esa-sceva/satcom-chunk-collection huggingface dataset:
# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 3 Materials and Methods
### Classifier Head  
The mask head of the Mask Scoring R-CNN method requires a classifier head to provide a category and bounding box for small targets, where the bounding box is used to clip the corresponding feature layer. It is then sent to the network for segmentation, and the final target mask is selected according to the target category. During detection, the original target detector predicts numerous target frames containing small targets; however, these target frames are redundant, and some target frames highly overlap. Therefore, numerous duplicated boxes must be filtered out in a nonmaximal, suppressed manner. We believe that the input feature involves numerous bounding box information because it is obtained through dozens of convolution layers. In this study, we adopted a deeper network structure (Figure 6). Four convolutions were used to replace the first fully connected layer to reco  
Figure 5: The structure of BiFPN.  
the input features. To weaken the influence of external information during detection and highlight the target features, the output size of each convolution layer is always consistent with the input [42].  
In this study, the number of categories of airport small targets and other daily detection tasks was significantly lower than the number of common targets in the COCO dataset, which contains 80 types of targets. This study only detects and locates people. Further, using only the fully connected layer to complete two tasks is obviously not conducive to the positioning of small targets on the apron. Therefore, given that the classification task is not complex, and considering that the use of fully connected and convolutional branches greatly increases the network parameters and computational consumption, we use a convolutional layer that is more suitable for localisation to complete the classification and localisation tasks. As mentioned above, the network was designed to follow the setup of R-CNN, with four layers of shared convolution and one fully connected layer. The convolutional layer was used to extract local information suitable for classifying and locating targets in the proposed box, and the full connection introduced position coding, integrating global information through different position information. At the network parameter level, the number of parameters of the original network full connection was \(14\times 14\times 256\times 1024=51,380,224\), whereas the parameter quantity of the convolutional layer was only \(3\times 3\times 256\times 256\times 4=2,359,291\), which was significantly smaller than the number of parameters of the full connection [43].
## 5 Conclusions  
The VRS GNSS technology is introduced as a very interesting approach not only for collection of magnetic data, but also for high-precision real-time data acquisition for positioning that can be extended to any geophysical research. It presents great advantages in terms of operational flexibility and cost-effectiveness. VRS requires only a single GPS receiver because no base station is necessary, reducing labor and equipment costs. It simplifies the operational logistics of field work and can operate at distances separated 50-70 kms from the reference stations without degrading accuracy. The results obtained in the magnetic survey with the compact integration of GSM-19 magnetic sensor with VRS GNSS technology are of great interest for archaeological purposes.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 2 Materials
### GEE Data and Derived Products  
In this study, we used various freely available datasets from Google Earth Engine to enhance the research. Specifically, we analyzed data from the Sentinel-1 satellite mission, which has already been used for diverse Earth observation applications, including flood detection [35; 36]. For our research, we employed the Interferometric Wide Swath (IW) mode of the S-1 mission, utilizing the VH polarizations (vertical-horizontal), which are more sensitive to changes on the land surface [37]. The IW mode provides extensive coverage and high-resolution imagery, making it suitable for flood detection and mapping [38]. The use of Sentinel-1 data, with its wide coverage, high-resolution imagery with a 10 m resolution, and frequent temporal updates, enabled us to conduct robust flood detection and mapping in the investigated study areas.  
Moreover, we employed the HydroSHEDS Digital Elevation Model (DEM) to mask pixels representing steep land surfaces [39]. The inclusion of topographic information through the DEM allowed us to refine the flood mapping process and focus on areas where flooding is more likely to occur, enhancing the reliability of our results. We also relied on the JRC Global Surface Water (GSW) Mapping Layers dataset, which provides comprehensive maps of surface water location and temporal distribution from 1984 to 2020 [40]. This dataset also offers valuable statistics on the extent and changes of water surfaces over time. By leveraging this dataset and incorporating seasonality information, we aimed to accurately delineate and analyze the distribution of permanent and semi-permanent water bodies, contributing to a better understanding of global surface water dynamics. Finally, we used the MERIT Hydro dataset [41] to obtain a global flow direction map at a high resolution of 3 arcsec.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Datasets** & **Raw Data** & **Spatial Resolution** & **Source of Data** \\ \hline SAR-image & Sentinel 1(GRD) & 10 m & Copernicus \\ Hydrosheds & Based on SRTM & 90 m & USGS \\ MERIT Hydro & Based on GIVBM, CSWO, and OSM & 90 m & NASA \\ GSW & Global surface water & 30 m & JRC \\ DEM & Digital elevation map & 10 m & Grass site \\ TIF files & Ground data & Shapefile (.sbp) & CEMS \\ Maps & Reference maps & 60 m/30 m/10 m & CEMS \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of data used.
