esa-sceva/satcom-chunk-collection huggingface dataset:
# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 3 Proposed Methodology
### Synchronization Using Ring Framework  
As illustrated in Figure 6, the \(m\) users involved in the communication are organized in a ring, so that user \(USER_{j}\) only takes messages from \(USER_{j-1}\) and communicates with \(USER_{j+1}\). The initial input vector and the weight range are distributed across the users. Here, flag \(g\) is used to indicate whether the output of TLTPMs of different user's are same or not. If output of two TLTPMs are identical then flag set to 1. Otherwise, it is set to 0.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Meaning** \\ \hline \(N\) & The quantity of input nodes that must be accessible to every node from the first \\  & concealed layer. \\ \(\lambda\) & Weight range \\ \(\tau\)1 & 1 st concealed layer node count \\ \(\tau\)2 & 2 nd concealed layer node count \\ \(\tau\)3 & 3 rd concealed layer node count \\ \(\delta\) & Weight vector \\ \(\gamma\) & The input of neural system \\ \(\xi\) & Number of input vectors \\ \(\mu\) & Vector dimension \\ \(\xi\) & Output of hidden layer \\ \(\kappa\) & Computation result of hidden layer \\ \(\zeta\) & Output of TLTPM \\ \(\Phi\) & Heaviside stage function \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Symbols’ Meaning.  
Phase One: Initialization:  
1. Each member \(USER_{j}\) will also have his or her own TLTPM, with starting weight.  
2. Each member \(USER_{j}\) would put the flag number \(g=1\) and initialize his TLTPM with the shared input.  
Phase two: Key Generation:  
3. Until coordination, every principal \(USER_{j}\) will carry out the procedures below.  
(i) Calculate the values of the hidden neurons and the output neuron. Principal \(USER_{j}\) now transmits its output and flag values to \(USER_{j+1}\). Then, \(USER_{j+1}\) compares its output to the received one. If both outputs are same then \(g_{j}=1\) is kept. Otherwise, \(USER_{j+1}\) reset the flag value to 0 and transfers it to \(USER_{j+2}\).  
(ii) If the flag parameter \(g_{j}\) is initialized to zero at any time during the calculation, the subsequent users \(USER_{k}\) (\(k=j+1\), \(j+2\)) continue to propagate \(g_{j}=0\) to the remaining principals in the ring until \(j\equiv k\ mod\ m\). As a result, a newer flag value will be sent to the agreed-upon users.  
(a) Go to step 2 if the flag \(g=0\).  
(b) If the flag \(g=1\), each user \(USER_{j}\) updates their weight vector according to the following learning rule (Equation (1)).  
\[\delta^{q+}_{v,w}=fun\left(\delta^{d}_{v,w}+\gamma^{q}_{v,w}\zeta\Phi\left( \delta^{q}_{v}\zeta\right)\Phi\left(\zeta^{G}\zeta^{D}\right)\right) \tag{1}\]  
The Heaviside stage function is \(\Phi\)  
The keys are the same weights acquired from the coordination. The keys now can be utilized for the encoding/decoding. After agreeing on the neural key by the members of the group, it may be necessary for users to enter or quit the group in the ring structure. One method is to simply resurrect the method in order to generate a new key for the customized group.
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 4 PEID: LOS/NLOS Identification Based on Peak Energy Index Distribution
### A Machine Learning-Based Approach
J45, being a variant of the decision tree algorithm similar to C4.5, exhibits a complexity of \(O(n\log(n)d)\). Random Tree, another variant of the decision tree algorithm, constructs a tree using randomly selected features at each node. The complexity of a Bayes Net is challenging to determine precisely. Previous research [45] suggests that when the Directed Acyclic Graph (DAG) in the model is uniformly sparse, the complexity of a Bayes Net can be quadratic in \(n\). Conversely, the complexity of the AdaBoost algorithm is linear in \(n\), making it the lowest among the five algorithms in terms of computational complexity. To further investigate how much contribution each feature makes to classification, we also perform feature importance analysis based on Random Forest. With the help of Mean Decrease Impurity (MDI) estimation provided by the WeKa tool, we can compute the feature importance and plot it in Figure 5.  
