esa-sceva/satcom-chunk-collection huggingface dataset:
# RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
## 2 Related Works
While in the proposed method, the unsupervised learning manner is applied, which does not need manual intervention, and the parameter learning is performed according to the data and model structure; thus, it is easy to operate. Moreover, the proposed method does not require a large number of manual annotation data in advance, so complex environments can be actively explored and better solutions can be obtained. Second, for [12], it is difficult to deal with missing data and the multi-classification problem, which can be handled well through deep reinforcement learning in our proposed method. Moreover, the relationships between data are easy to be ignored in [12]. While for the proposed method, the explicit data, implicit data, and the relationship between data can be explored. For [13], an appropriate proximity measure \(K\) and data preprocessing are required; otherwise, wrong predictions can be achieved by \(k\)-nearest neighbor. While this problem does not exist in our proposed method, which is simple, convenient, and flexible. For [14], before signal detection using the CLS and CLUS clustering methods, the signal needs to be preprocessed with the modulation-constrained GGM, which is inflexible and has difficulty dealing with the situations different from agglomerative hierarchical clustering. While in the proposed method, pre-modulation is not needed, and both the same and different agglomerative hierarchical distributions can be processed. For [15], unsupervised learning is used, which also does not need to label the data, but RIS is not used to improve the channel. The difference of signal characteristics when sending 0 and 1 tags is relatively small, which influences the improvement of detection effect. For [16], an AmBC signal detection method is proposed based on a deep transfer learning framework and convolutional neural network, so the requirement of channel state information computation is eliminated. The problem of insufficient annotation data is solved. However, a large amount of pre-training, fine-tuning, and mau intervention are required, which is relatively complex and not flexible enough. While in our proposed method, pre-training, fine-tuning, and mau intervention are not needed. Moreover, compared with references [12; 13; 14; 15; 16], RIS and multi-antenna are introduced into AMBC signal detection in our proposed method; furthermore, the signal detection problem is converted into the optimization problem of the RIS phase shift matrix and receiving antenna combination coefficient, and then deep reinforcement learning without data labels is presented to effectively solve the optimization problem, which improves the signal detection performance of AmBC system.
## 3 Geocasting Based on Local Coverage Areas  
In this section we briefly discuss the design of distributed coverage area construction that is proposed in our previous work [5, 6]. Throughout this work, it is assumed that one or more sinks are deployed within the wireless sensor network and each sensor node is logically grouped with only one sink based on a given metric, e.g., shortest path. Basically, the routing strategy of the wireless sensor network determines which node reports to which sink. Before building the coverage areas, the network forms routing trees for each sink based on a given metric. In this work, we consider Voronoi decomposition (explained in Section 2.3) for partitioning of the network between sinks in which the underlying routing metric is the shortest path [4]. In the shortest path routing, a _child node_ selects the neighbor that has the minimum hop count to a sink as the _parent node_ (_i.e._, the intermediate node) in order to get messages towards the sink. The construction of local coverage areas starts after the routing trees are created.
# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 4 Results
### Performance Comparison with Existing Feature Selection Methods  
The classification accuracy of the proposed model was compared with classification accuracies obtained after feature selection using state-of-the-art feature selection methods. The results obtained are depicted Figure 6. The results show that the proposed feature selection model produced better results for the datasets taken into consideration as compared to existing feature selection methods, namely VectorSlicer, RFormula, ChiSqSelector, UnivariateFeature Selector, and VarianceThreshold. The proposed model makes use of the accuracy values in order to determine the optimal feature set as opposed to using statistical methods to calculate the feature set; therefore, it acts as a good model for feature selection for classification tasks.
## 4 Method
### Flood Potential Index  
In this study, FPI is applied based on the GRACE-TWSA to improve flood monitoring ability. A diminished storage deficit coupled with intensified precipitation significantly amplifies the likelihood of flooding, indicating that the occurrence of floods hinges upon the disparity between extreme precipitation levels and the augmentation in water storage. Subsequently, the flood potential amount (FPA) is expressed as follows:  
\[FPA_{(t)}=P_{(t)}-S_{D(t)} \tag{7}\]  
where \(FPA_{(t)}\) denotes the flood potential amount at time \(t\), indicating the potential for flooding based on water availability. \(P_{(t)}\) denotes the total precipitation at time \(t\), which serves as the primary input contributing to the flood potential. \(S_{D(t)}\) denotes the regional storage deficit at time \(t\), calculated as the difference between the maximum possible storage capacity and the actual storage level, reflecting how much additional water the system can accommodate before flooding occurs. This equation effectively quantifies the relationship between precipitation and storage capacity, providing a critical measure of flood potential by assessing how much precipitation exceeds the capacity of the system to store water. Furthermore, the \(FPI\) is expressed as follows:  
\[FPI=\frac{FPA_{(t)}}{\max(FPA_{(t)})} \tag{8}\]  
All FPI values are found to be less than or equal to 1. When the FPI approaches 1, it indicates a substantial disparity between precipitation and regional water storage variations, suggesting a heightened probability of flooding.
