esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Vibration Impact Using the VPIB  
As seen in Figures 14 and 15, the SWD scales are in a logarithmic arrangement, and furthermore, the boundary lines of the impact zones on the scale are broken lines, making it difficult to unequivocally assess the position and comparison of the analysis results. Therefore, in 2016, the concept of the VPIB (Vibration Perception Index for Buildings) was introduced into the norm [37]. This indicator can be defined as the ratio of the maximum velocity values determined as a result of the seismogram analysis in third-octave bands to the velocity value in a given third-octave band corresponding to the lower boundary of dynamic effects consideration on buildings, i.e., line A on the SWD scale [20; 51].  
The VPIB indicator provides numerical information about the following:  
How distant the vibration parameters occurring in the building are from the lowest boundary line (line A)--VPIB \(\leq\) 1;  
How many times this boundary has been exceeded--VPIB? 1.  
The positions of lines with equal values of the VPIB indicator are presented in Figure 16. According to the definition of the VPIB indicator, line A has a VPIB indicator of 1, while line B has a VPIB indicator of 5 (Figure 17). This means that vibrations with velocity values even five times different can be classified into zone II in a given frequency band.  
Using the VPIB indicator greatly facilitates the comparative analysis of vibration intensities induced by different sources. Therefore, this indicator was applied to compare vibrations induced by blasting works in the open-pit mine and those induced by tremors in the underground mine. The results of the calculations are presented in Table 3 and graphically depicted in Figure 17. Figure 17 numerically illustrates the comparison of vibration effects from different events.  
Figure 16: SWD-I scale with lines representing equal values of the VPIB indicator.  
Figure 17: Comparison of VPIB indicator values—blasting works (21 March 2023) and underground mine tremor (24 December 2022).  
The value of the VPIB indicator for vibrations induced by blasting works only at a frequency of 7.94 Hz minimally exceeded 1, while in the case of tremors, at a frequency of 10.0 Hz, the VPIB indicator reached a value of 3.59 Hz. This means that vibrations from underground mine tremors exert 3.6 times stronger impact than vibrations induced during blasting works.
# Mask R-CNN-Based Stone Detection and Segmentation for Underground Pipeline Exploration Robots
## 1 Introduction
Although the above approaches can detect rocks from daylight images of outdoor environments, when the lighting conditions change, the detection performance is significantly reduced. A CNN-based method for rock fragment classification inside a tunnel using indoor lighting was studied in [13]. However, the diameter of tunnel-like spaces provides sufficient room for object investigation, whereas narrow underground pipelines pose significant challenges for tasks related to object recognition. For estimating the distance between the camera and segmented stones, and for the grasping action of the explorer robot, spatial knowledge is needed. Object distance measurement has been gradually used in various industrial applications. Several distance measurement methods are available, such as stereo vision image processing techniques [14; 15; 16], ultrasonic sensors [17], and depth cameras [18]. However, depth camera technology has gained much attention in recent years, mostly due to its high acquisition rates, long measurement ranges, and cost effectiveness. In this research study, we chose a single RGB-D camera (Intel RealSense L515) to perform both segmentation and distance measurement. To automatically identify stones and determine their distances from the explorer robot, a stone detection and distance measurement method based on Mask R-CNN is proposed. An end-to-end instance segmentation architecture is applied in the proposed method, which accepts a single picture as input and, without any preprocessing, returns all instances of stones (detection and pixel-level classification). Finally, distance estimation and classification results are merged for use in real applications. In this research study, we focus on detecting the stones on the underground pipeline surface and measuring their distance from the exploration robot. This study does not consider the additional process of weighing the stone and moving it by the robot manipulator. The contributions of this study are summarized as follows:  
* A Mask R-CNN-based system is developed to identify the stones and measure their distances from the robot in the underground pipeline. * A manually validated and labeled data set is presented for the segmentation work. This system offers a precise and fast robotic study of underground pipeline object detection research. The structure of this paper is organized as follows: Section 2 introduces the Mask R-CNN model, data acquisition, data processing, and evaluation methods. Experimental details and test results are discussed in Section 3. In Section 4, limitations, future directions, and conclusions are presented.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
###### Abstract  
Gesture plays an important role in human-machine interaction. However, the insufficient accuracy and high complexity of gesture recognition have blocked its widespread application. A gesture recognition method that combines state machine and bidirectional long short-term memory (Bi-LSTM) fusion neural network is proposed to improve the accuracy and efficiency. Firstly, gestures with large movements are categorized into simple trajectory gestures and complex trajectory gestures in advance. Afterwards, different recognition methods are applied for the two categories of gestures, and the final result of gesture recognition is obtained by combining the outputs of the two methods. The specific method used is a state machine that recognizes six simple trajectory gestures and a bidirectional LSTM fusion neural network that recognizes four complex trajectory gestures. Finally, the experimental results show that the proposed simple trajectory gesture recognition method has an average accuracy of 99.58%, and the bidirectional LSTM fusion neural network has an average accuracy of 99.47%, which can efficiently and accurately recognize 10 gestures with large movements. In addition, by collecting more gesture data from untrained participants, it was verified that the proposed neural network has good generalization performance and can adapt to the various operating habits of different users.  
