esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Magnetic Data Analysis
### Quantitative Interpretation of Carmel Magnetic Anomaly  
The Carmel anomaly is located on the western edge of the Galilean-Lebanon terrane (Figure 3). The magnetic analysis of Carmel anomaly has been carried out by applying the advanced developed methodologies of tangents and characteristic points (Figure 5). The depth of the upper edge of this anomalous target is about 6.5 km, and the average magnetization is about 3000 mA/m. It is vital that the determined position of the magnetization vector does not coincide with the normal one and is vertical (90\({}^{\circ}\)). Later, the validity of the obtained model was confirmed by the results of 3D combined modeling of the magnetic and gravitational fields. Geologically, this model is interpreted as an uplift of the magnetic crystalline basement [44]. An exciting fact confirming the modeling data is the coincidence of the apical part of the Carmel magnetic anomaly, with the zone of the highest hypsometric marks location of this ridge reaching 528-546 m (see Figure 5).
## 4 Conclusions  
This article systematically derived a method of measuring the absolute pose of a moving rigid body using a monocular camera based on a camera imaging model. A turntable test was conducted to verify that the proposed method could accurately measure position and attitude in repeated measurements within the visual range of the camera. The mean absolute error in the attitude angle measured at different camera positions varied by no more than 0.2 degrees. The error in the attitude angle measured by the camera at the first camera position increased with the tilt angle of the rigid body relative to the camera lens. The mean absolute error was 0.8092 degrees, the standard deviation was 0.6623 degrees, and there were individual errors up to 2.5 degrees. An analysis of the error in the coordinates of the center of mass position showed that the deviation of the measurements across the three camera positions was small, the maximum deviation of the mean absolute error was 0.53 cm, and the maximum deviation of the standard deviation was 0.66 cm. The measurement was thus insensitive to the camera placement, and repeated measurements could be made. Although the proposed method requires the placement of two sets of marker points, which may be infeasible, it is suitable for non-professional and low-cost measurement applications.  
Conceptualization, S.G.; methodology, S.G. and Z.Z.; validation, S.G. and L.G.; formal analysis, Z.Z.; investigation, M.W. and L.G.; resources, Z.Z.; writing--original draft preparation, S.G. and M.W.; writing--review and editing, L.G. and Z.Z.; visualization, S.G. and L.G.; supervision, Z.Z. and M.W.; project administration, S.G.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Common Key Technology R&D Innovation Team Construction Project of Modern Agriculture of Guangdong Province, China (grant no. 2021 KJ129).  
Not applicable.  
Restrictions apply to the availability of the data. Data can be obtained from the first author.  
The authors declare no conflict of interest.
## 2 Method
### UNet Network for Projection Domain Correction  
The UNet architecture is purposefully engineered to address artifact issues within projection data. This encoder-decoder network, which has a symmetric structure, enables feature reutilization via skip connections, thus contributing to the mitigation of information loss at deeper network levels [21]. The UNet architecture adopted in this study comprises five downsample layers and five upsample layers. The convolutional kernel size of the DownSample module is \(3\times 3\), the stride is 2, and the padding is 1. The convolutional kernel size of the UpSample module is \(1\times 1\), the stride is 1, and the padding is 0. With respect to the input projection data, that is, sinograms, UNet processes them through sequential convolution and pooling operations to encode and distill key features, followed by decoding through the corresponding transconvolution layers, reverting them to output projection data with preserved dimensions. This method proficiently reduces artifacts while maintaining the essential information in the projection data.
## 4 Analysis of the Natural Frequency of Two Boring Bar Models
### Comparison of Natural Frequency for Different L/D Ratios  
To compare the natural frequencies of the shear boring bar and non-shear boring bar for different L/D ratios, we fixed the inner radius \(R_{i}\) and the outer radius \(R_{o}\) of the boring bar at 0.043 and 0.045 m, respectively. Moreover, based on the same stacking sequences \([\theta]_{8}\), the ply angle \(\theta\) was set with values ranging from \(-90^{\circ}\) to \(90^{\circ}\). The length of the boring bar was set to 0.4, 0.6, and 0.8 m. The value of \(\omega_{s}\) of the shear boring bar was calculated according to the AMDM method. The value of \(\omega\) of the non-shear boring bar was calculated using Equation (27). Based on these two calculation methods, we use the MATLAB software to calculate the variation of the natural frequencies of the shear and the non-shear boring bars. The resulting values of \(\omega\) are shown in Figure 4, in which \"L/D\" with subscript \"S\" denotes the shear boring bar values and \"L/D\" without the subscript \"S\" denotes the non-shear boring bar values.  
