GA & The reliability issue is not considered. \\ ML-TSEP & Enhances energy efficiency by utilizing energy, distance, and load factors. & The reliability issue is not considered. \\ DE-SEP & Enhances energy efficiency by utilizing energy, and distance factors. & The load balancing issue is not fully addressed, and the reliability issue is not fully addressed. \\ GCEC & Enhances energy efficiency and coverage. & The load balancing issue is not fully addressed, and the reliability issue is not considered. \\ EPSO-CEO & Enhances energy efficiency by utilizing energy and distance factors. & The load balancing issue is not fully addressed and the reliability issue is not considered. \\ The proposed ERCP & The energy efficiency, load balancing, and reliability issues are considered & Has more computation energy. \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparative summary of the above-mentioned algorithms and the proposed ones.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
###### Abstract
Studies investigating species diversity in continuous water bodies such as rivers, wetlands, and high mountain lakes have increased, reflecting the interest in and recognition of these habitats [85; 86; 87]. However, these types of strains are poorly represented in the scientific literature. Culture parameter optimization is a key point in the improvement of industrial algal production. Both culture media and operational conditions must be \"tailored-made\" for individual strains to maximize their productivity [88]. One available tool is the application of the design of experiments (DoE) [88; 89; 90; 91; 92] to improve concentrations of specific metabolites. This type of experiment has been successfully applied to improve the deposition of specific metabolites in algae and cyanobacteria before [93; 94; 95; 96; 97; 98], especially in the selection of the carbon and nitrogen sources and their specific concentrations. However, to the best of our knowledge, there is no scientific literature on the optimization of culture media for strains from high-mountain environments. Therefore, the present study aimed to determine the optimal C/N/P ratio that enhances the concentration of carbohydrates, proteins, lipids, and carotenoids in two algal strains isolated from high-mountain lakes.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 1 Introduction
Gaussian filter decomposes detailed information and noises from low-frequency image components. It ensures that high-frequency image components only contain detailed information and noises. It can adjust the sparse coefficients in sparse representation to achieve the image denoising. So sparse-representation based image fusion of high-frequency components can complete image denoising and fusion. * Step 2 is a decomposition process of low-frequency and high-frequency components. The high-frequency components of each source image can be obtained, by subtracting the low-frequency components of original input source image. The low-frequency components only contain the source image information without details, and the high-frequency components include the detailed information of source image and noises. * Step 3 is an image fusion process. L2-norm based weighted average method and an online dictionary-learning algorithm are applied to low-frequency and high-frequency components respectively. * Step 4 is an integration process. The integrated image is obtained by merging the fused images of low-frequency and high-frequency components together. The contributions of this paper are as follows:  
1. The low-frequency and high-frequency components of source image are discriminated by Gaussian filter and processed separately. 2. An information-entropy based approach is used to select the informative image blocks for dictionary learning. An online dictionary-learning based image fusion algorithm is applied to fuse high frequency components of source image. 3. An L2-norm based weighted average method is used to fuse low frequency components of source image. This paper is structured as follows: Section 2 discusses the related works of image fusion methods; Section 3 presents the proposed framework; Section 4 simulates the proposed solutions and analyzes experimental results; and Section 5 concludes this paper.
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
## 4 Performance Evaluation
### Distance Covered  
Figure 8(a) shows the distance covered by different edges described in Figure 8 along with the deployed number of relays at each edge. Edge C-Y covers 358 m with 4 relays whereas edge Y-Z and Z-S cover 90 and 268 m with 2 and 3 relays respectively. Similarly, edge C-Z and C-S cover 352 and 528 m with 4 and 5 relays, respectively. Finally, edge C-X and X-S cover 352 and 441 m with 4 and 5 relays, respectively. It is evident from Figure 8(a) that as the number of relays increases across an edge, the distance covered also increases and vice versa. Figure 8(b) shows the distance covered by four different paths described in Figure 8 along with the number of relays deployed on each path. Distance covered by path-1 is 716 m and comprises of three edges, i.e., C-Y, Y-Z and Z-S. Similarly, the distance covered by path-2 is 628 m and comprises two edges, i.e., C-Z and Z-S. Likewise, path-3 covers 582 m and comprises of edge C-S whereas path-4 covers 793 m and comprises of edges C-X and X-S. It is once again obvious from Figure 8(b) that the paths comprising of more relays cover long distances compared with the paths comprising of less relays.
# Fuzzy Case-Based Reasoning System
## 1 Introduction  
Case-based reasoning systems [1; 2; 3] are important in the machine learning field. They have been broadly used in the process of solving new problems based on the solutions of similar past problems. Case-based reasoning is a prominent kind of analogy making [4; 5; 6].  
