esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Preprocessing  
### Experimental Data Collection  
In this paper, the experimental data were collected from 7 to 14 August 2022 at the Mycorrhizal Microbial Agriculture Practice Base, Huanxi Town, Jin an District, Fuzhou City, Fujian Province, China (26\({}^{\circ}\)20\({}^{\prime}\) N, 119\({}^{\circ}\)38\({}^{\prime}\) E). The base is mainly used for the research of fungus culture technology and fungus bait microbial water high-density fish culture system, equipped with 37 high-density fish ponds, which can realize automatic feeding and water circulation. Data acquisition equipment for fish pond dissolved oxygen is oxygenation control equipment (Kodak brand KD326-A), as shown in Figure 1. The device can accurately capture a variety of water quality parameters of the aquaculture water body, to achieve for the perch one-stop intelligent monitoring, high-pressure oxygen demand, but also to saving the data to a database for easy recording, providing a cell phone and Web services to achieve remote monitoring of real-time adjustment function. It is also equipped with a portable water quality testing pen, which can accurately detect the PH, total dissolved solids, conductivity, and other values of pond water quality. During the actual sampling period at 9:00 and 14:00 each day, the KD326-A equipment was placed on the surface of the ponds to detect and record the temperature and dissolved oxygen of the 37 aquaculture ponds, and then the water bodies of the 37 ponds were collected and tested for the values of PH, total dissolved solids, and conductivity with the portable water quality testing pen. The data distribution is shown in Table 1, and the minimum, maximum, mean, and standard deviation of each group of data are also labeled.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample Point**} & \multirow{2}{*}{**PH**} & **Water Temperature** & **Dissolved** & **Conductivity** & **Salinity** & **Total Dissolved** \\  & & **(\({}^{\circ}\)C)** & **Oxygen (mg/L)** & **(uS/cm)** & **Solids (mg/L)** \\ \hline
01 & 5.64 & 27.1 & 5.8 & 440 & 0.22 & 220 \\
02 & 5.39 & 27.1 & 5.9 & 440 & 0.22 & 218 \\
03 & 5.36 & 27.1 & 5.7 & 438 & 0.22 & 218 \\
04 & 5.64 & 27.0 & 5.8 & 430 & 0.22 & 220 \\
03 & 5.17 & 25.9 & 3.4 & 19.7 & 0.03 & 10 \\ Max. & 6.97 & 28.2 & 9.9 & 512 & 0.26 & 256 \\ Mean & 6.04 & 26.8 & 6.4 & 275.7 & 0.14 & 136.7 \\ Std & 0.41 & 0.53 & 1.44 & 0.057 & 0.05 & 58.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for water quality samples.
# The Use of SMAP-Reflectometry in Science Applications: Calibration and Capabilities
## 2 Capabilities
### SMAP-R Characteristics
#### 2.1.2 SMAP-R vs. TDS-1(SGR-ReSI)  
TDS-1, with SGR-ReSI--an instrument specifically designed to perform as a reflectometer--onboard also serves as a useful comparison. SGR-ReSI receives at GPS L1 band (frequency = 1575.42 MHz, wavelength = 19 cm) while SMAP-R receives at GPS L2C band (frequency = 1227.45 MHz, wavelength = 24.42 cm). TDS-1 measures in LHCP while SMAP measures in V-polarization and H-polarization. Figure 4 shows an example of the sampling for the GPS signals captured by SMAP-R and TDS-1 for 1 day of measurements.  
Figure 4 shows that TDS-1 coverage is greater than that from SMAP-R. However, as previously shown in Figure 3, the differences in coverage between SMAP-R and TDS-1 are smaller in comparison with the differences between SMAP-R and CYGNSS.  
The observed SNR range for TDS-1 is also 30 dB. While TDS-1 orbits at a slightly different speed and altitude than CYGNSS, TDS-1 integrates the received signal similarly over 1000 ms, creating a smeared observation over 7.6 km in the along-track direction. The spatial resolution attained from SMAP-R samples is therefore also better than that of the TDS-1 mission.  
