esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid FSK-FDM Scheme for Data Rate Enhancement in Dual-Function Radar and Communication
## 2 Signal Data Model  
A signal data model is developed in the following sections for the DFRC design, using FSK and FDM techniques. The DFRC transmitter, the radar receiver, and the communication receiver are all equipped with uniform linear arrays (ULAs) using \(M_{T}\), \(M_{R}\), and \(N_{R}\) antenna elements, respectively. In general, all arrays maintain a half-wavelength spacing between the elements. In this paper, it is considered that the DFRC transmitter and radar receiver are placed so close to each other that they receive the same angle of radiation from each other. The transmitter array mainly generates pulses for detecting and tracking radar targets. The secondary objective of the transmitter array is to embed communication bits in the pulses without affecting the radar operation. The DFRC transmitting array steers the transmitted power within the main beam, where radar operation occurs. The \(M_{T}\times 1\) vector form of the baseband signal for the \(\tau\)th radar pulse at the input of the transmit antenna [29] is  
\[\mathbf{s}(t,\tau)=\lambda(\tau)\mathbf{w}^{*}(t)\mathbf{x}_{fdm}(t) \tag{1}\]  
where time within each radar pulse is represented by \(t\), and the pulse number is represented by \(\tau\). For each transmitted waveform, \(\lambda(t)\) determines how much power is assigned to each waveform such that the total transmitted power is fixed. This vector is primarily designed to focus the transmitted power inside the main beam of the radar while minimizing the power radiated outside the main beam. Similarly, \(\mathbf{w}(t)\) represents the uniform transmit array beam-forming weight vector with dimensions \(M_{T}\times 1\) for all waveform combinations, \((.)^{*}\) denotes the complex conjugate and \(\mathbf{x}_{fdm}(t)\) is the composite vector developed by adding multiple FSK-modulated symbols. Each FSK symbol represents two bits of information based on multiple orthogonal waveform combinations. More details about the construction of \(\mathbf{x}_{fdm}(t)\) are discussed in Section 3. It is assumed that the proposed waveform vectors must be orthogonal in order to be effective, but this is not necessarily true of the baseband signals \(\mathbf{s}(t,\tau)\).
## 3 Material and Methods
### Acquisition of 2D Images  
Three different 2D cameras (C922 Pro HD Stream Webcams, San Jose, CA, USA) were positioned at angles of 0, 45, and 135 degrees with respect to the subjects performing the gait. These three camera systems were first calibrated and synchronized with the VICON motion capture system before the recordings were captured. The detailed process of time-synchronization and calibration is discussed in Section 3.3. Three different camera systems recorded 2D video simultaneously at 60 Hz for every trial. The images were retrieved from the recorded videos for dataset preparation.
## Data and Methods  
### Data  
To analyze the access vulnerability to EMS by region in South Korea based on real-time road traffic flow speed, we established spatial big data including road and position information of relevant facilities (Figure 1). The facility information we established using spatial big data included community centers in urban and rural villages, emergency centers, 119-safety centers, community health centers, population and road network maps, as well as traffic speed flow data (Table 1).  
Figure 1: Location of provincial-level divisions in South Korea except for Jeju island. South Korea consists of 17 first-tier administrative divisions: 6 metropolitan cities, 1 special city, 1 special self-governing city, and 9 provinces. The gray and pink areas indicate rural and urban areas, organized sub-municipal level divisions (Dong (neighborhood in Urban) and Ri (Village in Rural).  
We used 17,771 addresses of community centers in urban and rural villages (as the facility point to represent the center of a village (3507 urban community centers and 14,264 rural community centers) as of 2019 [37]. Community centers, as landmarks of villages, are a space for culture, administration, and communication for village residents in the villages which are the sub-municipal level divisions (Dong and Ri) [38; 39; 40].  
We also acquired data on the addresses of the medical facilities including emergency centers, community health centers, and 119-safety centers. Data were provided by various sources: data on emergency centers were provided by the National Medical Center (NMC) [41], community health centers were provided by the Ministry of Health and Welfare (MHW) [42], and 119-safety centers were provided by the National Fire Agency (NFA) [43]. The addresses were taken from the Open Application Programming Interface (Open API) and converted into coordinate data through a geocoding method.  
We used traffic-related data such as road network maps and traffic speed information. The road network map was provided by the Intelligent Transport Systems (ITS) of the Ministry of Land, Infrastructure and Transport (MLIT) [44], and the traffic speed was provided by the Traffic and Road Information Service of MLIT [45]. The road network maps were based on 2019 data, and the traffic speed information was also acquired from the Open API of ITS from February to April in 2020.