A fuzzy concept is widely understood by scientists as a concept for which the boundaries of application vary according to specific context or conditions, instead of being fixed [7; 8]. This means the concept is vague in some way, lacking a fixed, precise meaning [9; 10]. Some scientists have claimed that there is no fuzzy concept in the world. For example, [PERSON] stated in 1972 that fuzzy concepts did not exist since they were not scientific concepts [11; 12]. This suggestion was based on an idea that a scientific concept should be clear and precise. However, there is no general agreement on how a scientific concept should be defined, and scientists quite often use imprecise analogies in their models to help understand an issue.  
Over the last decade, a great deal of attention has been devoted to the use of case-based reasoning systems [13; 14; 15]. Scientists sometimes found that the exact inputs of a case-based reasoning system for predictions lead to inaccuracy in the prediction outputs [16; 17; 18]. In view of this, we consider the use of fuzzy cases as a point forecast for load. Traditionally, a case-based reasoning system used crisp cases as its inputs to output crisp results. In this paper, we use fuzzy cases as its inputs and predict fuzzy outputs instead. Our fuzzy case-based reasoning system could produce a fuzzy output according tothe fuzzy inputs by using the algorithm we proposed in this paper. We use this approach in weather forecasting as a test, and a proof is given.  
The organization of this paper is as follows. In Section 2, we briefly introduce the preliminaries of the paper: case-based reasoning and fuzzy logic theory. Section 3 presents how to use fuzzy case-based reasoning methods to express fuzzy sets and use them in prediction problem. Section 4 is the discussion and the experiments. Finally, the conclusion (Section 5) is given.
###### Abstract  
Since the electronic power conditioner (EPC) is a crucial part applied of a Space Travelling-wave Tube Amplifier (STWTA), the reliability issue must be considered. Of all the failure modes of an EPC, the insulation failure of an EPC in thermal and moist environments is the most serious, and needs special attention. By investigating the influence of contamination, humidity, and temperature on surface insulation resistance (SIR) and surface discharge, we focused on the determination of the insulation failure boundary in an EPC. Considering real working conditions, we used the typical circuit applied in the EPC as the test object. The insulation deterioration phenomenon under different thermal and moisture stress was studied. The results show that: (1) SIR of the samples did not change with contamination levels when the relative humidity (RH) was below 70%. When RH was higher than 75%, the SIR began to vary with temperature and ionic contaminant concentration. (2) Even if the samples were not contaminated (the ionic contamination concentration was less than 1.56 ug/cm\({}^{2}\)), the deterioration of the SIR still occurred at 85 \({}^{\circ}\)C/90% RH. (3) The insulation failure boundary caused by surface discharge, and the degree of electrical erosion were related to humidity, pollution, voltage and temperature. To improve the failure caused by insulation, encapsulation was used. Experiments showed that encapsulation is an effective protection method to prevent insulation deterioration.  
Space Travelling-Wave Tube Amplifier (STWTA); electronic power conditioner (EPC); surface insulation resistance (SIR); surface discharge; ionic contaminant; encapsulation Article
# Induced Emotion-Based Music Recommendation through Reinforcement Learning
## 4 Music Recommendation Based on Go-Explore
### The Methodology
#### 4.3.1 Induced Emotion-Based Music Recommendation as MDP  
In this section, we define the notions of _state_, _action_, _reward_, and _transition model_.  
* _state:_ one state corresponds to one specific emotion defined in the circumplex model (Section 3.1), represented as the pair \(\langle x,y\rangle\) where \(x\) and \(y\) are the coordinates in the two-dimensional plane; at each request of recommendation, the user starts with a start state, chooses a target state, and after listening each song reaches a new state.
* _action:_ the action space is the set of possible _musical songs_; given a current state \(E_{s}\) and it's coordinate in the circumplex model \(x\) and \(y\), our model recommends a song and stores \(a,d,e,in,liv,lou,s,t,v\) which are the _acousticness_, _danceability_, _energy_, _instrumentalness_, _loudness_, _speechness_, _tempo_, and _valence_ (see the Spotify audio features in Table 2); therefore, a recommendation is a tuple \(\langle E_{s},a,d,e,in,liv,lou,s,t,v\rangle\).
* _reward:_ as also detailed in the following, in our approach, we adopt a \"feedback-based reward\", i.e., after each listening, the user assigns a score (integer in \([0,10]\)) which represents the perception of the user on \"how much the emotion perceived after the listening is similar to the chosen target emotion\".  
