esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations and acronyms are used in this manuscript:  
AW-IPM  
Area-Wide Integrated Pest Management  
CCE  
Conventional Chemical Ecology  
CHs  
Critical Hydrocarbons  
CI  
Chemical Ionization  
CL  
Cue Lure  
CRCE  
Computational Reverse Chemical Ecology  
CSD  
Current Source Density  
CSPs  
Chemosensory Proteins  
DESI  
Desorption Electrosp Ionization  
DM-HMP  
Desmethyl Host-Marking Pheromone  
DVB/CAR/PDMS  
Divinylbenzene/Carboxen/Polydimethylsiloxane  
EAG  
Electronantenogram  
EI  
Electron Ionization  
ELSD  
Evaporative Light Scattering Detectors  
FAR  
_Ceratitis_ complex including _C. fasciventris_, _C. anonae_, _C. rosa_, and _C. quilici_  
GA  
Glutamic Acid  
GC-EAD  
Gas Chromatography-Electroantenographic Detection  
GC\(\times\)GC-TOFMS  
Two-dimensional Gas Chromatography with Time-Of-Flight Mass Spectrometry  
GC-FID  
Gas Chromatography-Flame Ionization Detection  
GC-FTIR  
Gas Chromatography-Fourier Transformed Infrared Spectroscopy  
GC-MS  
Gas Chromatography-Mass Spectrometry  
GOBP  
General OBP  
G Protein-Coupled Receptors  
GRO  
Ginger Root Oil  
GRs  
Gustatory Receptors  
GSH  
Tripeptide Glutathione  
HMP  
Host-Marking Pheromone  
HPLC  
High Performance Liquid Chromatography  
IPM  
Integrated Pest Management  
IRs  
Ionotrophic Receptors  
LC-MS  
Liquid Chromatography-Mass Spectrometry  
LC-QTOF-MS  
LC-Quadrupole Time-Of-Flight-Mass Spectrometry  
MALDI-TOFMS  
Matrix-Assisted Laser Desorption/Ionization-Time-Of-Flight Mass Spectrometry  
MAT  
Male Annihilation Technique  
ME  
Methyl Eugenol  
MEMS  
Microelectromechanical System  
MSI  
Mass Spectrometric Imaging  
NMR  
Nuclear Magnetic Resonance  
OBPs  
Odorant Binding Proteins  
Orco  
Olfactory receptor co-receptor  
ORs
Olfactory Receptors  
OSNs  
Olfactory Sensory Neurons  
PDMS/DVB  
Polydimethylsiloxane/Divinylbenzene  
RCE  
Reverse Chemical Ecology  
RK  
Raspberry Ketone  
RKTA  
Raspberry Ketone Trifluoroacetate  
RNAi  
RNA Interference  
SIT  
Sterile Insect Technique  
SPME  
Solid Phase Microextraction  
SSR  
Single Sensillum Recording  
TLC  
Thin Layer Chromatography  
UV-LDI o-TOFMS  
Ultraviolet Laser Desorption Ionization orthogonal Time-Of-Flight Mass Spectrometry  
VOCs  
Volatile Organic Compounds  
1-NPN  
\(N\)-phenylInphthalen-1-amine  
4-DMP  
4-allyl-2,6-dimethoxyophenol
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 2 Overview of GRU and GA
### Introduction to Gated Recurrent Units (GRU)  
GRU inherits the advantages of Long Short-Term Memory (LSTM) networks and simplifies its structure while retaining high efficiency. Compared with LSTM, GRU only contains two gates: the update gate and reset gate. Therefore, GRU has recently become a widely used neural network. The GRU structure is shown in Figure 1. GRU and LSTM have the same function when capturing long sequence semantic association, GRU can effectively suppress gradient disappearance or explosion, and the effect is superior to traditional RNNs and the computational complexity is lower than LSTM. The deep neural network model in this paper is mainly based on GRU.  