# A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
## 4 System Verification Process  
The nine-degrees-of-freedom turntable system is used to simulate the approaching process of the two spacecraft at a relative distance of 30 m to 0.3 m. The setup of the experimental system is shown in Figure 7.  
The target docking surface model is installed on the target platform, and the camera is installed on the chaser platform. The image signal is transmitted to the control computer through the optical transceiver, and the control computer encapsulates the received video data in real time by simulating the flight control network protocol and then uses the local area network to forward it to the auxiliary prediction computer. In addition, in the experiment, the incident angle of sunlight under the real lunar  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Confusion Matrix** & \(L=1\) & \(L=2\) & \(L=3\) & \(L=4\) \\ \hline \(L=1\) & 86 & 3 & 0 & 0 \\ \(L=2\) & 2 & 32 & 3 & 0 \\ \(L=3\) & 0 & 0 & 17 & 1 \\ \(L=4\) & 0 & 0 & 0 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix of the test set.  
Figure 6: Decision tree training algorithm process.  
Figure 7: Diagram of a semi-physical experiment system.  
solar simulator with a three-degrees-of-freedom servo is used to generate parallel light to illuminate the entire target plane.  
The data display and human-computer interaction interface is shown in Figure 8. The data display areas show the trajectories of the target in different views. When a rendezvous and docking operation ends (regardless of whether it ends normally or abnormally), the 6 DOF rendezvous and docking deviations are obtained, and a sample is also generated. In the process of rendezvous and docking, the mathematical simulator outputs six degrees of freedom at each moment. According to the small hole imaging model and the internal parameters of the camera, a simulated image \(I_{i,j}\) is generated from the mechanical model of the target end face. \(F_{i,j}\) is then obtained by extracting features from \(I_{i,j}\). By using Equation (2), the rendezvous and docking deviations are mapped to a safety level. With the features and the corresponding safety level, we can construct the training samples to train the decision tree. The whole process realizes the transformation from image \(I_{i,j}\) to rendezvous and docking safety level \(L\). At the test experiments, the surveillance camera takes an image of the target, as shown on the left side of Figure 8, and the decision tree predicts the safety level of the rendezvous and docking, which is displayed in text in Figure 8. The four safety levels mean \"Normal\", \"Early Warning\", \"Warning\" and \"Abnormal\", respectively. In order to visually display the real-time status of the rendezvous and docking, the position of the target is shown in Figure 8 in different views with a red cross.  
A nine-degrees-of-freedom turntable is used to run 50 sets of rendezvous and docking trajectories, and the results of safety judgments using the above methods are shown in Table 3.
## 5 Discussion  
### Findings from the ESPP Experiment  
Our experiment revealed that the LTSM and its derivative model outperformed baseline models, including LR and SVM. The baseline ML models could not capture any at-risk students at the end of the sixth week in a 16-week course with F1-scores below 0.8. On the other hand, the LSTM-based models (i.e., LSTM, CNN-LSTM, and Conv-LSTM) could effectively extract the temporal patterns inside the VLE data set with F1-scores of 0.84, 0.88, and 0.91, respectively. The traditional ML experiment had poor validation performance due to serious overfitting during the training phase, as exhibited in Table 4, especially when the data set was highly imbalanced. From the aspect of interventions, the combination of convolutional and LSTM layers had greater potential in both research and implementation, especially in spatiotemporal data processing. Nowadays, more and more end devices such as IoT that connect to the network might be utilized to track students' learning in real time. This DL architecture can be used to generate intelligent predictive modeling that improves the VLE learning process in the future.  
