esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The UM-VLC channel model based on ray tracing proposed by [PERSON] et al. [7] is used as a starting point, from which the LED/PD tilt and rotation model, the shadowing model, and the irregular wall model are inherited. Additionally, we include a dust scattering and absorption model based on Mie theory, which has already been considered in state-of-the-art UM-VLC channel models such as the one presented by [PERSON] et al. [5], although without considering its spectral characteristics. The proposed wavelength-dependent UM-VLC channel model allows us to define a CSK-based UM-VLC channel model very naturally. A simulation scenario with appropriate parameters is implemented to evaluate the CSK-based UM-VLC system in terms of the average received optical power, the signal-to-interference ratio (SIR), the signal-to-noise ratio (SNR), the channel impulse response (CIR), the root mean square (RMS) delay, and the bit error rate (BER). With these metrics, the performance of the CSK-based UM-VLC system was compared with the performance achieved under the reference channel proposed by [7]. The contributions of this paper can be summarized as follows:  
* A novel wavelength-dependent SISO UM-VLC channel model is proposed, taking into account LED/PD relative tilt and rotation, irregular walls, shadowing, light scattering, absorption, and their respective spectral characteristics. The LED is modeled as a Lambertian light source and the ray tracing method is used. * A novel wavelength-dependent CSK-based UM-VLC channel model is proposed on the basis of the proposed wavelength-dependent SISO UM-VLC channel model. * The performance of the proposed CSK-based UM-VLC channel model is evaluated in a simulated environment, obtaining parameters such as the average received optical power, SIR, SNR, CIR, RMS delay, and BER. The proposed model is then compared to a reference state-of-the-art UM-VLC channel model. The paper is structured as follows: Section 1 presents a brief introduction and gives a review of the various UM-VLC channel models presented in the literature. Section 2 defines important concepts in the topics of VLC and CSK necessary for contextualizing the rest of the text, while also defining the UM-VLC channel model proposed in this paper. Section 3 presents the simulation results and discusses them. Finally, the conclusions are provided in Section 4.
# A Monopole UWB Antenna for WIFI 7/Bluetooth and Satellite Communication
###### Abstract  
In this paper, a monopole UWB broadband antenna is designed, fabricated, and measured for wireless communication networks. The initial radiator model of the proposed antenna has a short-sleeve shape, and to expand the impedance bandwidth, the right and left angles are subtracted symmetrically from the lower half of the radiator. The impedance matching is improved by etching slots in the feed line and adding L-shaped patches symmetrically on both sides of the feed line. The results show that the proposed miniaturized antenna system can cover WiFi 7(2.4-2.484 GHz, 5.15-5.35 GHz, 5.725-5.825 GHz, 5.925-7.125 GHz), 4G LTE (2.3-2.39 GHz, 2.555-2.655 GHz), 5G (4.8-5.0 GHz), X-band (7-12.4 GHz), Ku-band (10.7-14.59 GHz), and C-band uplink bands (5.925-6.425 GHz). Moreover, the antenna is found to be omnidirectional at low frequencies, with a maximum peak gain of 5.43 dBi. The antenna can be used for multi-frequency wireless communication applications.  