It shows that the importance distribution of all selected features is quite balanced. That means that no feature should be discarded due to its poor contribution to the classification. However, both F1 and F2 achieve the highest value in the evaluation, indicating that the STD and the mean value of the SIFPE play an essential role in the LOS/NLOS identification. Such a result contrasts with the previously examined basic approach since it only achieves an accuracy of 67% when choosing the STD as a threshold. We attribute the improvement in accuracy to the machine learning technique and the rest features. Nevertheless, the STD of time index is still the most critical one. In the rest of the paper, we choose Random Forest as the default identification model when no algorithm is specified.
# PHEFT: Pessimistic Image Processing Workflow Scheduling for DSP Clusters
## 2 Model  
### Basic Definitions  
We address an offline (deterministic) non-preemptive, clairvoyant workflow scheduling problem on a parallel cluster of DSPs.  
DSP-clusters consist of \(m\) integrated modules (\(IM\)) \(IM_{1}\), \(IM_{2}\),..., \(IM_{m}\). Let \(k_{i}\) be the size of \(IM_{i}\) (number of DSP-processors). Let \(n\) workflow jobs \(J_{1}\), \(J_{2}\),..., \(J_{n}\) be scheduled on the cluster.  
A workflow is a composition of tasks subject to precedence constraints. Workflows are modeled as a Directed Acyclic Graph (DAG) \(G_{j}=\left(V_{j},E_{j}\right)\), where \(V_{j}\) is the set of tasks, and \(E_{j}=\left\{\left(i,k\right)\mid i,k\in V_{j},\;i\
eq k\right\}\), with no cycles.  
Each arc \(\left(i,k\right)\) is associated with a communication time \(d_{i,k}\) representing the communication delay, if \(i\) and \(k\) are executed on different processors. Task \(i\) must be completed, and data must be transmitted during \(d_{i,k}\) prior to when execution of task \(k\) is initiated. If \(i\) and \(k\) are executed on the same processor, no data transmission between them is needed; hence, communication delay is not considered.  
Each workflow task \(i\) is a sequential application (thread) and described by the tuple \(\left(r^{\prime}_{i},p^{\prime}_{i}\right)\), with release date \(r^{\prime}_{i}\), and execution time \(p^{\prime}_{i}\).  
Figure 1: Digital signal processor (DSP) cluster.  
Due to the offline scheduling model, the release date of a workflow \(r_{j}=0\). However, the release date of a task \(r^{\prime}_{i}\) is not available before the task is released. Tasks are released only after all dependencies have been satisfied and data are available. At its release date, a task can be allocated to a DSP-processor for an uninterrupted period of time \(p^{\prime}_{i}\). \(c_{j}\) is completion time of the job \(j\).  
Total workflow processing time \(p^{G}_{j}\) and critical path execution cost \(p_{j}\) are unknown until the job has been scheduled. We allow multiprocessor workflow execution; hence, tasks of \(J_{j}\) can be run on different DSPs.
### Contribution  
We identify the security flaws of the schemes in [4]. Aiming at solving the identified problems, a scheme, called AAD, is proposed to Acquire Authentic Data in UWSNs. AAD possesses three characteristics. (1) Due to the proper use of sensors' position information, it is communication-efficient. (2) In addition to acquiring authentic data, AAD is also resilient against both Path-Based DoS (PDoS) [5] and False-Endorsement DoS (FEDoS) [6] attacks. (3) The resilience of AAD against sensor compromises is superior to that in prior works [4]. From analytical and simulation results, the robustness of AAD is demonstrated to be superior to those of CoMAC and ExCo.
# Evaluating Litter Yield and Decomposition for Re-Vegetated Mangroves in a Subtropical Mudflat
## 2 Materials and Methods
### Field Investigation and Sample Collection  
#### 2.2.1 Monitoring of Litterfall  
Monitoring of litter yield was focused on the three dominant mangrove species (i.e., _Kandelia obovata (Linn.) [PERSON], Heritiera litteralis(Dryand)Ait._, and _Thespesia populnea(L)Soland.ex Corr_). For each species, three random sample plots (10 m \(\times\) 10 m) were used; their locations are indicated in Figure 1. The basic growth characteristics of the investigated mangrove trees in the sample plots are given in Table 1.  