deep learning; hand gesture recognition; millimeter-wave radar +
Footnote †: journal: Applied Sciences
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 1 Introduction
As shown in Figure 1, the pipeline consists of three consecutive modules, including (1) a SentiBERT-based encoder that aims to transform input tokens to sentiment-aware contextual embeddings, (2) a Bidirectional LSTM (BiLSTM) layer with attention to produce attentive hidden states, and (3) a single-layer CNN as a feature extractor. In addition, a standard detection head takes as input a concatenation of the generated features and feeds them into a fully connected layer followed by a softmax layer to output the prediction result, i.e., disaster Tweet or not. The design is validated through extensive experiments, including hyper-parameter tuning to decide certain design choices and an ablation study to justify the necessity of each selected building block. Results show that the proposed system achieves superior performance in the F1 score, making it a competitive model in Tweets-based disaster prediction. The rest of this paper is organized as follows: Section 2 reviews relevant studies; Section 3 covers the dataset description and the technical details of the proposed learning model; Section 4 provides experimental validation with result analysis; Section 5 summarizes our work and points out future directions.
## 3 PDM and Parameter Identification
### Flexible Dynamic Model Verification  
As shown in Figure 5, a 2-DoF planar robot is employed for evaluation. The motion control and data collection are realized by a self-developed controller with a sampling frequency of 1 KHz. Each joint is equipped with two encoders, which can measure the motor angle and joint output angle, respectively. The flexible dynamic model for the planning robot is depicted in Appendix A. After the preliminary identification, the learning spaces of the inertia-related parameters are configured between 90% and 110% of the preliminary values. The friction model is more complex, and thus, the corresponding learning spaces are set between 80% and 120%. The stiffness parameters form product manuals always have a big gap with the actual values. Hence, the learning spaces are set between 50% and 150%.  
The results of the preliminary identification and parameter tuning are shown in Table A1 in Appendix A. As can be seen, the inertia-related parameters have smaller changes after tuning. The changes in stiffness parameters exceed 10% by tuning, which means that the given values in user manuals have limited reference significance and need to be adjusted. The friction-related parameters generally have the biggest changes compared to the other two types. Then, the test trajectories shown in Figure 6 are applied to the robot to validate the model accuracy. Figure 7 displays the actual and simulated motor torques using both preliminary and fine-tuned parameters. Notably, the simulation error decreases significantly following the parameter tuning process. Hence, the simultaneous tuning of all parameters based on CARLA is effective for improving the model accuracy. Meanwhile, with the proposed identification method, each independent parameter can be obtained without the optimization of excitation trajectories.  
Figure 5: Layout of the experimental system.  
Figure 6: Test joint trajectories.  
Figure 7: Simulation and actual motor torques of each motor. (**a**) Motor 1. (**b**) Motor 2.
## 4 System Architecture
### The Workflow of Central Bandwidth Allocator  
During the working process of central bandwidth allocator, a tenant submits his VDC requirement first. Then, this request is delivered to the Allocation component of VDC, and this component decides whether to map VN to PN based on the VN type. If the VDC requirement is of the QoS-VDC type, the VDC Allocation component will generate virtual links for mapping  
Figure 4: Fairness in the cloud based on tenants.  