monopole UWB; short-sleeve-shape; impedance matching; L-shaped; omnidirectional +
Footnote †: journal: Applied Optics and Communications Letters  
1
Footnote 1: email: [EMAIL_ADDRESS]
# Order-Based Schedule of Dynamic Topology for Recurrent Neural Network
## 4 Results
### Experimental Results
Tables 3 and 4 show the results of the MSE and sMAPE metrics of the first test, respectively. They show the model with its counterpart and the evolution of the metric on each phase, at the end, the average obtained from the complete test is shown. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**MSE** & **Phase 0** & **Phase 1** & **Phase 2** & **Phase 3** & **Phase 4** & **Phase 5** & **Phase 6** & **Phase 7** & **Phase 8** & **AVG** \\ \hline M1 & 0.00053 & 0.00018 & **0.00005** & 0.00007 & 0.00002 & 0.00003 & **0.00005** & 0.00013 & 0.00009 & 0.00013 \\ MD1 & **0.00052** & **0.00015** & 0.00006 & **0.00002** & 0.00002 & **0.00001** & 0.00008 & **0.00007** & **0.00008** & **0.00011** \\ TR1 & 0 & 2 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\ RATE 1 & 1 & 1.05 & 0.89 & 0.89 & 0.89 & 0.73 & 0.73 & 0.73 & 0.73 & 0.73 \\ \hline M2 & 0.00070 & 0.00019 & 0.00009 & 0.00002 & **0.00002** & 0.00004 & **0.00008** & 0.00029 & 0.00008 & 0.00017 \\ MD2 & 0.00070 & **0.00013** & **0.00005** & 0.00002 & 0.00004 & **0.00002** & 0.00012 & **0.00005** & **0.00007** & **0.00013** \\ TR2 & 0 & 2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ RATE 2 & 1 & 1.09 & 0.93 & 0.93 & 0.93 & 0.93 & 0.93 & 0.93 & 0.93 & 0.93 \\ \hline M3 & 0.00078 & 0.00020 & 0.00016 & 0.00002 & **0.00002** & 0.00011 & 0.00011 & 0.00028 & 0.00010 & 0.00020 \\ MD3 & **0.00076** & **0.00014** & **0.00010** & 0.00002 & 0.00003 & **0.00002** & **0.00009** & **0.00006** & **0.00007** & **0.00014** \\ TR3 & 0 & 2 & 1 & 0 & 0 & 1 & 0 & 0 & 0 \\ RATE 3 & 1 & 1.12 & 0.95 & 0.95 & 0.95 & 0.79 & 0.79 & 0.79 & 0.79 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Series 1 (MSE), order [Cao] and comparison between the 5 models [M] and their respective dynamic versions [MD], rate, and the decision made [TR] (Growth = 2, Pruning = 1, Normal = 0). Figure 7: Graph of the Topology Rate (Dotted Lines, measured by left axis), Order evolution (CAO), and the decisions made by the algorithm (TR-Bars, the last two metrics measured by the right axis) in the first test. Table 5 shows the results of the OWA calculation in each model, the final percentage of the dynamic architecture, with respect to the Vanilla version, as well as, the number of new units if they were added in the first test. Table 6 shows the result of [PERSON]'s method on each phase of the 14 series. Table 7 summarizes the results of the average of the 14 tests performed three times. As can be seen in Table 7, it is just displayed the final rate of the model but is necessary to remember that the topology of the models changes according to the variation of the order of the series and the decisions made by the policy. So, the last rate might not be the same in all the past phases.
## 5 Performance Evaluation
### Real Dataset
#### 5.1.2 The Fidelity without the Dynamical Adjustment of \(T_{d}\)  
In this section, we will evaluate the fidelity without the dynamical adjustment of \(T_{d}\) during data collection by following the clustering map in Figure 1, and try to get some qualitative relations between the fidelity, the number of nodes and the sleeping node ratio. By default, the predefined fidelity threshold \(\delta\) is set to \(90\%\).  
Figure 2 shows that the sleeping node ratio (SNR) and nodes number for each cluster. SNR is defined by the following equation:  
\[SNR=1-\frac{Number\ of\ active\ nodes}{Total\ number\ of\ nodes}, \tag{18}\]  
which describes the ratio of the sleeping node for each cluster. In other words, the larger SNR, the more idle nodes that means the more energy conservation. Intuitively, when the number of nodes in the cluster decreases significantly, the SNR also appears to noteworthy decline. As we see in Figure 2, the SNR is more than \(60\%\) for the nodes in Cluster 1. We also note that the worst SNR 0 is obtained by Cluster 6 with the minimum number of nodes. Therefore, our SFDC framework is more suitable for the large scale dense WSNs in order to achieve a better effect on energy saving. The conclusion is experimentally verified by running the Algorithm 1 multiple times.  
Figure 3 shows the fidelity curves for 6 clusters with different size for 100 time slots (thirty one seconds per time slot). For the Clusters 1 and 2 with more number of nodes, the fidelity curves show a decline and instability. On the other hand, for Clusters 3-6 with fewer nodes, the fidelities are superior to the threshold throughout the data collection phase. Especially for Clusters 4-6 in which all nodes are active, it can be found that the fidelities during data collection are always \(100\%\). It happens because the structure fidelity is less or not susceptible to the reading changes from the observed area where the less sleeping nodes are located. Obviously, the increase on the number of sleeping nodes leads to the performance degradation (structure distortion), which is amplified when the number and density of node deployment are increased.  
