esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator
## 5 Simulation Results
### Proposed Opening.io Architecture  
Following our initial simulation and the insights derived from the experiment results, a revised architecture for the Opening.io ES architectures was proposed with the goal of improving its performance.  
The basic architecture of Opening.io contains three data nodes; one of them (Data Node 1) resides in the same VM as the ES node (VM0). Therefore, these two nodes are sharing/competing for the same resources (CPU and RAM). Having fewer resources results in the response time of queries executed in data node 1 (i.e., red) being more impacted than the response time of other types of queries. This can be seen in Figure 3(c). Furthermore, sharing resources in VM0 also negatively impacts the performance of the ES node. While there is no direct monitoring of this performance, indications of this impact are reflected in the response time of queries run on the other data nodes (blue and green).  
Figure 4: Query response time achieved for a traffic of (**a**) 140, (**b**) 150 and (**c**) 180 requests during 10 s.  
In this section, we propose and evaluate a new architecture that separates the ES node from data node 1 by migrating data node 1 to another separate VM (VM3), as shown in Figure 5. The proposed Opening.io architecture contains four VMs instead of three. In this architecture, VM0, which hosts the ES node, acts only as an ES node, indexing and coordinating the data nodes. Data node 1 is relocated into its own VM (VM3) with 4 CPU cores, 64 GB of RAM and 1 TB of storage. The main goal is to alleviate and distribute the load on the ES node and data node 1 and verify how the system will handle larger query traffic.  
We repeated the same experiments to evaluate the query response time while varying the query traffic using the same three types of queries (red, blue, and green) as in Section 5.2. Figure 5(a),b compare the query response time achieved by the existing Opening.io architecture and the proposed architecture.  
Figure 5(b) shows that the system reaches the threshold (red discontinuous line) at a later point than with the existing system. In fact, the system executes up to 240 Reqs/s of all three types of queries in less than one second. This means that the system can comfortably handle up to 240 Reqs/s. This is due to the ES node and data node 1 each running in separate VMs, giving them more computational resources (CPU and RAM) to process their tasks.  
Figure 5: High level overview of the proposed revised Opening.io architecture.  
Figure 6: Comparison of query response time achieved with different query traffic volumes. The red dotted line represents the threshold (barrier) of 1 s, beyond which queries affect the response time of those received in the following second.
# Solar Filament Detection Based on an Improved Deep Learning Model
## 5 Results and Discussion
### Evaluation Metrics  
After training, the model can be used for practical applications. It can map the input full-disk H\(\alpha\) images to result images. We evaluated the model's performance using the detection results of the test set. Evaluation metrics were used to calculate the deviation between the result images and the annotations.  
Three performance evaluation metrics--precision, recall, and F1 score--are introduced to objectively and quantitatively assess the detection results of the proposed method. Precision represents the ratio of correctly predicted solar filament pixels to all pixels predicted as solar filaments. A higher precision indicates a more accurate detection of solar filament pixels by the method. Recall measures the proportion of correctly predicted solar filament pixels to the actual number of solar filament pixels. A higher recall indicates fewer omissions in the detection of solar filament pixels. The F1 score, which is calculated based on precision and recall, is used to evaluate the similarity between the detection results and the true labels, providing a comprehensive assessment of the detection outcome. A higher F1 score indicates that the detection results of the proposed algorithm are closer to the true labels, indicating better detection performance.  
The formulas for calculating these evaluation metrics are as follows:  
\[precision=\frac{TP}{TP+FP} \tag{2}\]  
\[recall=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\ score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{4}\]  
where TP, FP, and FN denote the number of truly detected filament pixels, falsely detected filament pixels, and falsely detected non-filament pixels.
# GoZone: A Numerical Model for Travelling Fires Based on Cellular Automata Concept
## 3 Ignition Strategy
### Comparison against Fire Test  
A series of natural fire tests have been performed at Liege University in the framework of the European TRAFIR project. Incident heat flux history was measured at different locations from the centre of the fire through vertical and horizontal thermos-plate flux meters [44]. The proposed ignition strategy was tested by comparing the time of ignition evaluated using the recorded heat fluxes with the time of ignition observed during the test. Table 4 shows the results in terms of time of ignition observed in the test, time of ignition evaluated by the model using a critical energy of 400 kJ/m\({}^{2}\), and the error between the model and the tests.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(m\) [kW/m\({}^{2}\) s] & _t_ign [s] & _E_\({}_{\sigma}\) [kJ/m\({}^{2}\)] \\ \hline
0.1 & 231.0 & 562 \\
0.5 & 66.9 & 439 \\
1 & 42.2 & 441 \\
5 & 16.1 & 462 \\
10 & 10.9 & 442 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linear heat flux exposure.  