Figure 5: The system architecture of Nxt-Freedom.  
to the physical links, and these virtual links are eventually sent to the Path Collector component. On the other hand, if the VDC requirement is of the NQoS-VDC type, it is unnecessary for the VDC Allocation module to generate the mapping results, because the minimum bandwidth guarantee of NQoS-VDC is zero. In this case, the Dynamic Path Arbitration component is used to generate these mapping results instead, and suitable paths are determined in accordance with the physical network's current workload status, which are then sent to the Path Collector component. In the final step, the Redundant Bandwidth Allocator component queries the mapping results from the Path Collector periodically, and decides how to allocate the redundant bandwidth. However, some VDCs may not use all the bandwidth they are allocated with. To address this problem, the independent Dynamic Bandwidth Adjustment (DBA) component is employed to detect idle tenants with spare bandwidth, and the spare bandwidth will be allocated among VDCs of the idle tenants first; if there is still redundant bandwidth, it will be allocated to other tenants based on the max-min fairness principle.
## 3 Results and Discussion
### Risk Evaluation  
#### 3.2.1 Risk Assessment of Heavy Metal Pollution in Soil  
In order to reveal the degree of heavy metal pollution from local soil sampling sites or individual soil heavy metal indicators, we conducted pollution assessments on 63 soil sampling sites based on the single pollution index and the Nemerow comprehensive index  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Index** & **Unit** & **Min** & **Max** & **Mean** & **SD** & **CV** & **Background Value** \\ \hline Pb & mg\(\cdot\)kg\({}^{-1}\) & 0.20 & 36.50 & 13.37 & 10.03 & 0.75 & 30.90 \\ As & mg\(\cdot\)kg\({}^{-1}\) & 0.17 & 55.02 & 19.87 & 16.81 & 0.85 & 10.40 \\ Cu & mg\(\cdot\)kg\({}^{-1}\) & 0.94 & 27.27 & 6.91 & 5.04 & 0.73 & 30.10 \\ Zn & mg\(\cdot\)kg\({}^{-1}\) & 0.11 & 116.20 & 50.55 & 27.70 & 0.55 & 86.50 \\ Ni & mg\(\cdot\)kg\({}^{-1}\) & 8.68 & 61.87 & 25.06 & 10.97 & 0.44 & 32.60 \\ Cr & mg\(\cdot\)kg\({}^{-1}\) & 12.96 & 230.05 & 77.71 & 39.31 & 0.51 & 79.00 \\ NH\({}_{4}^{+}\)-N & mg\(\cdot\)kg\({}^{-1}\) & 5.56 & 118.10 & 47.23 & 19.91 & 0.42 & - \\ NO\({}_{3}^{-}\)-N & mg\(\cdot\)kg\({}^{-1}\) & 28.66 & 381.60 & 157.21 & 84.97 & 0.54 & - \\ AP & mg\(\cdot\)kg\({}^{-1}\) & 4.92 & 281.20 & 49.60 & 54.93 & 1.11 & - \\ AK & mg\(\cdot\)kg\({}^{-1}\) & 30.28 & 867.50 & 220.50 & 172.10 & 0.78 & - \\ EC & mS m\({}^{-1}\) & 1.43 & 25.04 & 6.92 & 5.33 & 0.77 & - \\ CEC & cmol\({}^{+}\) kg\({}^{-1}\) & 0.10 & 12.90 & 2.78 & 2.72 & 1.00 & - \\ SWC & \% & 4.47 & 47.49 & 20.35 & 6.70 & 0.32 & - \\ pH & - & 3.48 & 7.06 & 5.12 & 0.61 & 0.12 & - \\ \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for heavy metal concentrations and basic parameters in soils.  
(Table 2). The soil heavy metals (Pb, As, Cu, Zn, Ni, and Cr) had various degrees of pollution in the study area. The single-factor pollution index of the soil area was less than 1 for the 6 heavy metals except for As and Cr. The single-factor pollution index of As and Cr indicated that 17.46% and 4.76% of the sample sites were classified as having Mild Pollution for As and Cr, respectively. Nemerow comprehensive pollution index showed that sites with Slight Pollution and Mild Pollution accounted for 26.98% and 3.17% of all the sampling sites, respectively, indicating that nearly 70% of the sampling sites in the study area had no heavy metal pollution in the soil.