The major purpose of this study was to improve the performance in early prediction cases while maintaining the explainability of the prediction results. Two promising models were revealed from the experimental results, namely, CNN-LSTM and Conv-LSTM. CNN-LSTM has proven to produce higher accuracy but with a trade-off of an increasing number  
Figure 5: The visualized data set of two types of student performance with its interpretability: (**a**) ‘Fail’ and (**b**) ‘Pass’.  
of training parameters. On the other hand, the Conv-LSTM provided the highest performance but with a slightly increasing number of trained parameters. The total parameters used in the training phase for LSTM, CNN-LSTM, and Conv-LSTM were 1906, 3714, and 1978, respectively. The more parameters used in a predictive model, the longer the training process because all parameters were calculated in propagation learning. However, only an inference process was performed in the implementation phase. Thus, the difference in time execution could be minimized. If given the limited data set, traditional ML methods are insufficient to extract the temporal patterns inside the data set, resulting in overfitting learning. Therefore, this research supports the development of intelligent predictive VLE with two proposed LSTM-based model architectures improving prediction performance.
# Could _Gnomoniopsis castaneae_ Be Used as a Biological Control Agent against Insect Pests?
###### Abstract  
_Gnomoniopsis castaneae_ is the cause of the chestnut brown rot but has been also regarded as an important mortality factor for the chestnut gall waspy _Drryocosmus kuripillus_. The question to whether _G. castaneae_ could serve as a natural biocontrol agent against insect pests is investigated in the present study. We used three serious insect pests as experimental model insects: _Plodia interpuctella_ and _Trogoderma granarium_, which are important pests of stored products, and _Myzus persicae_, a cosmopolitan, serious pest of annual and perennial crop plants. Although chemical pesticides represent effective control means, they are also related to several environmental and health risks. In search for alternative pest management methods, scientific interest has been focused, interalia, on the use of entomopathogenic fungi. While _Isaria funosrosae_ has long been recognized as an effective control agent against several pests, _G. castaneae_ has been very little studied. The present study examined whether and to what extent _G. castaneae_ and _I. funosrosae_ exhibit insecticidal activity against fourth-instar larvae of _P. interpunctella_ and _T. granarium_ and adults of _M. persicae_. Mortality was examined in interrelation with dosage and time exposure intervals. Both fungi exhibited pesticidal action. However, _G. castaneae_ induced noteworthy mortality only at very high doses. In general, we concluded that _G. castaneae_ failed to cause high insect pathogenicity at normal doses and may not be an efficient biocontrol agent compared with other entomopathogens. On the other hand, our study reiterates the pathogenic potential of _I. funosrosrosae_. More studies are needed to further our insight into the potential of EF species as a component of IPM.  
biocontrol; _Gnomoniopsis castaneae_; _Isaria funosrosae_; _P. interpunctella_; _T. granarium_; _M. persicae_; survival time
## 5 Simulation Results
### Performance Comparison with MF-TDMA  
To adequately verify the advancement of the proposed method, we provide simulation results of MF-TDMA [29] as another benchmark. Let \(N\) denote the number of total carriers shared by the \(M\) UTs. The bandwidth for each carrier can be derived as \((B-\tau B)/N\), and the effective time-slot length for each UT is \(N/M\) s in unit time. Then, the ergodic sum rate for the MF-TDMA scheme can be derived as  
Figure 10: BER with variable \(\text{SNR}_{\text{UT}}\) when \(M=3\).  
Figure 9: BER with variable \(\text{SNR}_{\text{HS}}\) when \(M=5\).  
\[\mathbb{E}[\bar{R}] =\mathbb{E}[\bar{R}_{0}]+\sum_{i=1}^{M}\mathbb{E}[\bar{R}_{i}]\] \[=\frac{\tau B}{\ln 2}\int_{0}^{\frac{|\beta_{0}|^{2}\beta_{0}}{1+ \beta}}\frac{e^{-\frac{1}{4}\frac{\tau B\tau}{|\beta_{0}|^{2}\beta_{0}-\tau-B \tau}}}{1+x}dx+\frac{N}{M}\frac{B-\tau B}{N}\frac{1}{\ln 2}\sum_{i=1}^{M}\int_{0}^{ \frac{N|\beta|^{2}\beta_{0}}{(B-\tau B)}}\frac{e^{-\frac{1}{10}\frac{(B-\tau B )\tau}{|\beta_{0}|^{2}\beta_{0}-(B-\tau B)\tau}}}{1+x}dx. \tag{42}\]  
Using the same system parameters as Section 5.1, we provide simulation results of the ergodic sum rate for the three methods in Figure 11, where \(\alpha=0.02\) and \(N=3\). It is noteworthy that the proposed spectrum-saving method can provide a promising performance compared with spectrum isolation and MF-TDMA schemes.  