Figure 4 shows that the natural frequency curves of the two kinds of boring bar models are symmetrically distributed on both sides of the ply angle of \(0^{\circ}\). The natural frequency curves are different for different L/D ratios. For the same bar, a smaller L/D ratio (L/D = 4.4) yields a larger natural frequency. The larger the ratio is, the lower the natural frequency becomes. Moreover, comparing the two composite boring bar models, the natural frequency curves of the shear boring bar model are below the corresponding non-shear boring bar curves for the same L/D ratio. The closer the ply angle to \(0^{\circ}\), the greater the natural frequency difference between them is. However, if the L/D ratio is large and the ply angle is near \(\pm 90^{\circ}\), the frequencies of the two models are almost equal to each other. Therefore, we can conclude that, when the influence of the shear deformation and rotational inertia are considered, the natural frequency of the shear boring bar model is reduced. The smaller the L/D ratio of the composite boring bar, the smaller the ply angle is, and the greater the influence of the shear deformation on the natural frequency is.  
Figure 4: Comparison of natural frequencies for different L/D ratios.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
## 2 Permanent-Magnet Halbach Array Design
### Halbach Array Configurations
#### 2.2.2 Cylinder Halbach Array Configuration  
In the cylindrical Halbach array configuration, the permanent magnets are arranged in a circular pattern with their magnetization adjusted to direct the field along the axis. The magnets are oriented to focus the magnetic field along the cylinder's central axis with  
\begin{table}
\begin{tabular}{c c} \hline
**Magnet Property** & **Value Range** \\ \hline Remanence (Br) & 1.0–1.4 T \\ \hline Coercivity (Hc) & 800–2000 kA/m \\ \hline Maximum Energy Product (BHmax) & 200–440 kJ/m\({}^{3}\) \\ \hline Curie Temperature (Tc) & 310–400 \({}^{\circ}\)C \\ \hline Temperature Coefficient of Remanence & \(-\)0.09\% to \(-\)0.12\%/\({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: Properties of permanent magnet (NdFeB).  
Figure 2: Permanent magnets arranged in a linear Halbach array configuration.  
minimal leakage outside the cylinder. The direction of magnetic orientation in a conventional cylindrical Halbach configuration is shown in Figure 3.
## 2 Results
### Expression of Genes Encoding neuropeptides  
Neuropeptides can modulate nematode behavior, including locomotion and chemo-attraction [29]. In _C. elegans_, 31 genes encode _flp_ (FMRFamide-like peptides) and 75 genes encode _nlp_ (neuropeptides-like proteins) [28]. The _M. incognita_ neuropeptide complement is reduced compared to that of _C. elegans_ with only 19 _flp_ genes and 21 _nlp_ genes readily identifiable [26]. A limited number (10) of _M. graminicola_ genes encoding putative _nlp_ or _flp_ were identified in our transcriptomic dataset (Table S4). They shared the same expression patterns in resistant plants: they were overexpressed in pre-parasitic J2s and until 4 dpi, then their expression decreased at 8 dpi. However, as for CWDEs-encoding genes, _nlp_ or _flp_ genes expression lasted more in resistant plants than in susceptible plants and was significantly higher at 8 dpi (Figure 2(b)). Conversely, we noticed that genes encoding putative neuropeptide receptors (homologs of genes classified as \"_npr_\" in _C. elegans_) showed no significant alteration of their expression across the early stages of _M. graminicola_ parasitism (Table S4).  
Figure 2: Number of _M. graminicola_ Differentially Expressed Genes when comparing nematode infection of resistant (R) versus susceptible (S) rice plants (“DEG-RvS”), at 2 dpi (Tog2 vs. Nip2), 4 dpi (Tog4 vs. Nip4) and 8 dpi (Tog8 vs. Nip8). (**a**) Number of “DEG-RvS” at each time point. (**b**) Venn diagram showing specific and common “DEG-RvS” at each time point.  