Regardless of the instrument, spatial patterns of observed reflections are variable, with no exact repetition. Sampling depends on GPS constellation geometries coinciding within the receiver field of  
Figure 3: Peak signal to noise ratio (SNR) of the GPS reflections, captured by (**a**) SMAP-R at V-polarization with 25 ms integration time and (**b**) CYGNSS, for 1 day of measurements with 1,000 ms integration time. Peak SNR plotted in dB.  
view. Even though the sampling patterns of reflections are pseudo random in nature, SMAP-R data are collected continuously. On the contrary, the operation time of SGR-ReSI (onboard TDS-1) was limited to two out of eight days. Data collection from SGR-ReSI was limited because TDS-1 was an on-orbit test facility, so measurement time was shared between SGR-ReSI and other instruments.
## 2 Proposed FT-CFNN
### Convolutional Fuzzy Neural Network  
The architecture of the CFNN of the FT-CFNN is displayed in Figure 2. It comprises convolution, pooling, feature fusion, and FNN layers. Two convolution layers and two pooling layers are used in this network. Four feature fusion methods, namely GMP fusion, GAP fusion, CGMP fusion, and CGMP fusion, are proposed to reduce the feature size and integrate the network information. To reduce the size of adjustable parameters, an FNN is used to replace a fully connected network.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Outcomes**} & \multicolumn{5}{c}{**Time**} \\ \cline{2-6}  & **Baseline** & **1 Month** & **3 Months** & **6 Months** & **Difference** \\  & & & & & **(Baseline-6 Mon)** \\ \hline PPD in mm & & & & & \\ CONTROL & 7.57 & 6.0 & 4.86 & 3.71 & 3.86 \\ GROUP A & 9.86 & 8.0 & 6.29 & 4.83 & 5.0 \\ GROUP B & 9.43 & 7.29 & 5.43 & 4.0 & 5.43 \\ Pa & 0.032 & 0.11 & 0.185 & 0.09 & 0.09 \\ RAL in mm & & & & & \\ CONTROL & 7.57 & 6.0 & 4.86 & 3.71 & 3.86 \\ GROUP A & 10.14 & 8.29 & 6.57 & 5.14 & 5.0 \\ GROUP B & 9.71 & 7.57 & 5.71 & 4.29 & 5.43 \\ Pa & 0.01 & 0.05 & 0.11 & 0.10 & 0.09 \\ CEJ-BD in mm & & & & & \\ CONTROL & 6.54 & - & - & 5.09 & 1.46 \\ GROUP A & 7.5 & - & - & 4.39 & 3.11 \\ GROUP B & 7.73 & - & - & 5.01 & 2.71 \\ Pa & 0.15 & & & & 0.32 & 0.01 \({}^{*}\) \\ CEJ-AC in mm & & & & & \\ CONTROL & 3.91 & - & - & 3.17 & 0.74 \\ GROUP A & 3.7 & - & - & 2.64 & 1.06 \\ GROUP B & 4.01 & - & - & 3.07 & 0.94 \\ Pa & 0.62 & & & & 0.15 & 0.25 \\ BONE FILL in mm & & & & & \\ CONTROL & 2.63 & - & - & 1.91 & 0.71 \\ GROUP A & 3.8 & - & - & 1.74 & 2.06 \\ GROUP B & 3.71 & - & - & 1.94 & 1.77 \\ Pa & 0.03 & & & 0.87 & 0.02 \({}^{*}\) \\ DENSITY in HU & & & & & \\ CONTROL & 760.86 & - & - & 853.14 & 92.3 \\ GROUP A & 924.43 & - & - & 1144.29 & 219.9 \\ GROUP B & 865.86 & - & - & 1065.57 & 199.7 \\ Pa & 0.007 \({}^{*}\) & & & \(<\)0.001 \({}^{**}\) & \(<\)0.001 \({}^{**}\) \\ \hline PPD–probing pocket depth, RAL—relative attachment level; CBCT measurements: CEJ-BD—distance from cementoneamed junction to base of the defect; CEJ-AC—distance from cementoneamed junction to alveolar crest; Pa—\(p\) value intergroup comparison; “—statistically significant; \({}^{**}\)—highly statistically significant; CBCT taken at baseline and 6 months. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Intergroup and intragroup comparison of Mean values of PPD, RAL, radiographic characteristics including Bone fill and Bone Density according to CBCT in Hounsfield unit (HU) between Control, Group A and Group B at Baseline 1, 3 and 6 months.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 4 Performance Comparison with the Existing Models  
The designed antenna was tested for applicability in the desired applications with Vector Network Analyzer in the Anechoic Chamber. The obtained parameters from the proposed antenna were compared with the existing literature. The remarkable values in gain and efficiency were found to be good agreement. The optimized dimensions with the placement of AMC and dual band characteristics with suitable bandwidth are the key features of the designed antenna.  