We remark that the complexity of the recommendation task of the MDP described above depends on several terms. First, it depends on the number of emotions \(N_{e}\) described in the circumplex model. As explained in Section 5, in this preliminary work, we focus on 8 emotional states (see Figure 3). Furthermore, the complexity also depends on the domains of each Spotify feature described in Table 2, which represent the parameters that the model changes from time to time to \"adapt\" the trajectory. Finally, the complexity also depends on the length \(N\) of the trajectory (number of songs) \(m_{1},\ldots,m_{N}\) chosen by the model to reach the target emotional state.  
Formally, the complexity of the described MDP is:  
\[\mathcal{O}(N_{E}\times\prod_{x}range(x)\times N)\]  
where \(x\), with \(x=a,d,e,in,liv,lou,s,t,v\) is a Spotify audio feature (see Table 2) and \(range(x)\) is the range of \(x\). However, as discussed in Section 6, we have evaluated the system's responsiveness during real-experience time windows. As a result, the involved users positively rated the system's capability to recommend music in a timely manner.
# Efficient Bloom Filter-Based Routing Protocol for Scalable Mobile Networks +
## 3 Results and Discussion
### Increased No. of Route Requests  
The first performance test consists of measuring the No. of packets sent by the forwarding protocol to discover new routes in a network composed of 200 nodes. In this scenario, the chosen independent variable was the No. of source nodes. The target node remains the same for all tests. As can be seen in Figure 8a, the ZRP and OLSR protocols (INRIA implementation) maintain a value close to constant regardless of the No. of routes requested.  
The DSR protocol requires fewer messages to be delivered than the HRAN for low values of the source node, but the HRAN's layering and heat tunnelling technique enhances the efficiency of the routing process as the number of route requests grows. You can see the ratio of control packets to data packets transmitted in Figure 8b, with the HRAN routing protocol again providing the best results for larger route request numbers.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Phytoevaporation  
In phytoevaporation, heavy metals and metalloids are bound to volatile organic compounds which are then released into the atmosphere through the transpiration process of the plants. [PERSON] et al. [117] studied the phytovolatilization of arsenic from soil using _Pteris vittata_ plant. This plant is capable of metabolizing arsenic into volatile forms (arsenite and arsenate), reducing the arsenic concentration in soil. In nature, few plants are able to carry out phytoevaporation. For this reason, genetically modified plants are used, such as transgenic tobacco plants, that are able to remove mercury from contaminated soils [116]. This technique is not exempt from drawbacks. The main problem is that the volatile substances emitted into the atmosphere can be still toxic, and their emission constitutes secondary pollution around the plants themselves.
## 3 Results  
### Verification Studies  
The model implemented was firstly verified through static and dynamic analyses of isotropic beams. The results showed an excellent agreement with reference solutions, confirming the accuracy of the model.
## 1 Introduction
Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satelliteconstellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).
## 4 Performance Evaluation
### Simulation Results  
In this section, we evaluate the proposed scheme for encounter prediction among users. First, we calculated the encounter results using both Gaussian and RFR algorithms. Subsequently, we computed the energy using the RFEPM results, as the RFEPM demonstrated superior predictive performance compared to the Gaussian model.  
These results were calculated for five users considering the day of the week and the hour of the day. An encounter observed in a specific hour was denoted as 1, while the absence of an encounter was denoted as 0. To determine the encounters, we focused on the weeks in which an encounter occurred on the specified day and in the specified hour. Figures 6 and 7 illustrate the encounters that occurred in January at the cafeteria and library, respectively. The \(x\)-axis represents the ordered pair (day, encounters occurred) for the month, while the \(y\)-axis shows the normalized values of the encounters. In Figure 6, the highest normalized encounters at the cafeteria in January occurred on days 6 and 24. The lowest number of encounters, which was zero, occurred on day 1. The peaks in the graph represent the varying encounter frequencies across different days. Similarly, at the library, as shown in Figure 7, the highest number of encounters, nineteen, occurred on day 8, while the lowest number of encounters, only one, occurred on day 11. Figures 8 and 9 present the encounter predictions and the actual encounters observed through the Gaussian process for the cafeteria and library, respectively. In both Figures 8 and 9, the actual encounters are illustrated using the red dotted line, while the predictions are illustrated using the blue solid line. In both Figures 8 and 9, the timeline is shown on the \(x\)-axis, while prediction results are scaled in the range of [\(-0.2\) 1.0]. The cafeteria illustrates a higher number of encounters than the library, as most people tended to visit the cafeteria frequently.
## 2 OCC System Overview  
Figure 1 shows a generalized block diagram of the OCC system. The data is modulated using a modulation technique, and an LED driver is used to activate the LED. The projected image of the LED is then processed to demodulate the data bits. The optical channel characteristics of OCC are similar to those of other OWC technologies except that the system can achieve higher SINRs as it is less susceptible to interferences.