Figure 3: Schematic representation of the gene expression patterns of _Meloidogyne graminicola_ differentially expressed genes (DEGs) encoding putative (**a**) cell wall-degrading enzymes (CWDE) (GH5: endoglucanase, GH30: glucuronoxylanase, GH28: polygalacturonase), (**b**) neuropeptides (_nlp_: neuropeptide like-proteins, _flp_: FMRamide-related peptide-like), (**c**) molting-related proteins (_col_: cuticle collagen proteins, _cut_: cuticle, _cutl_: cuticlin-like, _nas_: zinc metalloproteinase, _mlt_: molting-defective, _mltn_: mlt10-related). Each row of the heatmap represents a gene and each column represents a stage of nematode development (J2: pre-parasitic J2s, Nip: in susceptible rice plants, Tog: in resistant rice plants) either at 2, 4 or 8 dpi. The common color key is given on the top of the figure. Values are expressed as Log2 of normalized Counts Per Million (CPM).
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Interpretation of Result  
#### 4.5.1 Model Evaluation Index  
Accuracy, precision, and recall rates are the evaluation indexes of deep learning-based fault diagnosis models [23]. They are also used to evaluate the detection effect of diagnosis models. The accuracy rate is the ratio of the number of samples correctly classified by the classifier to the total number of samples. The precision rate is the ratio of the number of correctly classified samples to the number of predicted classified samples. The recall rate is the ratio of the number of samples correctly classified into the labels to the actual number of samples on the labels [24]. The formulas are shown in Equations (12)-(14).  
\[A\mathrm{cc}=\frac{T_{\mathrm{p}}+T_{N}}{T_{P}+F_{P}+T_{N}+F_{N}} \tag{12}\]  
\[P=\frac{T_{P}}{T_{P}+F_{P}} \tag{13}\]  
\[R=\frac{T_{P}}{T_{P}+F_{N}} \tag{14}\]  
where \(T_{P}\) is the number of correctly classified samples of a certain type of sample A, \(T_{N}\) is the number of correctly classified samples of other types of samples, \(F_{P}\) is the number of other type samples incorrectly classified as \(A\), and \(F_{N}\) is the number of incorrectly classified samples of a certain type of sample A.
## 5 Indoor and Outdoor Application Testing  
The proposed portable wideband directional antenna scheme was tested for the indoor and outdoor reception of digital terrestrial television signals in the 470-862 MHz frequency range. This research was conducted in Thailand, which adopted the DVB-T2 standard for digital terrestrial television broadcasting. As a result, the indoor and outdoor tests were carried out within the 470-862 MHz frequency band. The transmitting station is located in Thailand's capital Bangkok at the coordinates \(13^{\circ}\)45\({}^{\prime}\)16\({}^{\prime\prime}\) N \(100^{\circ}\)32\({}^{\prime}\)24\({}^{\prime\prime}\) E, with an effective isotropic radiated power (EIRP) of 50 kW. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specifics**} & \multicolumn{3}{c}{**Simulation**} & \multicolumn{3}{c}{**Measurement**} \\ \cline{2-7}  & **470 MHz** & **680 MHz** & **890 MHz** & **470 MHz** & **680 MHz** & **890 MHz** \\ \hline \(|S_{11}|<-10\) dB  Bandwidth, \% & & (468–907), 63.85\% & & & (424–943), 75.93\% & \\ \hline Gain (dBi) & 3.45 & 4.07 & 5.21 & 2.69 & 3.75 & 4.84 \\ \hline HPBW in xz-plane, (deg.) & 149 & 194 & 185.1 & 150 & 159 & 160 \\ \hline HPBW in yz-plane, (deg.) & 99 & 92.8 & 84.2 & 102 & 78 & 102 \\ \hline Cross-pol. in xz-plane (dB) & & \(\leq\)\(-40\) & & & \(\leq\)\(-20\) & \\ \hline Cross-pol. in yz-plane (dB) & & \(\leq\)\(-40\) & & & \(\leq\)\(-40\) & \\ \hline Back lobe level (dB) & & \(\leq\)\(-10\) & & & \(\leq\)\(-9\) & \\ \hline Radiation pattern & & Unidirectional & & Unidirectional & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulated and measured performances of the portable wideband directional antenna scheme.