Figure 3: Critical energy comparison to obtain the evaluated time of ignition against the fire test data.  
Figure 3 shows the value computed by the model for a critical energy of 400 kJ/m\({}^{2}\) and 600 kJ/m\({}^{2}\) as a function of the observed time of ignition, showing a low dependency of the result of the model on this parameter. It can be observed that the times of ignition were significantly longer than those mentioned in Tables 1-3. This is due to the fact that in these fire propagation tests, it took a significant amount of time for the fire to approach the points of measurements, which means that the received flux was below the critical heat flux until then.
## 1 Introduction
Section 4 describes the steps required to autonomously determine the path planning according to the available information (reduced satellite imagery, cadastral information, and the current position of the autonomous robots). Although this work lacks experimental validation, Section 5 shows an example of the intermediate and final outputs on a real case as a proof of concept. Finally, a few cases are introduced to show the potential of the proposed path planning generated for a single robot and a team composed of two robots.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
###### Abstract  
Coverage limitation due to signal attenuation is one of the main challenges facing single-hop LoRaWAN when deployed in IoT applications that require low power consumption and operate over a wide area network. The challenge can be figured out using the dense deployment of typical LoRaWAN gateways to ensure devices can transfer their data to the network server successfully. However, this is considered a costly path because of the requirement of deploying a larger number of gateways. To cope with this, a multi-hop communication strategy can be utilized to extend the coverage of employed LoRaWAN networks, eliminating the need for dense gateway deployment. Thus, this paper proposes a Listen-To-Talk-based Multi-hop LoRaWAN LTM-LoRaWAN, a multi-hop relaying system for a wide area LoRaWAN network. It supports out-of-range devices to deliver their traffic to the gateway without using complex routing mechanisms or routing topology construction. Moreover, the proposed system is dynamic and does not require any special entities or assign specific tasks to specific devices. The paper provides a detailed description of the design and configuration of the proposed LTM-LoRaWAN. Using simulation, the feasibility of the proposed system was demonstrated. The evaluation result showed that the proposed system outperformed one-hop LoRaWAN and achieved better reliability in traffic delivery.  
LoRaWAN; multi-hop; IoTs; MAC; LPWAN; LoRa +
Footnote †: journal: Accepted: 25 September 2023
# Cost-Sensitive Variational Autoencoding Classifier for Imbalanced Data Classification
## 3 Method and Algorithm
### Cost-Sensitive Variational Auto-Encoder Classifier
#### 3.2.2 Variational Auto-Encoder Classifier  
VAE can be seen as a neural network in nature, so it can be used as a classifier in addition to generating new samples as a generation model. A new output layer is added after the VAE model for data classification, and then a classification loss item (such as the cross entropy loss function) is added on the basis of the original loss function to convert the VAE model into a classifier. The model of the classifier is shown in Figure 4, where \(X\) represents the input data, \(Y\) represents the label of the input data, and \(\hat{X}\) represents the reconstruction of the input data. Due to the fact that a new classification task is added to the original VAE model, the cost function of the original model needs to be changed. The changed cost function is shown in Equation (8), where \(L(\hat{y},y)\) represents the loss function of the model classification, and the cross-entropy loss function was used in this study. \(\psi\) indicates the importance of the classification task.  
\[L=-E_{Z\sim q(z|x)}[logP(x|z)]+KL(q(z|x)||p(z))+\psi*L(\hat{y},y) \tag{8}\]
# Wavelia Breast Imaging: The Optical Breast Contour Detection Subsystem
## 2 Materials and Methods
### Optical Breast Contour Detection (OBCD) Subsystem: The 3D Breast Surface Reconstruction Procedure  
When the 3D scan of the patient's breast is finished, the calibration parameters are applied to the acquired point clouds. The resulting cumulative point cloud is named \(Pc_{Acc}\) (see Figure 10).  
Figure 10 shows the point cloud of the breast when it is introduced into the circular opening, the lower plane of the examination table and the lower section of the plastic ring used to protect the patient's breast. To perform the 3D reconstruction of the breast, we need to remove the points corresponding to the ring and to extract a point cloud corresponding to the breast surface. The methods for 3D surface reconstruction from point clouds generally comprise three main processing steps: denoising, smoothing and mesh generation by triangulation, as presented in [19]. In most of the state-of-the-art methods, the input point cloud is preserved during the processing, while in other methods a deformation step is added before the standard processing [22]. Considering the shape of the pendulums breast during examination at prone patient position (see Figure 10), a new method for 3D breast surface reconstruction has been developed. This new method includes a linear stretching deformation step to transform the input point cloud into an elevation point cloud. Starting from an elevation point cloud, it is easier to transform it into a rectangular grid, thus enabling the use of 2D image processing methods, instead of 3D point cloud processing, to perform the denoising and smoothing steps. Moreover, this rectangular grid allows to define easier the topological connection relationships between points, in the meshing step.  