Figure 2: SNR Nodes number.  
Therefore, we notice that there is a trade-off between the fidelity in the data structure and the SNR. This raises the necessity to perform the dynamical adjustment of \(T_{d}\) to achieve large SNR while guaranteeing the low distortion.
# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods
### Data Classification and Analysis
#### 2.5.1 Classification System
Figure 7: Underwater photo samples for Tier 1 substrate classes: (**a**) coarse unconsolidated, (**b**) fine unconsolidated, and (**c**) anthropogenic; Tier 2 fine unconsolidated substrate classes: (**d**) sand, (**e**) sand (ripple surface pattern), (**f**) sand (pockmarked surface pattern), (**g**) sand with co-existing woody debris, (**h**) sand with co-existing organic detritus, and (**i**) sand with co-existing mussel shell hash. detailed Tier 2 classes (CMECS substrate subgroup level), in which the resultant classified area had ground truth samples with sufficient class variation (six Tier 2 classes possible). Conversely, the coarse unconsolidated and anthropogenic Tier 1 classes could not be further discriminated into detailed Tier 2 classes because the ground truth samples in those resultant classified areas only represented a single class type and lacked additional class variability for further expansion. For the biotic component, the aquatic vegetation bed Tier 1 class was further discriminated into detailed Tier 2 classes (CMECS biotic community level) in which there were two Tier 2 classes possible with ground truth samples illustrating additional class variability. Additionally, only the 3 m airborne data were used to evaluate Tier 2 classifications since the 10 m satellite did not have sufficient spatial resolution to resolve more detailed bottom types. More information about the classification methods is provided in the Remote Sensing Classification Section 2.5.3.
# Finite-Time Consensus Tracking Control for Speed Sensorless Multi-Motor Systems
## 2 Preliminaries and Problem Formulation  
### Graph Theory  
Consider a multi-motor system consisting of one leader and n followers, where motor 0 (L) is the virtual leader, and motors 1, 2,..., n are the followers. Each motor can be considered a node in the multi-agent network. The data interaction between motors is realized through communication network, which can be represented by graph theory [5; 26; 37]. \(G_{n}=\{V_{n},E_{n}\}\) is an undirected graph, where the vertex set \(V_{n}=\{v_{1},v_{2},\ldots,v_{n}\}\) refers to \(n\) motors and the edge set \(E_{n}=\big{\{}\big{(}v_{i},v_{j}\big{)}|v_{i},v_{j}\in V_{n}\big{\}}\,(i,j=1,2,\ldots,n)\) refers to the interactions among motors. \(\big{(}v_{i},v_{j}\big{)}\in E_{n}\) denotes that nodes \(v_{i}\) and \(v_{j}\) can receive information from each other. The adjacency matrix, which indicates the connection weight between nodes \(v_{i}\) and \(v_{j}\) for the undirected graph is \(A_{n}=[a_{ij}]\in R^{n\times n}\), where \(a_{ij}=0\), if \(\big{(}v_{i},v_{j}\big{)}\
otin E_{n}\) and \(a_{ij}>0\), if \(\big{(}v_{i},v_{j}\big{)}\in E_{n}\). In addition, the self-loop condition is not considered in the paper, i.e., for \(i=1,\ldots,n,a_{ii}=0\). The Laplacian matrix related to \(L=[l_{ij}]\in R^{n\times n}\) is defined as \(l_{ii}=\sum\limits_{i\
eq j}a_{ij}\) and \(l_{ij}=-a_{ij}\), where \(i\
eq j\). Assume that the diagonal matrix \(B=diag\{a_{10},\ldots,a_{m0}\}\) refers to the weight matrix between the followers and the leader. If \(a_{i0}>0\), then the matrix indicates that \(i\) follower can obtain the information of the leader. Otherwise, \(a_{i0}=0\).