The monitoring was carried out from September 2013 to August 2014. Three litterfall traps were randomly placed in each of the nine sample plots. The litterfall collection unit was made up of a square wooden frame (1 m \(\times\) 1 m) and a nylon mesh bag (pore size: 1 mm) attached to the frame. The litterfall collector was hung under the tree with the bottom being about 0.3 m above the ground. Litterfall was collected monthly. The litters in each collection unit were packed in a plastic bag, transported to the laboratory, and oven-dried at 60 \({}^{\circ}\)C until constant weight was obtained. The dried litter biomass was then obtained by weighing.
# The Improved A* Algorithm for Quadrotor UAVs under Forest Obstacle Avoidance Path Planning
## 2 Materials and Methods
### Improved A* Algorithm  
The heuristic search of the traditional A* algorithm is relatively simplistic, and the weight relationship between cost functions is not taken into account, which leads to a higher number of nodes being searched and reduced efficiency. Additionally, to ensure the shortest path, the algorithm has more turning points, resulting in a path that is not smooth [40]. In this paper, to meet the requirements of high efficiency, low energy consumption, smoothness, and continuity for forestry inspection UAVs, an improved A* obstacle avoidance algorithm is proposed. The algorithm improves the traditional A* algorithm in the following four steps.
## 5 Results
### Tuning of the SAUR Algorithm  
The first goal of simulations was to tune the SAUR algorithm. To recall, the SAUR method has one tuning parameter \(\beta\) that is used to scale the impact of average transponder usage on the placement of transponders in particular nodes. After some preliminary experiments, we decided to test the values of parameter \(\beta\) in the range from 0.0 to 2.0 with step 0.2. In Table 2, we present the results of the tuning process, i.e., values of parameter \(\beta\) providing the best results in terms of the accepted traffic for US26 and Euro28 networks. We should underline that the obtained values of parameter \(\beta\) are quite similar for both analyzed topologies.  
In turn, in Figures 6 and 7 we present detailed results showing the performance of the SAUR method for the tested values of parameter \(\beta\) for networks US26 and Euro28, respectively, with 10,000 and 20,000 transponders available in the network. The key observation from these figures is that the impact of the tuning parameter was the largest for TP E, while for other traffic profiles the impact was smaller, especially for larger number of available transponders (i.e., 20,000). This follows from the fact that TP E had the largest variations in values of traffic bit rate between different node pairs, and thus the placement of transponders was very important for both transmitting/receiving (end nodes) and regeneration. It should be noted that in the following subsections, the reported results of the SAUR method were obtained using the values of the tuning parameter \(\beta\) shown in Table 2.
## 2 Measurement Technique  
This section describes the configuration of the equipment of the 5G channel sounder that was used for our experiment. The time domain measurements were conducted in an I2O NLOS environment at 19, 28, and 38 GHz frequencies. The block diagram of the measurement equipment is shown in the Figure 1. An arbitrary waveform generator (AWG) was applied to a transmitter (Tx). The receptor (Rx) was equipped with a 12-bit (1 GHz bandwidth) high-speed digitizer (Rx) for the acquisition of a sound signal. At the transmitter, the AWG transformed a radio frequency (RF; up to 44 GHz) carrier with broad modulation bandwidth from the produced differential basis band in-phase quadrature (IQ), using an E8267D PSG up converter. The down-converter M9362 AD01 PXIe was used to convert the RF frequencies (up to 40 GHz) to the intermediate frequency, the IF signal was amplified by an M9352A hybrid amplifier/attenuator (500 MHz), and the IF signal was lastly acquired using the interlocking mode with the M9703A 12-bit 1 GHz bandwidth high-speed numbers. The local down converter oscillator (LO) was used with an N5173B EXG. Two rubidium clock devices (one for Tx and one for Rx) were used to synchronize the transmitter with the receiver, providing a high generation l0 MHz referring signal for all of the devices with \(\leq 10^{-11}\) accuracy and \(\leq 3\times 10^{-11}\) stability. The function generation system (Trigger box) was used to derive the trigger signals. Additional details of the measurement hardware can be found in the literature [25]. A 12-bit high speed digitizer M9703A was used for the sounding signal acquisition, which can provide one channel 1.6 Ga/s (625 MHz bandwidth), or four channels 3.2 G Sa/s interleaving acquisition (1 GHz bandwidth) [26].  