In the GRU structure, the update gate is set as \(z_{t}\) and the reset gate is set as \(r_{t}\). The formula of GRU networks is shown in the Equations (1)-(5):  
\[r_{t}=\sigma(w_{r}\cdot[h_{t-1},x_{t}]) \tag{1}\]  
\[z_{t}=\sigma(w_{z}\cdot[h_{t-1},x_{t}]) \tag{2}\]  
\[\widetilde{h_{t}}=tanh(w_{\widetilde{h}}\cdot[r_{t}*h_{t-1},x_{t}]) \tag{3}\]  
\[h_{t}=(1-z_{t})*h_{t-1}+z_{t}*\widetilde{h_{t}} \tag{4}\]  
\[y_{t}=\sigma(w_{0}\cdot h_{t}) \tag{5}\]
# Hybrid-Supervised-Learning-Based Automatic Image Segmentation for Water Leakage in Subway Tunnels
## 4 Analysis and Discussion
### Performance Evaluation of WRDeepLabV3+ Semantic Segmentation Network  
WRDeepLabV3+ uses the WRes2 Net101 structure as the core network with an Epoch of 50 in the freezing phase and 50 in the thawing phase. Firstly, the effect of the optimizer on WRDeepLabV3+ (without attention mechanism) under different learning rate decay methods is compared. As shown in Figure 8, the initial learning rate is 0.0005, the green line is the _MIoU_ of Adam under CosLR, the blue line is the _MIoU_ of Adam under StepLR, the yellow line is the _MIoU_ of SGD under CosLR, and the orange line is the _MIoU_ of SGD under StepLR. Under different learning-rate-decay methods, the _MIoU_ of the optimizer under CosLR is higher than that of StepLR. Experiments show that the proposed model converges faster and can obtain the best segmentation effect when using Adam optimizer and CosLR decay. Figure 9 shows that the initial learning rate is 0.0005 using Adam optimizer and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & _Precision (\%)_ & _Recall (\%)_ & _MIoU (\%)_ & _F1 (\%)_ \\ \hline WL-CAM & 71.4 & 74.3 & 67.3 & 72.8 \\ (Res2 Net101) & 83.9 & 75.6 & 73.6 & **79.5** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance evaluation of WL-CAM with different backbone networks based on the proposed dataset.  
Figure 7: Loss of WL-CAM with different backbone networks.  
CosLR decay. Compared with the training model of WRDeepLabV3+ and DeepLabV3+, _MIoU_ is improved by 3.3%.  
Secondly, we compare WRDeepLabV3+ without and with the attention mechanism. As shown in Table 11, under the same dataset, due to the addition of channel attention mechanism and spatial attention mechanism, _Loss_ is reduced by 7.5%, _Recall_ is improved by 2.4%, _Precision_ is improved by 3%, and IoU is improved by 2.2%.  
Finally, the effect of transfer learning on the performance of WRDeepLabV3+ is discussed. The transfer learning of the backbone network model is to use the parameters of the trained model (pre-trained model) to transfer to the new model to help the training of the new model. As shown in Figure 10, it allows the network to improve faster during training. After training, the convergence effect and performance of the model are better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**WRDeepLabV3+** & _Loss_ & _Recall (\%)_ & _Precision (\%)_ & _IoU (\%)_ \\ \hline No attention mechanism & 0.057 & 86.2 & 81.9 & 75.4 \\ Add attention mechanism & **0.053** & **88.6** & **84.9** & **77.6** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of different WRDeepLabV3+ under the proposed dataset.  
Figure 8: Effects of different learning rate decay methods on the optimizer.  
Figure 9: Comparison of DeepLabV3+ and WRDeepLabV3+ under CosLR and Adam.
# A Joint Positioning Algorithm in Industrial IoT Environments with mm-Wave Communications
## 5 Simulation Results
### Performance of SRS-Pos under Different Configuration
Figure 8 shows a time estimation error of SRS-Pos with a combination configuration \(\left\{K_{\mathrm{TC}}^{\mathrm{SRS}},N_{\mathrm{symb}}^{\mathrm{SRS}},N_{ \mathrm{PRB}}^{\mathrm{SRS}}\right\}\). In this figure, the ordinate represents the time estimation error, and it is calculated by i (16). Two different colors represents two different comb sizes. It can be observed that the estimation accuracy under \(K_{\mathrm{TC}}^{\mathrm{SRS}}=2\) outperforms that of \(K_{\mathrm{TC}}^{\mathrm{SRS}}=4\). Especially, for \(\left\{2,2,28\right\}\) and \(\left\{4,4,28\right\}\), the performance of the former is around 1.5 dB better, while the SRS-Pos length of the former is twice of the latter and the duration of former is half of the latter. This can prove that the sequence autocorrelation and crosscorrelation plays an more important role for sequence detection than the power cumulation in the time domain. However, under the same comb size configuration, the estimation error decreases with the sequence time domain duration \(N_{\mathrm{symb}}^{\mathrm{SRS}}\).