Table 4 shows the performance comparison in which dimension, resonating frequencies, bandwidth, gain, and efficiency of different antennas are compared with the proposed antenna.  
Figure 21: Current distribution characteristics, (a) antenna without AMC Backing, (b) antenna with AMC Backing.  
Figure 20: Gain versus Frequency plot of the antenna (a) without AMC Backing (b) with AMC Backing.
## 1 Introduction
The absence of comprehensive multi-intent datasets in other languages presents formidable hurdles when applying multi-intent detection within those linguistic contexts. Furthermore, the existing joint detection models necessitate meticulous refinement and tailoring to suit distinct application scenarios, thereby securing the achievement of optimal detection performance. To conduct a more comprehensive investigation into Chinese multi-intent detection in the automotive domain, this paper presents the collection and annotation of a Chinese automotive multi-intent dataset (CADS). CADS is an open-source dataset specifically tailored for intelligent conversations related to automotive topics. It encompasses a total of 13,100 Chinese utterances, with each sentence capable of expressing up to three distinct intents. Building upon the CADS dataset, this paper introduces a novel joint framework model for multi-intent detection known as Auto-HPIF (automotive heterogeneous parallel interactive framework). This model integrates the Chinese BERT [7] language model and a Gaussian prior attention mechanism into the encoder stage. It devises a slot-intent parallel interactive framework based on heterogeneous graphs within the interaction process of joint multi-intent detection and slot-filling tasks. Additionally, the cross-entropy loss function is employed for the multi-intent classification task. The main contributions of this research can be summarized as follows:Addressing the challenges of multi-intent detection in the automotive domain, a Chinese automotive multi-intent dataset CADS was constructed. It contains 13,100 Chinese utterances, seven slots, and thirty intent types. The dataset is composed of multiple data sources, including automotive controls, navigation, and car services, covering diverse language styles and intent types;
2. An innovative multi-intent joint model specialized for the automotive domain is proposed. Its improvements mainly include three aspects. Firstly, it integrates the Chinese BERT language model and a Gaussian prior attention mechanism within the encoder stage, enhancing the accuracy and precision of semantic feature extraction. Secondly, addressing the tasks of multi-intent detection and slot filling, the model adopts a heterogeneous graph parallel interaction network, thereby further enhancing the exchange of information and interaction between tasks. Lastly, the successful resolution of the challenge of inadequate adaptability in the automotive domain's multi-intent models is achieved by introducing the cross-entropy loss function;
3. Thorough experimental evaluations were conducted on the CADS dataset alongside two publicly available datasets. The extensive results illustrated that Auto-HPIF significantly enhances the accuracy of both multi-intent classification and slot-filling tasks. By leveraging pre-training methods, it can adapt to the Chinese language style and facilitate more efficient human-machine interactions in the automotive scenario.
## 3 Integrated Disaster Resource Scheduling Modeling
### Pre-Disaster Resource and Facility Optimization
#### 3.1.2 Pre-Deployment of Charging Stations and Reservoirs
By appropriately setting the time limit and optimality gap, the proposed approach efficiently yields the optimal pre-disaster resource allocation and infrastructure layout informed by predictive information.
# A Corrected Adaptive Balancing Approach of Motorized Spindle Considering Air Gap Unbalance
## 2 Unbalance and Force of Motorized Spindle
### Electromagnetic Unbalance and Force  
#### 2.2.1 Characteristic of Spindle Motor with Dynamic Air Gap Eccentricity  
As a kind of motor, the air gap eccentricity of motorized spindle is inevitable. When the dynamic air gap eccentricity of a spindle motor occurs, the rotor of motor orbits around the geometric axis of the stator.  