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 3 Results
### Initial Transformation to Spherical Targets  
The parameter \(\text{RMSE}_{\text{SFHER}}\) describes the error of the cloud shape and dimensions after transformation (see Section 2.6.2) based on the four spherical targets (i.e., based solely on the four centers of the targets as described in Section 2.6.2). The results are presented in Table 1. The lowest \(\text{RMSE}_{\text{SFHER}}\) was achieved by the static scanner Leica RTC 360, while the highest values were observed for the GeoSLAM ZEB Horizon RT.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach
### Dataset
#### 3.4.2 YOLOv5  
YOLOv5 is a recently released CNN that distinguishes between static and moving objects in real time, with notable performance and good accuracy. This model processes the full image region using a single neural network, divides it into different components, and then predicts the candidate-bounding boxes and probabilities for each component. The YOLOv5 network is an evolution of the YOLOv1-YOLOv4 network and is composed of three architectures: the head, which generates YOLO layers for multi-scale prediction, the neck, for enhancing information flow based on the path aggregation network (PANet), and the backbone based on cross-stage partial (CSP) integrated into the Darknet [37; 38]. The data are given to CSPDarknet for feature extraction before being transferred to PANet for feature fusion. As seen in Figure 2, the YOLO layer uses three independent feature maps to produce detection results (class, score, location, and size).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Training Images** & **Testing Images** & **Total** \\ \hline Robmarkcole & 1155 & 337 & 1492 \\ \hline Glenn-Jocher & 1500 & 128 & 1628 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of fire images in the dataset.  
The YOLOv5 network is used as a backbone in Figure 2 to extract significant and practical characteristics from the input frame sequences with CSP. The YOLOv5 network builds feature pyramids using the neck model to enable models to successfully generalize in terms of object scale. As a result, it is simpler to recognize the same object at various scales and dimensions [40].
## 4 Results and Discussion
### Fit of Durations Simulation  
In each of the ten thousand scenarios generated in the simulation models of the four case studies, the CTCR and EVM methodologies were applied for the period forecasts on completion of the projects in different months of the progress of the project, using an error range of plus or minus 1 month. The results obtained for each case study are presented below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factors** & **Cardenillo** & **Santiago** & **Sopladora** & **Alazán** & **Mean** \\ \hline K1 & 0.26 & 0.32 & 0.31 & 0.37 & 0.32 \\ K2 & 0.15 & 0.17 & 0.13 & 0.14 & 0.15 \\ K3 & 0.42 & 0.38 & 0.30 & 0.27 & 0.34 \\ K3 & 0.17 & 0.13 & 0.25 & 0.22 & 0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Consolidated results for the k factors of the four projects analyzed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Cardenillo** & **Mazar Dudas** & **Sopladora** & **Santiago** \\ \hline Planned cost (millions of USD) & 995.6 & 56.55 & 678 & 2684.3 \\ Planned cost probability (\%) & 5.40 & 20 & 23 & 26.40 \\ Simulated cost 95\% probability (million USD) & 1006.36 & 59.35 & 714 & 2839 \\ Planned duration (months) & 75 & 27 & 47 & 68 \\ Planned duration probability (\%) & 4.20 & 7.70 & 5.50 & 6.80 \\ Simulated duration 95\% probability (months) & 83 & 32 & 55 & 78 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation models for total costs and durations of the case studies—results.