## 4 Experimental Results
### AE Characteristics
#### 4.3.2 Distribution of AE Events
This is evidence of the exacerbated damage inside the coal element. Moreover, the micro-damage at this stage propagated across the interface between the rock element and the coal element. Many AE events were also found at the bottom corner of the rock element. After the peak stress in Figure 8j, the AE events maintained the increasing tendency inside the R-C specimen. The growth rate was fast at the height of 0-50 mm, while it was relatively weak at the height of 50-100 mm. It should be noted that dense AE events at stage IV were still scattered inside the coal element. Only rare AE events were distributed at the local areas of the rock element. For the R-C-R specimen, the 3D distribution of AE events during the compressive process is displayed in Figure 9. The AE events first appeared at the height of 33.33-66.67 mm. As shown in Figure 9a-c, they were almost located inside the coal element during stage I. No AE events were captured inside the upper and lower rock elements. At stage II, the number of AE events was mainly accumulated inside the coal element, as shown in Figure 9d-h. The distribution space also propagated gradually at the height of 33.33-66.67 mm. At this stage, a few AE events also appeared near the top and bottom coal-rock interfaces. This demonstrates that the micro-damage that stemmed from the coal element had interconnected and spread into the rock element. Afterwards, AE events increased continuously at a height of 33.33-66.67 mm, as shown in Figure 9i,j. The coal element experienced the most damage at the yielding stage. The distribution density of AE events inside the rock elements also gathered slightly at stage III. Ultimately, a weak increasing tendency of AE events inside the R-C-R specimen was presented at stage IV. This can be seen in Figure 9k-n. Massive AE events were concentrated on the coal element. However, the damage was rarely scattered inside the rock element without the formation of a distinct nucleation belt. The distribution of AE events in the R-C-R-C-R specimen during the loading process is revealed in Figure 10. Apparently, the AE events first originated at a height of 20-40 mm at stage I (see Figure 10a,b). Then, sporadic AE events were captured at a height of 60-80 mm. At stage II, the AE events, as shown in Figure 10c,d, were dominantly gathered and generated inside the coal elements. A small amount of AE events also appeared randomly at a height of 40-60 mm, which indicates that the released damage energy spread to the rock element. Subsequently, the number of AE events at stage III not only gathered inside the coal elements, but also in the rock elements (see Figure 10e,f). The growth rate in the coal elements was continuously greater than that in the rock elements. Consequently, the distribution area and density of AE events in the coal elements further thrived at stage IV. The concentration of AE events in the interburden rock element is shown in Figure 10g-j. This provides evidence that damage occurred to the rock element at a height of 40-60 mm.
## Optimized Two-Tier Caching with Hybrid Millimeter-Wave and Microwave Communications for 6G Networks
### Contributions  
Motivated by the potentials of data caching and dual-mode data transmission, we propose a two-tier caching mechanism with hybrid mmWave and \(\upmu\)Wave communicationsto minimize delay. The data caching is executed at both the SBS and UE levels. Moreover, dual-mode transmission is supported between serving SBS and UE. Additionally, mmWave-based D2D communications are utilized to achieve high-speed data services between proximal sets of UE through an LoS link. Furthermore, RL is employed to accurately determine optimal cache decisions while taking into account the limited size of caches, channel conditions, and UE's connectivity patterns. The main contributions of our work are as follows:  
* We propose a novel data caching approach for both the SBS and UE levels. We consider a HetNet environment, where SBSs are equipped with dual-mode transmission facilities. In addition, D2D communications are provisioned through mmWave links.
* We model the data placement problem as an MDP. The RL is utilized to perform cache decisions considering the UE's evolving data demanding patterns, cache sizes, and mobility. The network delay assessment takes into account dual-mode data transmission between the SBS and UE. This includes leveraging uWave or mmWave data transmission between the serving SBS and UE. Moreover, to further improve communication capabilities, D2D communication through mmWave transmission is introduced between two sets of UE.
* Experimental results demonstrate a high performance gain in our proposed caching approach in comparison with the state-of-the-art caching approaches under various system parameters.  
The remaining paper is organized as follows. Section 2 introduces the system model and problem formulation. In Section 3, an RL-based data caching decision algorithm is proposed to deal with the delay minimization problem. In Section 4, the simulation results are analyzed and a detailed discussion is provided. Finally, in Section 5, the conclusion is provided. Table 1 provides the key notations.
## 3 Indoor Positioning Systems
### Hybrid Indoor Positioning Systems
#### 3.6.5 Other Hybrid Indoor Positioning Systems  
In addition to the traditional radio-based sensors (Wi-Fi, RFID, Bluetooth, etc.), inertial sensors (barometer, gyroscope, accelerometer, magnetometer), and fixed or mobile cameras used in computer vision, other sensors are used in experiments and combined to provide indoor locations.  