## 3 Payload Design
### Suppayload 1: Data Acquisition System
#### 3.3.1 Optics  
The optical system comprised a scientific sCMOS sensor, the pco.panda 4.2 from PCO Imaging, based in Kelheim, Germany [6]. The ZEISS Dimension 2/25 lens from Carl Zeiss Industrielle Messtechnik GmbH, located in Oberkochen, Germany [17], was the lens of choice for subpayload 1. In comparison to the PCO imager onboard RSONAR II, two other imagers AURICAM(tm) and the Fast Auroral Imager (FAI) are also listed. AURICAM(tm) is a commercial star tracker [5], manufactured by Sodern. Various models of star trackers from different vendors have been flown on various satellites including DirecTV-15, One Web, Pleiades Neo, and Cygnus missions among others. The FAI is part of the e-POP Mission onboard the Cascade, Smallsat, and Ionospheric Polar Explorer (CASSIOPE) satellite, whose primary focus is on ionospheric and space weather studies. The FAI [18] features a WFOV of 26\({}^{\circ}\) and images aurora around the Earth in nadir pointing orientation under nominal conditions. FAI has an f/4 lens system that is combined with a fiber-optic taper, which results in an effective f-number of f/0.8. While FAI is not a star tracker typically used for attitude determination like AURICAM(tm), we have used FAI images to examine spaceborne RSO images for feasibility [19], simulation analysis [20], and RSO observation prediction analysis. A feasibility study was conducted on the use of the IDS UI-3370 CP-M-GL Rev.2  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Nominal** & **Peak** & **Input** & **Nominal** & **Peak** \\  & **Current (A)** & **Current (A)** & **Voltage (V)** & **Power (W)** & **Power (W)** \\ \hline Subpayload 1 & 0.37 & 0.39 & 28 & 10.36 & 10.92 \\ Subpayload 2 & 0.29 & 0.34 & 28 & 8.12 & 9.52 \\ \hline \multicolumn{5}{c}{Total Power (W)} & \multicolumn{3}{c}{18.48} & 20.44 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RSONAR II Power Budget.  
Figure 3: RSONAR II harness diagram.  
compact monochrome camera, coupled with a telephoto lens, for RSO imaging. This low-cost optical system was found to be an economical alternative, costing approximately one-tenth of the PCO imaging system while offering comparable sensor specifications in a compact form factor. The key specifications of these four imaging systems are detailed in Table 3. The sensor specifications in Table 3 are related to each other. FOV is directly proportional to the number of pixels in the sensor and the pixel size but is inversely proportional to the focal length.
## 4 Discussion
regale_ [PERSON], _GAPDH_ is the commonly used reference gene to normalize _Fusarium_ wilt-related candidate genes' expression levels at different time points of post-_F. oxysporum_ inoculation [28; 29; 41; 42; 43]. Nevertheless, in this study, we found that _HIS_ was the most reliable reference gene for similar studies in _L. sargentiae._ Instead, _GAPDH_ was found to be the least stable reference gene among all nine tested reference genes (Table 3). Consistently, in _L. pumilum_ DC, _18S_ is the suitable reference gene, instead of _GAPDH_ for resistance to _F. oxysporum_-related studies [30]. These results show that _GAPDH_ is unsuitable for accurate qRT-PCR normalizing the expression levels of _F. oxysporum_-resistant genes in _L. sargentiae_ and _L. pumilum._  
To confirm the suitability of the identified most reliable reference gene (_HIS_), it was used to normalize the expression levels of four resistance-related genes at different times in two treatments in _L. sargentiae._ The selected target genes, including _POD_, _PAL_, _CHI_, and _DIR_, are well-known to be involved in defense mechanisms against various pathogens in plants. _POD_, _PAL_, and _CHI_ were significantly up-regulated in _L. regale_ under _F. oxysporum_ treatment [25; 29; 43; 46]. _DIR_ may play an important role in responses to different pathogenetic fungi in many plants. In _Pisum sativum_, the expression of _DIR_ was significantly induced after inoculating with _F. solani_ f. sp. _Phaseloli_[47]; _Erysiphe necator_ in _Vitis vinifera_[48]; _Colletotrichum gloeosporoides_ in _Physcomitrella patens_[49]; and _Marssonina brunnea_ in _Populus_[50]. In the present study, the four resistance-related genes expression patterns via _HIS_ normalization were consistent with the above results. This result further demonstrated that _HIS_ should represent the reference genes for qRT-PCR normalization of the expression levels of target genes related to _F. oxysporum_ resistance in _L. sargentiae._ Functional characterization of _POD_, _PAL_, _CHI_, and _DIR_ is needed to uncover the regulatory mechanisms associated with their respective expressions in responses to _F. oxysporum_ in _L. sargentiae_ and close species.