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 5 Used Tools  
For the purpose of this work the following tools were used:  
* **Network protocol analyzer:** for this work, Wireshark ver. 4.4.7 [31] was used. It is installed on the monitoring station and captures all packets exchanged with the studied PED. It is used to execute step 3 of the proposed methodology--finding out whether the exchanged information is secure or not;
* **Network analyzer:** for this work, Colasoft Capsa 11 free [32] was used. It is also installed on the monitoring station and is used to monitor the traffic entering/exiting the network interface of the studied PED. This tool will be used to observe the response of the PED when it is subjected to the various TCP DoS attacks;
* **Burg Suite Community Edition:** this is a platform for performing security tests on web applications [33]. It is used in step three of the proposed methodology as a validation of the obtained results from the Wireshark;
* **Nmap:** this is used to discover hosts and links in a computer network by sending packets and analyzing the responses [34]. It is part of the built-in tools of Kali Linux. It provides many capabilities for exploring computer networks, host discovery, service and operating system discovery. By using specialized scripts, it can be used to identify vulnerabilities. Nmap is used in the first and second steps of the developed methodology;
* **hping3:** this is a tool that can be used to scan a network/device as well as to generate altered ICMP/UDP/TCP packets for implementing various DoS attacks [35]. It is one of the built-in tools of Kali Linux. The tool is used in the first and last stages of the developed methodology. These tools are chosen due to several factors:
* First: they are free. There is no need to pay for them, which means anyone can use them. Thus, the proposed experimental setup for studying the level of network security of PEDs is much more affordable and almost anyone can implement it using these free tools;
* Second: regardless of being free, the information they provide is reliable and trustworthy. They are used by everyone who is involved in network security monitoring of various network devices. There is an endless amount of information on the Internetabout how and what these tools can be used for, which is what makes them so popular among network security enthusiasts and researchers;
* Third: they are easy to work with. For example, Capsa 11 presents information in a graphical form, and one can choose what the refresh interval should be, which results in a change of the graph. Changing this interval produces different graphs that can be used to represent different stages of a study.
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 3 Experiments and Results
### Dataset Introduction  
The gearbox dataset, featuring small samples in the target domain, was collected from a two-stage gearbox test rig, as shown in Figure 5. Vibration signals of the gear were measured via an accelerometer attached to the gearbox housing. The signals were recorded using a dSPACE system (DS1006 processor board, dSPACE Inc., Wixom, MI, USA) at a sampling frequency of 20 kHZ. More details about the test rig and data acquisition apparatus are in [8]. The dataset was gathered under nine working conditions: healthy, missing tooth, root crack, spalling, and chipping tip with five different severities. These conditions are shown in Figure 6.  
Figure 4: The proposed fault diagnosis model with intra-domain learning strategy.  
Each working condition of the gearbox dataset has 104 samples, each of which has a length of 3600. Table 1 briefly describes some metadata of the dataset. In the case of a nine-class classification problem where the number of samples is far less than the sample dimension, it is challenging to train a reasonably good diagnostic model from scratch. In addition, fault signatures might be submerged by meshing frequencies and noises, thus aggravating the difficulties of feature extraction. Note that when the dataset was released originally in [8], the authors considered utilizing transfer learning to solve the problem, but ultimately chose a different approach.  
The Case Western Reserve University (CWRU) bearing dataset is chosen as the source domain for transfer learning in this study, as it is one of the most extensively used datasets for fault diagnosis benchmarking [34]. The dataset contains vibration signals collected at a frequency of 12 kHz and 48 kHz. We only utilize those with a sampling frequency of 12 kHz to reduce variation in the source domain. Three types of faults (ball, inner race, and outer race) are introduced in the experiments, each with three fault sizes (0.007, 0.014, and 0.021 in).  
The monitoring items, sampling frequency, and working conditions are different across source and target domains, but both datasets have high-frequency aperiodicity. Moreover, the source and target domains resemble each other visually after time-frequency transformation, allowing us to transfer feature extractors between intra-domains.  
Figure 5: Gearbox test rig of University of Connecticut [6].  
Figure 6: Nine working conditions, including healthy state and eight types of fault [6].
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results  
### Results on ZH300  
#### 5.1.1 Input Sequence Length  
The input sequence length affects the model's performance, and each climate variable may need a different length of the input. This experiment indicates the proper input sequence for each model to perform one step ahead prediction on ZH300. Figure 5 shows the average RMSE prediction results comparing the models with the input sequence length of one to six months. The SP-CRN in this experiment is utilizing the PRD proposal of nearby-time representation, where \(n_{pre}=0\) (zero nearby-time representation). The results show that when increasing the length of sequential input, the RMSE of all models is significantly decreasing. Furthermore, the RMSE results tend to be converged after the sequence length of five or six months, indicating that the five to six months prior knowledge of ZH300 is essential for one step ahead of ZH300 prediction, which is consistent with the SST memory is about six months [52]. The SST is used to indicate ENSO, which impacts the prediction of weather and climate. The results also present superiority of the models consider periodicity component, which has the lower RMSE from their base model.