# Full Field Inversion in Photoacoustic Tomography with Variable Sound Speed
###### Abstract  
To accelerate photoacoustic data acquisition, in [[PERSON], [PERSON], [PERSON], [PERSON] (2010). Full field detection in photoacoustic tomography. Optics express, 18(6), 6288-6299] a novel measurement and reconstruction approach has been proposed, where the measured data consist of projections of the full 3D acoustic pressure distribution at a certain time instant \(T\). Existing reconstruction algorithms for this kind of setup assume a constant speed of sound. This assumption is not always met in practice and thus can lead to erroneous reconstructions. In this paper, we present a two-step reconstruction method for full field detection photoacoustic tomography that takes variable speed of sound into account. In the first step, by applying the inverse Radon transform, the pressure distribution at the measurement time is reconstructed point-wise from the projection data. In the second step, a final time wave inversion problem is solved where the initial pressure distribution is recovered from the known pressure distribution at time \(T\). We derive an iterative solution approach for the final time wave inversion problem and compute the required adjoint operator. Moreover, as the main result of this paper, we derive its uniqueness and stability. Our numerical results demonstrate that the proposed reconstruction scheme is fast and stable, and that ignoring sound speed variations significantly degrades the reconstruction.  
photoacoustic tomography; full-field detection; wave equation; final time inversion; uniqueness; stability; iterative reconstruction Article
# Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers
## 2 Methods  
### Principle of Phase Detection Using Interpixel Crosstalk  
The concept of phase detection is based on a recently proposed method [19]. This method takes advantage of the interpixel crosstalk produced by spatial filtering on the Fourier plane of an input image. This interpixel crosstalk causes the light wave of each pixel to leak into the other pixels and interfere near the pixel boundaries. We assume that reference pixels with known phases are appropriately introduced in the page data. In this case, the signal phase can be determined from the intensity distribution at the pixel boundaries. For example, in the case of a four-level phase signal, four different known phase pixels are placed around the signal pixel, as shown in Figure 1. Among the light intensities at the pixel boundaries above, below, left, and right of the signal pixel, we determine the known phase pixel that exhibits the minimum boundary intensity. Because such a minimum boundary intensity results from a destructive interference, the signal phase can be determined as the opposite of the known phase. This conventional phase-determination algorithm is called the minimum-finding algorithm.  
Figure 1: Arrangement of the known phase-reference pixel in a four-level phase signal. \(\phi_{\mathrm{s}}\) represents the signal phase.  
However, not only do the light waves that reach the pixel boundaries come from adjacent pixels, but sufficient light waves may also leak from distant pixels, depending on the size of the aperture for spatial filtering. Because this light wave leakage from distant pixels is not considered in the previous minimum-finding algorithm, signal-detection errors may occur.  
In the present study, supervised machine learning is employed to consider the influence of these distant pixels. Figure 2 shows that the intensity distributions of the \(3\times 3\) pixels that surround the target signal are extracted from the intensity image and trained using the signal phase as the label. Because the extracted light-intensity distribution contains information on the light waves coming from the surrounding pixels as well as the adjacent known phase pixels, this learning corresponds to learning the influence of distant pixels.
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods
### Internet of Things
#### 2.1.3 Network Topology  
An IoT network is a representation of the distribution of its nodes in space and how they interconnect. The most used topologies are point-to-point, star, and mesh, as depicted in Figure 2 [18].  
Point-to-point (P2P) is the simplest topology and has only two nodes. For this topology to work, none of the elements (the two nodes and the link between them) can fail, which is a tough constraint.  
The star topology is one of the most common, since it is scalable and easy to deploy. In this topology, all nodes are connected to a central device that acts as a gateway. The nodes cannot communicate with each other, but only with the gateway. It is possible to add and remove nodes from the network without breaking it. It has the disadvantage of having a single point of failure (the gateway).  