As shown in Figure 2, it is known that the rotor orbits around the geometric center \"O\" of stator under the dynamic air gap eccentricity; \(\delta(\theta,t)\) is the air gap length between the motor rotor and stator in a polar coordinate system, which can be represented as follows according to the geometric relationship:  
\[\delta(\theta,t)=\delta_{0}-\delta_{d}\cdot\cos(\theta-\omega_{r}t), \tag{4}\]  
where \(\delta_{0}\) is the nominal air gap length, \(\delta_{d}\) is the dynamic air gap eccentricity length, \(\theta\) is the mechanical angle, and \(\omega_{r}\) is the angular speed at minimum air gap position, expressed as  
\[\omega_{r}=\omega_{1}(1-s)/p, \tag{5}\]  
where \(p\) is the magnetic pole pair number of motor, \(s\) is the motor slip rate, and \(\omega_{1}\) is the angular frequency of motor power supply.  
When a motorized spindle is subject to the dynamic air gap eccentricity, the unbalanced magnetic pull (UMP) of the motor is generated, causing an undesired electromagnetic vibration of the spindle rotor. If the UMP contains 1\(\times\) rotating frequency component, then 1\(\times\) rotating frequency component of the UMP should be removed from the balancing reference of the online active balancing scheme in frequency domain to improve the balancing effect. Therefore, the key problem is the calculation of the UMP. For different types of motors, the UMP calculations are different. This paper mainly applies a three-phase asynchronous spindle motor driven by a frequency converter to calculate the UMP.
## 3 Model Structure and Fault Diagnosis Process
### MARN Model Structure  
The MARN model is shown in Figure 5, which in general consists of the residual feature extraction layers, the mixed attention feature enhancement layers and the Softmax classification layers.  
In order to learn as many features as possible from the input images, the feature extraction layer uses 16 groups of residual structures (each containing two \(3\times 3\) convolutional layers) for feature extraction; all the features extracted by the feature extraction module are subsequently passed through the feature enhancement layer to augment the representation of the features. In residual networks, a portion of the input feature matrices are passed directly from the bottom layer to the top layer, so further channel compression  
Figure 4: Spatial attention mechanism for MARN (SA, pooling operation followed by dimensionality reduction to get the spatial feature matrixes).  
Figure 3: Channel attention mechanism for MARN (CA, compressing and splitting the feature matrices to obtain the channel attention matrixes).  
and refinement are required; and the top layer information obtained through mainline convolution lacks location and detail information, although it contains more global abstract information. Therefore, channel and spatial mixed attention mechanism is introduced to utilise high- and low-level information fusion enhancements to make the information more complete.  
After feature enhancement, the global average pooling (GAP) is used to match each feature matrix assigned with different intrinsic meanings to the fault categories, which better integrates global spatial information and reduces the network parameters. Finally, the Softmax classifier is used to classify the faults.
# The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
###### Abstract  
The vital role of honeybees in pollination and their high rate of mortality in the last decade have raised concern among beekeepers and researchers alike. As such, robust and remote sensing of beehives has emerged as a potential tool to help monitor the health of honeybees. Over the last decade, several monitoring systems have been proposed, including those based on in-hire acoustics. Despite its popularity, existing audio-based systems do not take context into account (e.g., environmental noise factors), and thus the performance may be severely hampered when deployed. In this paper, we investigate the effect that three different environmental noise factors (i.e., nearby train rail squaeling, bekeeper speech, and rain noise) can have on three acoustic features (i.e., spectrogram, mel frequency cepstral coefficients, and discrete wavelet coefficients) used in existing automated beehive monitoring systems. To this end, audio data were collected continuously over a period of three months (August, September, and October) in 2021 from 11 urban beehives located in downtown Montreal, Quebec, Canada. A system based on these features and a convolutional neural network was developed to predict beehive strength, an indicator of the size of the colony. Results show the negative impact that environmental factors can have across all tested features, resulting in an increase of up to 355% in mean absolute prediction error when heavy rain was present.  
honeybees; beehive acoustics; beehive monitoring; context-awareness; precision beekeeping 2022
## 2 Concept of Time Frequency Analysis and Processing
### Choi-Williams Distribution
However, all of the other three bilinear time frequency methods produce cross terms, which are shown in Figure 1a,c,d, respectively. The cross-term problem is most severe in WVD, whereas CWD shows the best cross-term suppression ability. Although the STFT has no cross-terms, its time frequency concentration and resolution perform bad as a linear time frequency analysis method. Hence CWD is the best choice in the following time frequency analysis for the communication signals.