As said before, the points related to the ring shall be removed and a point cloud corresponding to the breast surface has to be extracted from \(Pc_{Acc}\) to obtain the input point cloud \(Pc_{B}\) for the 3D breast surface reconstruction method. The procedure to generate \(Pc_{B}\) is detailed in Appendix B.  
The 3D breast surface reconstruction method for the OBCD subsystem consists in the following processing steps:  
Figure 10: Cumulative point clouds corresponding to three 3D breast scans with the Wavelia OBCD subsystem: (**a**) cumulative point clouds corresponding to two human breasts: Patient 002 and Patient 004; (**b**) cumulative point cloud corresponding to a test breast phantom.  
* linear stretching procedure;
* denoising procedure;
* smoothing procedure;
* meshing procedure.  
Each of the above processing steps as well as the breast volume computation are specified in a separate sub-section below.
## 3 Results
### Main Impact Factors of the Field GPP Footprint
#### 3.5.3 Influence of Atmospheric Stability on Footprint of Field GPP  
Field GPP footprint distance increased with the increase in atmospheric stability. In this paper, we analyzed the footprint distance when the atmospheric stability was at an unstable, stable and intermediate state (Figure 10). We found that the footprint distance became very small when the atmospheric state was unstable (footprint distances ranged from 15 m to 117 m). When the atmospheric stability was at the intermediate state, the footprint distance was further (footprint distances ranged from 96 m to 1122 m). When the atmospheric stability was at a stable state, the footprint distances were the furthest (footprint distances ranged from 154 m to 2468 m).
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
###### Abstract  
At present, polarization imaging detection experiments of underwater objects mainly focus on the degree of polarization but lack of Stokes vector imaging effect of each element. Based on the principle that the polarization characteristics of different materials are different, the experiment of underwater target detection by laser pulse polarization is carried out in this paper, and the influence of different depths of an underwater object and material factors on polarization imaging detection is studied. The results show that in air, the average degree of polarization of iron sheet is 0.56, that of ceramic tile is 1.00, and that of plastic is 0.48. In water, the average degree of polarization of iron sheet is 0.3625 at 7.5 m, the average degree of polarization of ceramic tile at 7.5 m is 0.359, and that of plastic at 7.5 m is 0.3805. The medium will change the degree of polarization of the measured object. The targets made of different materials have different polarization characteristics, and the polarization information is of great value in improving the detection performance of man-made targets. Polarization detection can obtain better information about underwater objects than traditional radiation intensity detection and can effectively suppress the absorption and scattering of light by water.  
underwater target laser pulse polarization detection;target detection; degree of polarization 2023  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], J. Detection Underwater Targets Using Polarization Laser Assisted Echo Detection Technique. _Appl. Sci._**2023**, _13_, 3222. [[https://doi.org/10.3390/app13053222](https://doi.org/10.3390/app13053222)]([https://doi.org/10.3390/app13053222](https://doi.org/10.3390/app13053222))
# Optimal Clustering in Stable Instances Using Combinations of Exact and Noisy Ordinal Queries
## 1 Introduction
### Our Contribution
#### 1.1.3 Techniques and Relation to Prior Work
In [8; 9], queries involving some \"scalar/multiplicative\" factor \(\alpha\geq 1\)--similar to what we use to simulate same-cluster queries--are used; Their queries are of the form \"\(\alpha d(x,z)\geq d(y,z)\)\" and the answer is correct if the inequality holds, but the oracle may not answer whenever these distances are \"close\", i.e., \(d(x,z)\leq d(y,z)<\alpha d(x,z)\); [9] considers the variant in which the answer is adversarially wrong in this case. It widely believed that _same-cluster queries_ may be difficult to implement in practice, though very powerful. Algorithms based on _same-cluster queries_ both exact and with errors have largely been studied. The _error-free/exact_ case is closely related to our \"all at high cost\" implementations (assuming one same-cluster query costs as one exact call in our model). Specifically, Ref. [24] considers \(k\)-means and provide an algorithm using \(O(k^{2}\log k+k\log n)\) same cluster queries, while Ref. [25] uses \(O(\frac{k^{14}\log k\log n}{e^{k}})\) same-cluster queries for computing \((1+e)\)-approximate correlation clustering; Ref. [26] provides an exact algorithm using \(2C_{OPT}\) same-cluster queries, where \(C_{OPT}\) denotes the number of \"disagreements\" in the optimal solution. Same-cluster queries can also solve _non-center-based_ clustering problems [27], where the corresponding algorithm uses \(O(k^{3}\log k\log n)\), with