# 3D-STARNET: Spatial-Temporal Attention Residual Network for Robust Action Recognition
###### Abstract  
Existing skeleton-based action recognition methods face the challenges of insufficient spatiotemporal feature mining and a low efficiency of information transmission. To solve these problems, this paper proposes a model called the Spatial-Temporal Attention Residual Network for 3D human action recognition (3D-STARNET). This model significantly improves the performance of action recognition through the following three main innovations: (1) the conversion from skeleton points to heat maps. Using Gaussian transform to convert skeleton point data into heat maps effectively reduces the model's strong dependence on the original skeleton point data and enhances the stability and robustness of the data; (2) a spatiotemporal attention mechanism (STA). A novel spatiotemporal attention mechanism is proposed, focusing on the extraction of key frames and key areas within frames, which significantly enhances the model's ability to identify behavioral patterns; (3) a multi-stage residual structure (MS-Residual). The introduction of a multi-stage residual structure improves the efficiency of data transmission in the network, solves the gradient vanishing problem in deep networks, and helps to improve the recognition efficiency of the model. Experimental results on the NTU-RGBD120 dataset show that 3D-STARNET has significantly improved the accuracy of action recognition, and the top1 accuracy of the overall network reached 96.74%. This method not only solves the robustness shortcomings of existing methods, but also improves the ability to capture spatiotemporal features, providing an efficient and widely applicable solution for action recognition based on skeletal data.  
action recognition; spatiotemporal attention; multi-staged residual; skeleton; 3D CNN +
Footnote †: journal: Applied Sciences
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods
### Types of C-ITS Messages
#### 2.1.2 Decentralized Environmental Notification Messages (DENMs)  
DENMs are designed to convey environmental information related to traffic events in a decentralized manner. Each DENM consists of a common ITS PDU header followed by several containers that together form the DENM payload. The structure of a DENM, shown in Figure 2, is as follows [24]:  
* ITS PDU Header: This header includes information about the protocol version, message type, and Intelligent Transport System Station (ITS-S) ID of the originating ITS-S.
* DENM Payload: The payload comprises four primary containers, which are transmitted in a fixed sequence:
* Management Container: Contains information pertinent to the management of the DENM, including action IDs, detection times, reference times, and other elements such as termination, event position, relevance distance, relevance traffic direction, validity duration, and transmission interval.
* Situation Container: Provides details about the event type and its context. It must include the informationQuality and eventType elements and may include linked-Cause and eventHistory. The informationQuality value ranges from 1 (lowest) to 7 (highest), while eventType includes causeCode and subCauseCode to describe the specific event.
* Location Container: Describes the event's location. It must include traces and may also include optional elements such as eventSpeed, eventPositionHeading, and roadType.
* a la Carte Container: This container is used to include additional application-specific data not covered by the other containers. It can include elements such as lanePosition, impactReduction, externalTemperature, roadWorks, positioningSolution, and stationaryVehicle, depending on the use case.  
Each container in a DENM has specific requirements regarding its presence and the information it must or may contain. The management container and ITS PDU header are mandatory, while the situation, location, and a la carte containers are optional and included based on the specific needs of the message and the context of the event.  
DENMs are used to alert road users of detected events and are processed by ITS applications to provide relevant information and warnings. The detailed specifications for each container and its elements are outlined in ETSI EN 302 637-3 [25], providing comprehensive guidelines for effective communication within C-ITS environments.  
Figure 2: DENM structure [25].
## 4 Discussion
We can estimate the minimum number of measurements needed to ensure a measurement tolerance (MT) that is the same as the instrument's axial resolution, using the expression for MT, \((MT=(1.96\cdot Sw)/\sqrt{N})\) for N number of measurements according to the ISO standards [35; 36]. Based on the present results, for the measurement of GCL, IPL, INL, OS, and RPE in the ETDRS sectors, only one measurement is enough. Based on the sector that had the worst repeatability, a larger number of measurements will be needed for RNFL, OPL and ONL (9, 4, and 8, respectively). However, based on the sector with second worst repeatability, the number of measurements needed for these layers reduce significantly to 2, 2, and 6, respectively. The higher number of measurements needed for ONL reflects the difficulties in segmenting this layer as discussed earlier. For pathologies where the measurement of ONL is important, we need to take this factor into consideration while planning the measurement protocol. Reliability of the quantitative measurement from OCT is very important as these measurements are used for the screening, diagnosis, and follow up of various retinal and neural disorders. OCT has been shown to be useful in detecting and monitoring changes in individual retinal layer thickness in diseases like Glaucoma [7], age related macular degeneration [4; 37], diabetic retinopathy [8], inherited retinal disorders [16; 38], and neurological disorders [5; 10; 13]. Knowledge on the repeatability of OCT for individual retinal layer measurements is important for clinicians. This is needed to differentiate if the observed changes in the retinal thickness are due to measurement fluctuations or actual pathological changes. The limitation of the present study is that only healthy eyes were analyzed. For disease follow up, we need to know the repeatability on pathologic eyes, and further studies on individual layer thickness repeatability are recommended.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AE Autoencoder BCE Binary cross-entropy BSE Back-scattered electron CCE Categorical cross-entropy CCVAE Conditional convolutional autoencoder CNN Convolutional neural network DA Discriminant analysis DL Deep learning DT Decision Tree FC Fully-connected GAN Generative adversarial network GPU Graphics processing unit K-NN K-nearest neighbours LDA Linear discriminant analysis MAE Mean absolute error ML Machine learning MSE Mean square error QDA Quadratic discriminant analysis RBM Restricted Boltzmann machine ReLU Rectifier linear unit RNN Recurrent neural network ROI Region of interest SCCE Sparse categorical cross entropy SE Secondary electron SEM Scanning electron microscope SGD Stochastic gradient descent SOM Self-organising maps STEM Scanning transmission electron microscopy SVM Support vector machine VAR Variational autoencoder YOLO You only look once
## 4 Numerical Results and Discussions
### The Effects of Parameters on \(R_{\min}\)  
The efficiency of the parameter settings in the proposed technique is investigated according to the following figures.  