The mesh topology is not as common as might be assumed and is more expensive to deploy. All nodes are connected to each other, which causes redundancy in the connections. It also allows the network to work in case of a connection failure (because of the redundancy). Nevertheless, it needs more computational power and more memory than the star topology to move data from one node to the other [19; 20].
## 4 Discussion  
### Statistical Analysis on Classification Accuracy  
Statistical analysis was conducted to verify the effectiveness of LSSC compared with other selection criteria. The \(t\)-test was used as a statistical analysis tool to identify whether the difference between classification accuracies by the LSSC and others was significant [43]. The dataset of classification accuracies was constructed for each selection criterion and used in the analysis. Each problem was validated via LOSOCV in the Results section; the data of one of the subjects were used for test data, and the results of all subjects were averaged for each problem. The classification accuracies of all selection criteria were divided by the optimal value for the same tested subject in each problem to normalize the range of the accuracies from zero to one. The normalized classification accuracies from all nine problems were included in the dataset of each selection criterion. The dataset of the LSSC was compared with others using the \(t\)-test, and \(p\)-values were calculated (Table 5). When the calculated \(p\)-value is lower than 0.01, it can be considered that the null hypothesis of two groups is rejected at the confidence level of 99%. The \(p\)-value was lower than 0.01 in all cases in Table 5, and statistically significant differences could be verified between the classification accuracies by the LSSC and others. This means that the LSSC was clearly more effective than others in the nine problems on physiological data.
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 2 Materials and Methods
### Collection of NIR Spectra  
Near-infrared (NIR) spectra were collected from the samples using a MicroNIR OnSite handheld spectrometer (Viavi; Santa Rosa, CA, USA), operating between 908-1676 nm (6 nm resolution). This instrument uses a linear variable filter as the dispersing element coupled to a linear detector array (128-pixel uncooled InGaAs photodiode array) [24]. The spectra integration time of the instrument was set to 100 ms.  
Two spectra were collected from two opposing sides of each individual chili, for a total of four spectra per chili sample (n = 400 spectra in total). In order to ensure consistency in the spectral acquisition location and minimize the prospect of background interference, chili samples were laid on a ceramic plate, and all spectra were collected approximately 3 cm down from the stem of the chili. In addition, reference black and white spectra were obtained to calibrate the instrument for every 10 samples. Spectra were exported in ASCII (*.csv) format.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 2 Related Work
### Feature Embedding-Based Methods  
Feature embedding-based methods aim to determine a feature distribution that can distinguish between normal and anomalous samples. Typically, these methods use a pre-trained network as a feature extractor to extract shallow features from images. By fitting normal sample features to a Gaussian distribution, Mahalanobis distance is a common method to calculate the anomaly scores [7; 17] between the test set samples and the Gaussian distribution, to estimate the anomaly localization. Research in [8] employed a coreset-subsampled memory bank to ensure low inference cost at higher performance. Some studies attach a normalizing flow module to the feature extractor [10; 11; 18], features were first extracted and a normalizing flow module was utilized to enable transformations between data distributions and well-defined densities. Subsequently, anomaly detection and localization were performed based on the probability density of the feature map.  
In general, feature embedding-based methods have achieved better results on the MVTec AD [1] dataset than those of the reconstruction-based methods because of their powerful representation capability of deep features. However, they rely on the uniformity of an object's location, which makes optimization difficult for cases in which the object's position varies significantly.
## 5 Conclusions  
This work contributed to testing a novel method for the industrial CFD prediction of the droplet aerobreakup process while providing an insight into the mist formation and development, which is of particular interest for engineering design. The aerodynamic breakup of a liquid droplet was numerically simulated adopting a physically consistent and computationally efficient multiscale approach for two-phase flows. In contrast to our previous study [19], a hybrid VOF-Lagrangian model was implemented allowing us to simulate the dynamics of the small liquid fragments. Moreover, the mean turbulent compressible flow was approximated following the SAS-URANS approach leading to the better prediction of unsteady turbulent structures. The breakup features observed in the experiments which are deformation, fragmentation, and dispersion of the produced mist were reproduced with acceptable accuracy by the hybrid method, without being computationally cumbersome. The effectiveness of the proposed method was demonstrated by making both qualitative and quantitative comparisons with reference solutions provided by experimental visualizations and high-fidelity numerical simulations. That highlighted the benefits of the proposed relatively simple CFD model, which is also able to predict the characteristics of the secondary droplets within the produced mist.  