The AWG yielded a 1-ns multipath resolution from a 1000 Mcps with sample rate of 7.2 GHz. We used a signal generator (up-converter) to generate the center frequencies at 19, 28, and 38 GHz with a transmitted power of 0 dBm. The signal was transmitted through a 11.6 dBi gain (39.9\({}^{\circ}\)/49.9\({}^{\circ}\) azimuth/elevation half-power beamwidth (HPBW)), a 11.6 dBi gain (39.7\({}^{\circ}\)/49.7\({}^{\circ}\) azimuth/elevation HPBW), and a 15.2 dBi gain (29.6\({}^{\circ}\)/29.7\({}^{\circ}\) azimuth/elevation HPBW) ETS-Lindgren horn antenna for the 19, 28, and 38 GHz frequencies, respectively. At the receiver, an omnidirectional antenna (3 dBi gain) with a relatively high gain power amplifier of 37 dB was used to collect the received signal.
# AEA-RDCP: An Optimized Real-Time Algorithm for Sea Fog Intensity and Visibility Estimation
###### Abstract  
Sea fog reduces visibility to less than 1 km and is a major cause of maritime accidents, particularly affecting the navigation of small fishing vessels as it forms when warm, moist air moves over cold water, making it difficult to predict. Traditional visibility measurement tools are costly and limited in their real-time monitoring capabilities, which has led to the development of video-based algorithms using cameras. This study introduces the Approximating and Eliminating the Airlight-Reduced DCP (AEA-RDCP) algorithm, designed to address the issue where sunlight reflections are mistakenly recognized as fog in existing video-based sea fog intensity measurement algorithms, thereby improving performance. The dataset used in the experiment is categorized into two types: one consisting of images unaffected by sunlight and another consisting of maritime images heavily influenced by sunlight. The AEA-RDCP algorithm enhances the previously researched RDCP algorithm by effectively eliminating the influence of atmospheric light, utilizing the initial stages of the Dark Channel Prior (DCP) process to generate the Dark Channel image. While the DCP algorithm is typically used for dehazing, this study employs it only to the point of generating the Dark Channel, reducing computational complexity. The generated image is then used to estimate visibility based on a threshold for fog density estimation, maintaining accuracy while reducing computational demands, thereby allowing for the real-time monitoring of sea conditions, enhancing maritime safety, and preventing accidents.  
sea fog intensity estimation; visibility estimation; maritime accidents; real-time monitoring; video-based algorithms; maritime safety +
Footnote †: journal: Applied  
Article
###### Abstract
For example, [PERSON] [8], by treating EEG signals as graph structures, using the graph convolution layer of a GCN to extract node features, the pooling layer to summarize the abstract node features, and the fully connected layer for classification, achieves efficient information fusion and feature extraction; [PERSON] [9] proposed a method based on self-supervised graph neural networks, which represents the spatiotemporal dependencies in EEG by constructing two EEG graph structures (electrode geometry and dynamic brain connectivity). However, epilepsy data not only have graphical structural features (such as the spatial relationship between electrodes) but also contain time series information (such as the change of epileptic signals over time). A small number of studies have focused on extracting non-Euclidean features related to graphs between channels and Euclidean features related to time of EEG signals. For example, a dynamic functional connectivity neural network for epileptic seizure prediction using multi-channel EEG signals is innovative in that it combines graph convolutional networks (GCNs) and convolutional neural networks (CNNs) to extract non-Euclidean and Euclidean features and enhances model performance by introducing intra-group loss and inter-group loss. Our study proposes a method that combines a graph convolutional network (GCN) with a long short-term memory (LSTM) network. The spatial features include the interdependencies between the electrical signals recorded in the epileptic seizure area learned by a GCN, and the temporal features include the correlation between the EEG signals before and after the epileptic seizure learned by LSTM. In this method, we perform computational fusion on the extracted time-frequency domain features and find relatively important features for predicting epileptic seizures by analyzing the feature importance. The main contributions of this paper are as follows:  
1. The GCN-LSTM model proposed in this paper consists of multiple layers of graph convolutional networks (GCNs) and long short-term memory (LSTM) units, enabling it to learn features at different levels. At the electrode level of the signal acquisition device, the GCN learns spatial features at various hierarchical depths. Along the time axis, the LSTM network learns temporal sequence features, integrating spatial features with temporal ones to identify highly discriminative features for epilepsy detection. 2. The performance of the epilepsy seizure detection framework GCN-LSTM proposed in this article was verified using the epilepsy dataset of Boston Children's Hospital. Compared with several existing models, the framework presented in this paper yields superior results, demonstrating the robust detection capability of the proposed network in the realm of epilepsy EEG recognition. By analyzing the feature importance, we found the features that have a greater impact on epileptic seizure prediction.