## 2 Waste Glass as Resource for Construction Materials
### Glass Wastes as Aggregate Substitutions  
The use of CWG as aggregate materials (typically larger than 1.0 mm) has been widely investigated. Several studies reported a reduction of workability with the usage of CWG aggregates in the highest percentage proportion of substitution [12; 29; 30], while some other reports indicated there was no significant difference in the workability through a slump test [31; 32]. Although it is difficult to find agreement on the outcomes of several reports, there is somewhat of an agreement that partial glass substitution improved the binder action of cement, although there is no clarity on the relative effects of substituting coarse and fine aggregates [17; 33].  
The outcome of compression strengths of concrete with CWG will be the interplay between a glass's bond strength with the binder cement matrix. The influence of the recycled glass addition on the compression strength is rarely a straightforward explanation [34]. Some studies carried out on the compressive strength concluded that there are no significant effects or changes due to the usage of 20-30% recycled glass in the concrete [35]. However, a decrease in compressive strength was reported [23; 36] and was attributed to the weakening of the aggregate paste bond due to the addition of waste glass instead of coarse aggregate [36; 37]. On other hand, some other works [5; 38; 39; 40] indicated increased later age strength with partial substitutions of aggregates with CWG, although the early age strength is lower.  
Flexural strength of the glass substitutions for aggregates in concrete shows a similar trend with that of its compressive strength. It is found that flexural strengths increase to the maximum with the optimum addition of glass powder [10; 32; 39]. Generally, it is recorded that 4-11% higher flexural strength resulted with the 10-20% substitution of fine and coarse aggregates of CWGs [41]. A similar trend in splitting strengths is also reported on the effects of CWG substitutions for aggregates. It is found that splitting strengths increase by 4-12% with the CWG additions up to 20% [25]. The variability that accompanies the numerous reports of the effect of substitution on mechanical properties of concrete demands further clarification. In this work, we focus mostly on the effects of substituting fine aggregates with CWG sourced from a local waste processing plant.
## 5 Conclusions  
A simple, lightweight, and efficient Res-CNN structure was constructed to achieve the extraction of ROI-binarized hyperbolic regions from GPR images, facilitating swift and precise identification of target reflection areas. These are instrumental in enhancing our understanding and analysis of the distribution and burial depth of subsurface targets.  
Figure 15: ROI-binarized hyperbolic region extraction results from network architecture without a skip connection structure.  
The Res-CNN model comprises ten blocks and employs distinct activation functions within downsampling and upsampling blocks to fine-tune network functionality. Notably, the integration of skip connections ensures effective information propagation throughout the deep network, thereby enhancing segmentation accuracy.  
Different types of GPR anomaly label datasets were constructed using random media data and binarized images from homogeneous media, enhancing the network's ability to better adapt to hyperbolic features and yield accurate ROI results. Numerical simulations and field experiments demonstrate that the Res-CNNs efficacy effectively separates target regions from the GPR background. Meanwhile, the field data result also proves that the trained network has good ability and confirms the effectiveness and applicability of this algorithm for guiding practical interpretation.  
Looking ahead, the study presents an advanced and efficient tool for GPR image analysis, providing a simple and powerful solution for extracting target reflection regions. These advancements simplify the interpretation process and hold promise for improving subsurface imaging and target identification across various applications, such as geological surveys, infrastructure assessment, and more.  
Conceptualization, H.Z. and Q.D.; methodology, D.F.; software, H.Z.; validation, H.Z.; formal analysis, B.Z.; investigation, H.Z.; resources, X.W.; data curation, H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., and D.F.; visualization, H.Z.; supervision, Q.D.; project administration, Q.D.; funding acquisition, Q.D. and D.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grants 42374180, 42074161, 42104143, and 42174178, in part by Natural Science Foundation of Hunan Province, China under Grant 2021 JJ30806 and 2022J40584, in part by the Fundamental Research Funds for the Central Universities of Central South University under Grant 2020 zts171, and in part by the Hunan Provincial Postgraduate Research Innovation Project under Grant CX20200227.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data are part of an ongoing study.  
We are grateful for resources from the High Performance Computing Center of Central South University.  
The authors declare no conflicts of interests.
## 2 Materials and Methods
### Model Development  
The proposed model in Figure 6 combines a pre-trained MobileNetV2 architecture with a CfC-NCP-implemented layer to classify tomato leaf diseases. Initially, MobileNetV2, a widely used CNN architecture, extracts rich hierarchical features from input images. Pre-trained on the ImageNet dataset, MobileNetV2 efficiently learns diverse visual features, enhancing the model's capability while conserving computational resources.  