As to future perspectives, the possibility of applying more sophisticated models based on wavelet multiscale methods [45, 46] is under study. Very recently, this innovative approach employing the adaptive wavelet collocation method to solve the energetic flow structures on dynamic grids has been further developed for supersonic flows [47, 48, 49], and novel wavelet-based adaptive URANS models [50, 51] might be explored in the present context [28].  
Conceptualization, G.D.S.; methodology, V.R. and G.D.S.; validation, V.R. and G.D.S.; investigation, V.R.; resources, G.D.S.; data curation, V.R.; writing--original draft preparation, G.D.S.; writing--review and editing, G.D.S.; visualization, V.R.; supervision, G.D.S. All authors read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank [PERSON], University of California at Santa Barbara, for kindly providing the experimental images reported in Figure 4. The authors acknowledge the CINECA award under the ISCRA initiative (Project HP10 CSFGMV) for the availability of high performance computing resources.  
Figure 10: Percentage distributions of DPM particle sizes (**left**) and particle speeds (**right**) at two different time instants that are \(t^{*}=1.75\) (red and blue) and \(t^{*}=2\) (cyan and green).
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 3 Results  
### GPS Signal Source Used  
First, this paper takes the GPS L1 CA code as the main verification object, and the data is divided into two types: highly dynamic and static. Given the particularity of highly dynamic data, this study adopted two methods, namely, self-generation and navigation signal simulator acquisition.  
Three GPS signal sources were used in this study. The first one was generated from a self-designed signal generator and is described in detail in Section 3.2.1. The second signal source was generated by the Spinrent GSS8000 GNSS signal simulator. OLinkStar NS210M was used as the sampler, the corresponding intermediate frequency was 4.123968 MHz, the sampling frequency was 16.367667 MHz, and the sampling bit width was set to 2 bits. The final source is a low dynamic signal measured from an outdoor antenna. The measured data's bit width was 8 bits, the sampling frequency was 38.192 MHz, and the IF was 9.548 MHz.
## 3 Results  
### Spectral Analysis of Effect of Catch Weight and Gear Design on the Codend Drag  
The three codends' temporal drag forces at various catch weights are shown in Figure 5. The temporal evolution of the drag force for all the three codend presents oscillation in a quasi-periodic nature and highlights the codend motions on the codend drag forces. At the empty stage, the drag force of the three codends oscillates continuously over time. In this case the 2p codend (codend 3) had a greater drag force than those of 4p codends (codends 1 and 2). At the catch stage, the drag force fluctuates greatly over time. The more the catch weight and number of netting panels increases, the more the oscillation amplitudes of the drag force increase. Indeed, for each codend, the maximum amplitudes of the codend drag oscillations were 0.15 N, 0.41 N, 0.63 N, and 0.74 N at the catch weight of 0, 7.4, 14.8, and 22.3 kg, respectively. While, at the same catch weight, the maximum amplitude of drag oscillations for the codons 1, 2, and 3 was 0.71N, 0.61N, 0.74N, respectively. It was observed that the amplitude oscillations of the temporal drag of the 2p codend is greater than the one of 4p codends (codends 1 and 2) due to its greater instability and motions.  
The PSD obtained using the temporal drag data of the three codends decreases as the frequency increases at all catch weights (Figure 6). The PSD results of the temporal drag reveal that the highest power spectrum peak is achieved at a very low frequency for all the three codends at all catch weights. Indeed, at the same catch weight, the maximum PSD peak is obtained at the frequencies of 0.21-0.28 Hz, 0.033-0.41 Hz, and 0.016-0.57 Hz for the codends 1, 2, and 3, respectively. Note that the highest frequency peaks of the temporal drags for codends 2 and 3 were obtained at the very low-frequency compare to coded 1 because of the greater unsteady motions of these codends. When regarding the raw spectra, at the empty stage, the maximum PSD amplitude of the 2p codend (coded 3) is 79.72% and 54.72% greater than that of codons 1 and 3, respectively. At catch stage, the maximum spectra amplitude of the 2P codend is more than fifty and seventy times lower than that of temporal drag spectra for the codends 1 and 2, respectively (see Figure 6). There is significant difference in the oscillation frequency of each codend (\(p<0.05\)). The PSD results for the drag force also show that the power spectrum obtained for all the codends exhibits quasi-periodic oscillations.  
Figure 5: Time evolution of the three codend drag forces at different catch weight and at \(u_{0}=0.9\) m/s.