# Mapping with Pleiades--End-to-End Workflow
## 6 Discussion
### Future Research Goals  
The most difficult blocks within the processing chain were identified as the stereo matching, the fusion process and DTM generation. The first two methods directly contribute to the quality of the resulting DSM. In stereo matching, we should try to increase the plausibility of the cost functions. A realistic way will be to learn the cost function on huge ground truth data sets based on deep learning of a convolution neural network. A second option which seems to be promising is to deeply investigate the generalized Census transform and combine it with a robust cost function that also yields useful correspondences in texture-less regions.  
The fusion process should somehow globally optimize the resulting surface, while being computationally feasible. Like in SGM an RMF could be solved in a heuristic manner. Alternatively, global optimizations like in References [86; 87] would be of interest as well even though they are computationally complex. Anyhow, like in photogrammetry with increasing resolutions the community will go from DSM as the desired output to a fully 3D point cloud. Thus, the fusion of multiple stereo disparities has then to be performed in 3D yielding a simplified triangulated textured point cloud representation.  
As in the fusion process, DTM generation could be solved globally, for example, by a variational approach. In addition, the multi-spectral information, that is, our ortho mosaic may serve as an input in processing. A classification on the multi-spectral data would help to discriminate man-made objects and vegetation from bare earth regions.  
To reduce the manual effort in the whole process automatic methods for transferring GCPs from SAR amplitude images to optical images are needed. Actually, the underlying data exists (e.g., worldwide TerraSAR-X acquisition that were used in the WorldDEM generation) and also methodologies for multi-modal image matching. In the optimal case, such a transfer workflow could be directly performed at the satellite data providers. Then, end-users would get optical images and GCPs or optical images with already optimized sensor models.
## 2 Materials and Methods
### Graph Structured Network (GSN)  
The GSN is formed by nodes and segments. Nodes can be divided into two groups: (1) Physical nodes (also called crossroad nodes) that are the centers of the crossroads. In the MAS, a crossroad is represented by a primary agent--the crossroad agent (CA), and can be composed of two to four virtual segments; see Figure 5. These are connected to the physical node. (2) Virtual nodes are not part of any crossroad; they just represent boundaries of the crossroads. Just as the virtual segments, they are present only in the virtual environment. Each node has its own ID and it is aware of the neighboring nodes. A set of IDs is an abstraction of an MRSA's route.  
Figure 5: From the left: a virtual, a physical, and a logical segment. Larger and smaller nodes represent physical and virtual nodes, respectively.  
There are three types of segments (Figure 5):  
* Virtual;
* Physical;
* Logical.
###### Abstract  
As Augmented Reality (AR) applications become commonplace, the determination of a device operator's subjective Quality of Experience (QoE) in addition to objective Quality of Service (QoS) metrics gains importance. Human subject experimentation is common for QoE relationship determinations due to the subjective nature of the QoE. In AR scenarios, the overlay of displayed content with the real world adds to the complexity. We employ Electroencephalography (EEG) measurements as the solution to the inherent subjectivity and situationality of AR content display overlaid with the real world. Specifically, we evaluate prediction performance for traditional image display (AR) and spherical/immersive image display (SAR) for the QoE and underlying QoS levels. Our approach utilizing a four-position EEG wearable achieves high levels of accuracy. Our detailed evaluation of the available data indicates that less sensors would perform almost as well and could be integrated into future wearable devices. Additionally, we make our Visual Interface Evaluation for Wearables (VIEW) datasets from human subject experimentation publicly available and describe their utilization.  
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Footnote †: journal:  
_Future Internet_**2017**, 9, 40; doi:10.3390/f9030040