# Research on Characteristics of Flow Noise and Flow-Induced Noise
## 3 Validation of Calculation Method  
### Computational Model and Boundary Conditions  
This paper utilizes numerical simulation method verification based on the simplified model of the teardrop submarine \"Albacore Submarine\" [19]. The geometric data of this teardrop submarine model came from [20]. We used the same scaling as in reference [21], using a size scale of 1:25, model length of 3.2 m, and width of 0.4 m. The entire flow field computational domain was a cuboid, with a length 4 times the submarine length in the incoming flow direction, and 2 submarine lengths in the wake flow direction. The cross-section of the computational domain was a square with a side length 10 times the maximum width of the model. To accurately simulate the flow field flow details, \(y^{+}=50\) was taken and the mesh was 127 w. The computational domain mesh is shown in Figure 1.  
The boundary conditions of the flow field computational domain consist of the velocity inlet, pressure outlet, hull surface, and outer boundary. The boundary conditions were set as follows:  
1. Velocity inlet: The left end face (blue) of the fluid domain, with a Dirichlet boundary condition of 5 m/s velocity.
2. Outflow: The right end face (yellow) of the fluid domain, located 2 submarine lengths behind the stern, so a free outflow boundary condition was used.
3. Symmetry boundary condition: The upper, lower, left, and right four end faces of the fluid domain. These positions were far away from the submarine surface, so the normal velocity components on these end faces could be considered zero, thus a symmetry boundary condition was used.
4. Wall boundary condition: The outer surface (green) of the model, with a no-slip boundary condition.
# Bearing Fault Diagnosis Approach under Data Quality Issues
###### Abstract  
In rotary machinery, bearings are susceptible to different types of mechanical faults, including ball, inner race, and outer race faults. In condition-based monitoring (CBM), several techniques have been proposed in fault diagnostics based on the vibration measurements. For this paper, we studied the fractal characteristics of non-stationary vibration signals collected from bearings under different health conditions. Using the detrended fluctuation analysis (DFA), we proposed a novel method to diagnose the bearing faults based on the scaling exponent (\(\alpha_{1}\)) of vibration signal at the short-time scale. In vibration data with high sampling rate, our results showed that the proposed measure, scaling exponent, provides an accurate identification of the health state of the bearing. At the end, we evaluated the performance of the proposed method under different data quality issues, data loss and induced noise.  
bearing; vibration; diagnostic; non-stationarity; detrended fluctuation analysis; fractal characteristics; scaling exponent; data anomalies; Gaussian noise +
Footnote †: journal: Applied Sci.  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 Formulation of The Problem
### Limitation of the W-DO Assumption
Each sentence was divided into a group with the other \(K-1\) (\(2\leq K\leq 6\)) sentences in the time domain, resulting in \(K\) simultaneously occurring speech conditions. For \(K=2\), each sentence was divided into a group with each of the remaining 35 sentences resulting \(36\times 35=1260\) combinations. For \(K>2\), each sentence was randomly grouped 35 times with \(K-1\) other sentences to give the same number of combinations (1260) as for \(K=2\). In addition, \(\xi=0.9\). The average length of each recording is about 8 s. Based on the aforementioned conditions, a statistical analysis of ROTF is taken. Statistical results are shown in Figure 2 with 95% confidence intervals. It can be observed that more TF bins are overlapped when the number of simultaneously occurring sources is increasing. In particular, when \(K\geq 5\), the TF bins are almost overlapped, with a high percentage of over 90%.
## 6 Discussion  
The simulation results underscored the efficacy of the proposed Optimized LSTM-GA model, which consistently achieved higher accuracy, precision, and recall compared to both the Basic LSTM and the standard LSTM-GA configurations. These metrics indicated that hyperparameter tuning via an expanded genetic search space captured complex temporal patterns more effectively, contributing to enhanced spoofing detection under diverse scenarios. While these findings affirm the robustness of LSTM-based approaches for GPS spoofing, it is critical to situate these performances within the broader landscape of machine learning paradigms and hardware constraints. Consequently, the following subsections contextualize the proposed methodology by contrasting it with alternative model architectures and exploring avenues to mitigate potential computational overhead in resource-limited UAV environments.