[PERSON] et al. described a solution for fusing infrared signals and images obtained by cameras, with the results inserted into a covariance matrix. A fusion algorithm based on the Monte Carlo filter consumes the matrix data and estimates the locations in indoor environments [105]. In the study, the authors combined a set of five infrared detectors and a camera to identify the position of a target by hyperbolic trilateration. It is obtained from phase difference measurements with the infrared sensor and by homography with the camera. The results reached a precision of around 0.70 m without the presence of multiple paths.  
[PERSON] et al. proposed a localization technique by fusing several classifiers based on the strength of the received signal (RSS) of visible light, in which different intensity-modulated sinusoidal signals emitted by LEDs are captured by photodiodes placed at various points in the grid [5]. The authors first obtained RSS readings to compose a fingerprint for each site. The signals were subjected to multiple data fusion and machine learning algorithms. Two algorithms proposed by the authors, grid-independent least square and grid-dependent least square (GD-LS), combine the outputs of the classifiers. Experiments were performed to show that the probability of having an average square positioning error of less than 0.2 m by the GD-LS is 93.03%.
## 4 Results and Discussion
### Effect of CAWA on the Geotechnical Properties
#### 4.2.3 Results of California Bearing Ratio (CBR) Tests  
It is critical to obtain CBR values for the materials used in a subgrade of pavement structure or subbase layer in order to evaluate their effectiveness under traffic loading. Soaked CBR tests on specimens of silty sand soil treated with varying proportions of CACW were undertaken to imitate the poorest field circumstances. The experiment was conducted three times, and the average is shown in Figure 7. From the figure, it is clear that when 10% of CACW is added, the CBR value increases from 21% to 40%. When the percentage of CACW increased to 15%, the CBR value dropped from 40% to 25%. The value of the CBR has dropped by roughly 37.5 percent. Additionally, the decrease in CBR value, which went from 25% to 18% when CACW addition increased from 15% to 20%, is strongly connected  
Figure 6: Effects of the addition of CACW on Ultrasonic pulse velocity at different mixes of CACW.  
with the rise in CACW percentage. This is because when CACW concentrations rise, more water is absorbed during the soaking phase, leading to an increase in fine materials, which in turn, causes a loss in strength and consequently lower CBR values. The same trend is observed with a higher value of CBR, as seen in Figure 7 when 2% of cement was added to the mix. It is worth noting that during the soaking period, the swelling was tracked and recorded, and the results showed that the soaked CBR specimens had no swelling. It can be deduced that adding 10% CACW with 2% cement to the soaked CBR value produced the greatest value, i.e., 60% and that it may be utilized as a subbase layer in the construction of high-traffic loading highways in compliance with standards. The process of filling and densification introduced by 10% CACW in the soil structure provides this strength, as seen in Figure 7. As one can see, the CACW powder acts as a void filler, forming a dense structure beneath the loading plunger of the CBR machine; furthermore, since its ratio is low (10%), water absorption is also low, enhancing the soil-CACW mixture's resistance to penetration and resulting in a higher CBR rating of 40%, which reached 60% when 2% of cement was added to the mix.
## 3 Results
### Comprehensive Management Model for Collapsed Area
#### 3.4.2 Comprehensive Management Planning for Collapsed Areas  
The management model designed for the comprehensive management of the coal mining subsidence area in Zhaogu Mining Area indicates the following results: the village relocation area in the non-waterlogged region of the subsidence area is 72.11 \(\text{hm}^{2}\); the road rehabilitation area covers 39.83 \(\text{hm}^{2}\); there is a potential for reclaiming 1178.75 \(\text{hm}^{2}\)as cropland; 74.68 \(\text{hm}^{2}\) can be reclaimed as woodland; and an additional 74.68 \(\text{hm}^{2}\) can be reclaimed as forestland. The extent of excavation in the \"digging deep\" region is 235.24 \(\text{hm}^{2}\), whereas the extent of filling in the \"shallow padding\" area is 267 \(\text{hm}^{2}\). A total of 783.36 \(\text{hm}^{2}\) of the waterlogged area in the subsidence area will undergo treatment. After the treatment, the water utilization area in the waterlogged area will be reduced to 516.36 \(\text{hm}^{2}\). Out of this, 130.51 \(\text{hm}^{2}\) will be allocated for integrated farming, another 130.51 \(\text{hm}^{2}\) for ecological farming, and the remaining 130.51 \(\text{hm}^{2}\) will also be used for ecological farming. The area for eco-tourism is 144.27 \(\text{hm}^{2}\), the area for the fish and lotus joint zone is 96.27 \(\text{hm}^{2}\), and the area for the fishery-photovoltaic park is 145.31 \(\text{hm}^{2}\). The management plan for the collapsed region is presented in Figure 12. Annually, this plan can yield direct and indirect economic advantages and ecological and social benefits to the Zhaogu Mining Area. Additionally, it can serve as a valuable reference for future management of subsidence areas.
# AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment
## 3 Materials and Methods
### Dynamic Assembly Pose Tracking
#### 3.2.3 Texture Modality  
RGB data can be utilized not only for region modality but also for texture modality, especially when the tracked assembly parts have distinctive surface texture features. This can significantly reduce the drift phenomenon in practical applications and further improve the stability and reliability of tracking.  
To account for local object appearance, a key point-based probability density function is applied in this paper. As shown in Figure 4, first, for each image frame, we detect the key point within the image within the minimum horizontal rectangular bounding box region of the mask of the tracking object. The feature descriptor is then extracted based on the detected key points and matched with the feature descriptor of the key frame. If the directional difference of feature descriptors between the current frame and the existing key frames exceeds a certain threshold, the current frame will be regarded as a key frame.  
Figure 3: Visualization of corresponding points in depth images: (**a**) 3D surface points in depth image; (**b**) corresponding associations (depicted by red line segments) between correspondence points (shown in yellow) and 3D surface points of CAD model (shown in blue).  
Finally, if a frame is considered a key frame, a depth rendering is generated, and for each key point falling on the contour, a reconstruction of the 3D model point is performed, its feature descriptor is added, and the non-occluded 3D points are stored in the key frame.  
Our method applies the ORB algorithm to extract key points. Although there is still a certain gap between the ORB algorithm and the SIFT algorithm in terms of accuracy, ORB is faster in calculation and can achieve a better balance between tracking quality and efficiency in AR application scenarios.  
With the 2D key points detected by the current frame and the matching 3D model points from the key frame, a probability density function can be established. Assuming that the reprojection error follows a normal distribution, the probability density function can be constructed to describe independent point pairs.  
\[p(\theta\mid D_{t})\propto\prod_{i=1}^{n}\ exp\left(-\frac{1}{2\sigma_{t}^{2}} \rho_{tukey}\big{(}\parallel x_{i}^{\prime}-x_{i}(\theta)\parallel_{2}\big{)}\right) \tag{5}\]  
where \(D_{t}\) is texture modal data; \(x_{i}^{\prime}\) is the key point detected in the current frame; \(x_{i}(\theta)\) is the 3D model point projected onto the imaging plane; \(\sigma_{t}\) is a user-defined standard deviation, which is used to represent the uncertainty of texture modality relative to other modalities; and \(\rho_{tukey}\) is the Tukey error norm, which is used to minimize the effect of outliers.
## 3 Results  
### ML Modeling Screening Phase Results  
The modeling screening phase is of huge importance since no gold standard is available for presenting trustworthy results. It was initially performed for trying and provisioning most of the well-known Scikit learn's supervised learning classifiers. Using naive Bayes did not result in significant performance metrics, giving overall accuracies no higher than 0.429. The assumption that all features are independent can be considered a limitation in this particular case. A similar model evaluation was retrieved using the logistic regression classifier, resulting in an accuracy of 0.425. The linearity between the dependent variable and the independent variables can be considered as a limitation. Furthermore, we tried the K-nearest neighbor algorithm (KNN) which was not able to retrieve a greater accuracy than 0.325. This can be potentially explained due to the high dimensionality as well as the sensitivity of choosing the neighbors based on the distance criteria. Utilizing the principal component analysis (PCA) with a support vector machine algorithm (SVM) resulted in achieving an overall accuracy of 0.497, which was also evaluated as a low performant approach.  
We concluded that the most promising insight we retrieved was in using the decision tree approach, where we achieved a preliminary overall accuracy value of 0.764. Using the decision tree ('gini' attribute selection measure in correlation with the 'best' splitter as splitting strategy approach) provides additional benefit since the advantageous characteristic of decision trees is their comprehensibility. Although it has a simple visualization representation, this approach is beneficial because it forces the root split by some feature abundance distributions. The screening modeling phase results are summarized in Table 1.  
Considering the decision tree algorithm's accuracy, we continued modeling utilizing the tree-based random forest algorithm assuming that the performance metrics will be additionally improved by taking advantage of the tree-related majority voting.