## 4 Materials and Methods
### Meta-Path-Level Aggregation  
Each meta-path contains a specific structure and semantic information, and the node may be connected to other nodes through multiple meta-paths, so we need to learn more comprehensive node embedding. In addition, traditional pooling strategies (average pooling or max pooling) cannot effectively measure the importance of different meta-paths. In this paper, a semantic-level attention mechanism is used to analyze the set \(\{H^{\rho_{i}}\}_{i=1}^{p}\) performs overall aggregation to generate an overall representation containing multiple relational semantics. To this end, an intuitive idea is to measure the contribution of each meta-path to the final node representation and then use the respective contributions as weights to aggregate the individual node representations, so Here an attention layer \(L_{att}\) is added to learn the corresponding weights:  
\[\{\beta^{\rho_{1}},\beta^{\rho_{2}},\cdots,\beta^{\rho_{p}}\}=L_{att}(H^{\rho_ {1}},H^{\rho_{2}},\cdots,H^{\rho_{p}}) \tag{14}\]  
First, calculate the importance of the single meta-path \(\rho_{i}\).  
\[e^{\rho_{i}}=\frac{1}{N}\sum_{n=1}^{N}\tan\h\left(q^{T}[W_{sem}\cdot h^{\rho_ {i}}+b]\right) \tag{15}\]  
where, \(W_{sem}\) represents the parameter matrix, and \(q\) represents the semantic attention vector that needs to be learned.  
Next, the generated set \(\{e^{\rho_{i}}\}_{i=1}^{p}\) is regularized using the \(softmax\) function to obtain the weight \(\beta^{\rho_{i}}\) of the meta-path \(\rho_{i}\):  
\[\beta^{\rho_{i}}=softmax(e^{\rho_{i}})=\frac{\exp(e^{\rho_{i}})}{\sum_{j=1}^{ p}\exp(e^{\rho_{i}})} \tag{16}\]  
Finally, the dynamic heterogeneous information network representation \(H\) will be obtained by the linear combination of the node representation set \(\{H^{\rho_{i}}\}_{i=1}^{p}\):  
\[H=\sum_{i=1}^{p}\beta^{\rho_{i}}\cdot H^{\rho_{i}} \tag{17}\]  
It should be noted that the attention weight learned by the model is binary cross-entropy loss in the method of this paper, and the learned weight helps to measure the similarity of a node to its neighbors under different distributions, that is, the more similar the node features to its neighbors, the greater the assigned weight. At the same time, since the classification label is not involved, the weight will not be affected by the label.
# Photovoltaic Solar Cells: A Review
## 2 Solar Cells
### Materials Employed in PV Cells
#### 2.7.2 Future Trends  
GaAs solar cells have contributed as concentrators and space solar cells [54] and are anticipated to create new markets, such as large-scale electric power systems and PV cell-powered electric vehicles. Single-junction GaAs solar cells, which are important as sub-cells for multijunction PV cells, have shown the highest ever stated efficiency (29.1%) under the standard AM 1.5 spectra for single-junction PV cells. GaAs multijunction solar cells of three junctions or more have been developed due to the limiting efficiency of single-junction PV cells. GaAs multijunction solar cells will be widely used in space because of their high obtained PCE. The concentrator PV (CPV) systems with several times more annual power generation capability than typical crystalline Si flat-plate systems will open a new market for charging stations including battery-powered electric vehicle applications. Multi-junction solar cells are greatly expected to be high-efficiency PV cells applied to solar cell-powered electric vehicles and large-scale PV power plants. Further development of super-high-efficiency and low-cost PV cells is crucial to create new markets [75; 76].