Figure 2 depicts the influence of the interference tolerance threshold on the max-min cognitive user achievable rate. The simulation parameters are \((d_{1},d_{2})=(3,7)\) m, \(P_{1,\max}=(20,15)\) dBm, \(P_{2,\max}=15\) dBm, \(\sigma_{u}^{2}=\{10^{-6},10^{-4}\}\) W. It is observed that when \(Q\) increases to a certain value, \(R_{\min}\) initially increases and eventually saturates because the transmission rate of the cognitive user is a monotonically increasing function of transmit power \(P_{i}\) which is affected by \(Q\) and the peak power constraint. When \(Q\) is sufficiently small, the transmit power is mainly limited by \(Q\). When \(Q\) increases to a certain value (larger than the peak power), the transmit power is determined mainly by the peak power. Thus, increasing \(Q\) cannot continuously improve the achievable sum-rate.  
Figure 3 depicts the the max-min achievable rate versus the peak power of source \(S_{1}\) when \((d_{1},d_{2})=(3,7)\) m, \(Q=(5,10)\) dBm, \(P_{2,\max}=15\) dBm and \(\sigma_{u}^{2}=\{10^{-6},10^{-4}\}\). The result shows that \(R_{\min}\) increases and tends to saturate to a stable value as \(P_{1,\max}\) increases to a certain value. This is because \(R_{\min}\) is proportional to the user's transmission power, and the cognitive user transmission power is affected by \(Q\) and \(P_{i,\max},i=1,2\) at the same time. When \(P_{1,\max}\) is small, the user's transmit power is limited by \(P_{1,\max}\). Thus, \(R_{\min}\) increases as \(P_{1,\max}\). As \(P_{1,\max}\) increases to a certain value (larger than \(Q\)), however, the user's transmit power is limited by \(Q\). Thus, continuing to increase \(P_{1,\max}\) will not change \(R_{\min}\).  
Figure 4 depicts the max-min achievable rate obtained by the proposed JPCRA scheme versus the interference power \(\sigma_{u}^{2}\) caused by the primary user to cognitive user when \((d_{1},d_{2})=(3,7)\) m, \(Q=(5,10)\) dBm, \(P_{1,\max}=(20,15)\) dBm, \(P_{2,\max}=15\) dBm. It can be seen that \(R_{\min}\) decreases and approaches 0 as \(\sigma_{u}^{2}\) increases, and the descend degree of JPCRA is more gradual. Thus, consideration of the interference from the primary network is necessary to design the cognitive energy harvesting system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Description** & **Value** \\ \hline \(d_{i}\) & The distance between source \(S_{i}\) and relay \(R\) & 0–10 m \\ \(d_{iu}\) & The distances between source \(S_{i}\) and primary user PU & 5 m \\ \(m\) & The pass loss exponent & 2 \\ \(\sigma^{2}\) & The white noise power at R & \(10^{-9}\) W \\ \(\sigma_{u}^{2}\) & The interference caused by the primary network & \((10^{-10},1)\) W \\ \(P_{i,max}\) & The peak power of source node \(S_{i}\) & \((0,30)\) dBm \\ \(Q\) & The interference threshold & \((-10,30)\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters used in the simulation part.