the hidden constant depending exponentially in the dimensionality of the clusters. Regarding _noisy_ same-cluster queries, Ref. [28] uses \(O(\frac{nk\log n}{(1-2p)^{2}})\) same-cluster queries to reconstruct the \"latent\" clusters. The closest result to ours is probably [29], proving that \(\gamma\)-pertubation-stable instances with \(\gamma\geq 3\) can be solved using \(O(n\log^{2}n)\) noisy same-cluster queries [29], and with \(O(n)\) queries in the noise-free case. Their result applies to a rather general class of center-based clustering problems (including ours). On the one hand, our algorithms use fewer low-cost noisy comparisons, namely \(O(n\log n)\), though for a restricted class of inputs; for balanced clusters, the same-cluster queries are \(O(k\log k)\), though we use exact queries. On the other hand, in some cases, same-cluster queries may be harder or more costly than comparisons of distances, and thus the costs may be incomparable in general. Finally, our algorithm using few same-cluster queries uses similar ideas to (coupon collector and the double Dixie cup extension) in [30] for \(k\)-mean instances that satisfy a \(\gamma\)-margin property (similar to \(\gamma\)-perturbation-stability, though not equivalent).
# Tailoring the V-Model for Optics: A Methodology for Optomechatronic Systems
## 4 Application Example
### System Design: Cross-Domain Solution Concept of a High-Resolution Lighting System  
The solution concept shown in Figure 11 is the outcome of the system design of a research project on high-resolution headlamps. The principle solution shown was derived from a functional structure based on analyses with a prototype [66; 80]. A key decision was the application of a light modulator based on subtractive image generation (\"DMD\" in Figure 11), which is characterized by high resolution [27]. Controlling the light source essentially involves having control over the output luminous flux. Operating the light modulator requires a complex controller that switches the individual micromirrors of the DMD according to the required light distribution.  
The majority of the system's heat loss is expected in the area of the light source and at the light modulator with this concept. Due to the functionally required gaps between the individual micromirrors, about one third of the incident light is absorbed and correspondingly converted into heat. In order to keep the light source and light modulator within the operating temperature range, active cooling is provided. From the selection of the light modulator the required illumination optics including light source follows, for which standard components are selected. As a projection optics, a prototype of a special distorting optics is provided, which was developed and patented within the research project [66; 67].  
Based on the concept shown, a prototype of a complete high-resolution headlamp is build to meet the lighting requirements of the research project [9]. However, the conflict of objectives between high optical efficiency and image quality is not sufficiently considered, which is, however, necessary with regard to the suitability of the system for series production. By concretizing the process model, a detailed design with tailored optical elements is made possible, so that a comprehensive trade-off between optical efficiency and imaging quality can be made.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 2 Methodology
### Proposed Algorithm Description
#### 2.2.2 Adaptive Threshold Generation Algorithm  
This section proposes a threshold calculation method, which generates the threshold by a moving window. The threshold is dynamic and is determined only by the instantaneous magnitude of the channelized signals. The window size is set to \(P\), and the channelized output signal can be expressed as  
\[y_{k}(m)=\sum_{n=0}^{\infty}y_{k}(m+nP),m=1,2,\cdots,P \tag{14}\]  
Set \(y_{k}(m+nP)\) as a data group. The same detection threshold is adopted for the same data group of each channel, and the detection threshold of the \(n\)th data group is generated from the \((n-1)\)th data group. The calculation formula of the noise threshold \(A_{TH}\) is as follows  
\[A_{TH}=a\cdot A_{N}+W_{N} \tag{15}\]  
Figure 5: The weighted overlap-add (WOLA) channelization structure.  
where \(A_{N}\) indicates the estimated noise value, \(W_{N}\) indicates the minimum detection threshold value for suppressing the noise, and \(a\) is the proportionality factor. The optimum values of the \(W_{N}\) and \(a\) are experimentally determined for different hardware platforms.  
Thead channels are selected to calculate the noise threshold, considering the relevance of adjacent channel noise. In addition, in order to eliminate the noise influence of the DC component and the high-frequency component, it is necessary to discard the odd marginal channels. Calculate a set of amplitudes for each sampling point, select the smallest amplitude as the noise accumulation point, and accumulate \(P\) points as the noise estimation value. The influence of random noise on the threshold is reduced by accumulating \(P\) data points.