After feature extraction by the MobileNetV2 model as the CNN component, the 2 \(\times\) 2 \(\times\) 1280 features are reshaped, merging the first two dimensions into sequential data of size 4 \(\times\) 1280. These data are then fed into the CfC model, an RNN designed to capture temporal dependencies. In this configuration, the CfC layer has an input size of 32 with 128 neurons implementing NCP wiring, and the output size is 36. These configurations represent the optimized settings achieved in this research after extensive testing. This  
Figure 6: Proposed architecture for a hybrid CNN-RNN model that efficiently captures temporal and spatial relationships in images using pre-trained CNN and advanced neural network components CfC and NCP.  
allows for the model to efficiently capture disease patterns without compromising its ability to recognize unique patterns in rare cases. Finally, the output of the CfC-NCP layers is passed through a dense layer with SoftMax activation, generating class probabilities for disease classification. This structure leverages the strengths of CNNs for spatial feature extraction and RNNs for temporal sequence modeling, enabling the model to make accurate predictions regarding the presence of various tomato leaf diseases.  
The layered structure of the model is shown in Table 1; it begins with the MobileNetV2, which is a pre-trained model used as a feature extractor. This group of layers processes the input images and extracts high-level features, resulting in an output shape of (None, 4, 1280) and comprising 2,257,984 parameters.  
Next, the CFC layer, identified as cf_c_4, processes the reshaped feature maps. This layer, which includes NCP, captures temporal dependencies and transforms the input data from a shape of (4, 1280) to (36). This reduction in dimensionality is achieved with 217,568 parameters.  
Finally, a dense layer, referred to as dense_3, is used for classification. This fully connected layer classifies the processed features into different disease classes, with an output shape of (11) and containing 407 parameters.  
In summary, the model architecture utilizes MobileNetV2 for initial feature extraction, reshapes the features, processes them through the CFC layer to capture temporal dependencies, and classifies them into 11 disease classes with a dense layer.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 4 Discussion
### Effect of Incubation Temperature on Xylanase and Cellulase Production  
Incubation temperature is paramount for optimal enzyme production due to alterations in microbial protein structure and properties with temperature variations [44]. At temperatures below or above the optimum, metabolic activities are reduced, with consequent inhibition in growth and enzymes synthesis [11]. While some studies suggest higher xylanase production with increased temperature, others have suggested otherwise [45; 46]. In concordance with this study however, optimal xylanase production at 35 \({}^{\circ}\)C by _Streptomyces_ sp. F2621 has been documented [38], while another study reported 32 \({}^{\circ}\)C as optimum with significant reduction of over 50% xylanase productivity by _S. halstedii_ at 45 \({}^{\circ}\)C [45]. Previous studies have reported optimum temperatures of 30 and 26 \({}^{\circ}\)C for cellulase production by two _Streptomyces_ strains and _B. subtilis_ respectively [47; 48]. In addition, [PERSON] et al. [49] also reported optimal temperature of 30 \({}^{\circ}\)C for cellulase production by _Streptomyces_ sp. strain J2. Comparatively, a higher temperature of 50 \({}^{\circ}\)C for production of CMCase by _B. licheniformis_ MVS1 and _Bacillus_ sp. MVS3 has been reported [35]. Differences in optimal temperature for enzyme production could be attributed to strain variations between bacterial groups and adaptability to temperature dynamics [50].
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 5 Experimental Verification  
Since the gear motion in the planetary gearbox is a typical composite motion, the vibration signal is more complex than that of a fixed shaft gearbox. Moreover, the gear is the most fault-prone part of the planetary gearbox. Therefore, this paper takes the gear of the planetary gearbox as the diagnostic object to verify the effectiveness of the proposed method. ICEEMD-time-frequency information entropy method was utilized to extract the fault feature of the gear vibration data from a fault-prediction test bed. We used the power transmission fault prediction test bed (DPS) manufactured by Spectra Quest,  
Figure 5: The fault diagnosis procedure for a planetary gearbox.  
USA. The test bed is shown in Figure 6, which includes a centrally fixed sun gear, a planetary carrier and ring gear, and four planetary gears that change with the center of rotation of the sun gear. In addition, the planetary gearbox of this experimental bench was small in volume, and the vibration caused by the fault was weak, which increases the difficulty of diagnosis. In this experiment, a total of 12 operation conditions were collected. The specific operation condition information is shown in Table 2. The signal sampling frequency was 12,800 Hz and the signal sampling point was 524,288. The experimental fault data includes five states: gear tooth crack fault, gear wear fault, tooth breaking fault, gearing missing fault, and normal. In this study, experimental data were divided into 40 segments, and each segment had 12,800 points.
# A Robust Wind Turbine Component Health Status Indicator
###### Abstract  
Wind turbine components' failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine's component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component's health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component's status in a multivariate environment, providing the selected variables' joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling.  
wind turbine; Gaussian Mixture Copula models; failure index; health status indicator; cubic spline smoothing +
Footnote †: journal: Applied  
0000-0001-467-256
## 3 Shock Vibration Field Test
### Measurement Points Arrangement  
In order to study the vibration response and attenuation law of the ground and the ruins, combined with the specific conditions of the test site, seven positions were selected between the Chengshang Village ruins and the travel lane, and vibration monitoring points were arranged on the ground surface, and one horizontal pickup was installed at each measuring point, and its X direction was perpendicular to the direction of the travel lane. The distances from the monitoring points to the travel lane are 2.5, 5.0, 10, 20, 35, 50, and 70 m, respectively, and the plane locations of the monitoring points are shown in Figure 3.
# TGN: A Temporal Graph Network for Physics Prediction
## 3 Methodology
### Temporal Graph Network  
In order to capture both spatial and temporal dependences of physical systems, we propose a temporal graph network model (TGN) by fusing GSE and AutoLinear. The TGN model predicts the state field sequence after time step \(t\) in an autoregressive manner:  
\[\tilde{\mathbf{S}}_{t+1}=\text{TGN}(G,(\mathbf{S}_{1},\dots,\mathbf{S}_{t})),\dots,\tilde {\mathbf{S}}_{t+T}=TGN(G,(\mathbf{S}_{1},\dots,\mathbf{S}_{t},\tilde{\mathbf{S}}_{t+1},\dots, \tilde{\mathbf{S}}_{t+T-1})). \tag{12}\]  
During training, for each predicted time step \(k\), we use the error between the predicted state \(\tilde{\mathbf{S}}_{k}\) and the corresponding true state \(\mathbf{S}_{k}\) to supervise the TGN model in generating the predicted state \(\tilde{\mathbf{S}}_{k}\) for time step \(k\). The loss function of the T-GCN model is shown in Equation (13).  
\[\mathcal{L}_{k}=\|\mathbf{S}_{k}-\tilde{\mathbf{S}}_{k}\|_{2}^{2}. \tag{13}\]  
Compared with next-step models such as MeshGraphNets, our model not only utilizes graph neural networks to learn the topology of the mesh to capture spatial dependence, but also captures trending and periodicity information from historical time series. Modeling from both spatial and temporal perspectives, our model greatly mitigates the error accumulation and yields stable and accurate predictions.
## 2 Materials and Methods  
### Edible Mushrooms and LAB Strains Used for Fermentation  
_S. luteus_, _B. edulis_, _C. cibarius_, and _R. caperata_ mushrooms were collected in the Varenos Forests (Eastern Lithuania) in September of 2021. Images of the mushrooms are given in Figure 1. Fresh fruiting bodies were cleaned and boiled in water (\(1/1\); mushroom mass by water volume) for 5 min with 3% salt (the concentration of salt was calculated from the fresh mushroom mass) [6]. After boiling, mushrooms were cooled to room temperature (\(22\pm 2\)\({}^{\circ}\)C) and used for fermentation. Unfermented and unboiled mushrooms were analysed as control 1; boiled unfermented mushrooms were analysed as control 2. In total, 12 samples of mushrooms were prepared.  
Selected LAB strains were previously isolated from spontaneously fermented cereal, identified and characterised by [PERSON] et al. [12]. These LAB showed a broad spectrum of antimicrobial properties [12]. _L. casei_ LUHS210 and _L. uvarum_ LUHS245 strains were incubated separately and multiplied in De Man, Rogosa, and Sharpe (MRS) broth culture medium (Biolife, Milan, Italy) at 30 \({}^{\circ}\)C under anaerobic conditions. Boiled mushrooms (100 g) were inoculated with 3 mL of LAB multiplied in MRS (average cell concentration 9.0 log\({}_{10}\) CFU/mL) followed by fermentation for 7 days at 22 \(\pm\) 2 \({}^{\circ}\)C. The principal scheme of the first stage of the experiment is shown in Figure 2.  
Further, mushroom samples were visually evaluated to determine which treatment led to the lowest intensity of visible mould colonies on samples' surfaces. Then, the most effective LAB strain was selected for further treatment. Because of the dark unacceptable colour of _S. luteus_ mushrooms, they were not used for subsequent experiments.  
The parameters of thermal treatment and ultrasonication before fermentation were optimised. Ultrasonication was performed at a 37 kHz frequency, using a 100 W power level. The equipment employed was an ultrasonication processor (PROCLEAN 3.0 DSP, Ulsonix, Berlin, Germany). Each sample was processed for 30 min. The principal scheme of the second stage of the experiment is shown in Figure 2.  
Figure 1: Images of edible mushrooms used in this experiment.