To discover how the performance gap behaves with shared bandwidth, we provide simulation results of the three methods in Figure 12. Note that the 'Gap' curve represents the performance gap between the spectrum-saving and the MF-TDMA schemes. It can be noticed that MF-TDMA is competitive when \(\tau<0.3\); however, the spectrum-saving outperforms the other two methods with the increase of shared bandwidth.  
Figure 11: Ergodic sum rate with variable \(\beta\) when \(M=5\) and \(N=3\).  
Figure 12: Ergodic sum rate with variable \(\tau\) when \(M=5\) and \(N=3\).
# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
## 1 Introduction
Based on the above analysis, this study introduces a KD fused fault diagnosis approach within an IoT architecture for BMU operational data collection and fault state analysis. The proposed method integrates online equipment operational data with existing expert knowledge bases, enabling the mining of massive networked operational data to establish a global data relationship network and fault diagnosis inference links. This framework effectively achieves the intelligent management of BMU industry knowledge, facilitates smart fault analysis, and promotes information interconnectivity across the BMU industry. The main contributions of this paper are as follows:  
1. Establishment of an IoT architecture to acquire and store operational data from distributed BMU systems, forming a comprehensive dataset that serves as the foundation for system status analysis and fault detection. 2. Development of a KD fusion framework that integrates expert-system prior knowledge with real-time operational data, which constructs multi-source relational association rules based on operational parameters and fault samples, ensuring the reliability of the proposed fault diagnosis model. 3. Implementation of a multi-BMU integrated test system to validate the approach's effectiveness. Experimental results demonstrate that the method simultaneously satisfies both accuracy and timeliness requirements for equipment fault diagnosis while providing comprehensive and innovative fault maintenance strategies. The rest of this article is arranged as follows: Section 2 presents the comprehensive IoT system framework and the KD integrated fault diagnosis methodology. In Section 3, an experimental system was built to test the performance of this method. Section 4 concludes this paper.
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 4 Experimental Results
### Result Analysis  
Since the RBENN enhances the reference CU before MC, the performance of the RBENN can be more clearly represented by comparing the PSNR of the reference CU and the original CU before and after the RBENN. Table 3 shows the average gains of the CU with different sizes in the four video sequences. The PSNR gains were calculated as the average of all CUs. Considering the different sizes and numbers of each CU, the PSNR gains were calculated as  
\[PSNR\ gain=\frac{\sum_{W,H\in\{16,32,64\}}\left[(W\times H)\times\sum PSNR_{W, H}\right]}{\sum_{W,H\in\{16,32,64\}}\left[(W\times H)\times\sum NUM_{W,H} \right]}, \tag{7}\]  
where \((W\times H)\) is the area of a CU, \(\sum PSNR_{W,H}\) represents the sum of PSNR gains for each CU after the RBENN, and \(\sum NUM_{W,H}\) denotes the total number of CUs. According to Table 3, we can see that there was a 0.052 dB PSNR gain for all CUs larger than \(16\times 16\), on average. For _BQSquare_, the PSNR gain reached 0.110 dB, which was consistent with the coding results. We can also see that the RBENN performed better for CUs with sizes of \(\{16\times 16\), \(32\times 16\), \(16\times 32\), \(16\times 64\), \(64\times 16\)}. We believe that this was because the training set used for training RBENN1 was large, leading to a higher generalization ability. Figure 10 visually displays the distribution of the PSNR gains. We can see that, in the _RaceHorses_ the RBENN achieved significant negative gains for a few CUs. This is the reason why limited coding improvement was obtained for it. In the future, we will add flags in the coding process to further improve the coding efficiency.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Class D**} & \multicolumn{2}{c}{**LDP**} \\ \cline{2-3}  & **EncTime** & **DecTime** \\ \hline BasketballPass & 70,977\% & 17,959\% \\ \hline BQSquare & 64,455\% & 30,334\% \\ \hline BlowingBubbles & 58,523\% & 26,708\% \\ \hline RaceHorses & 47,806\% & 26,507\% \\ \hline Average & 56,653\% & 24,021\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity of our method.  
Figure 9: Quality enhancement results on reference CU performed by the RBENN.
# Provenance and Paleo-Environment of the Late Carboniferous Bauxite Formations in Southern Shanxi
## 5 Discussion
### Metallogenic Environment
The identification of coal seams and mudstones further corroborates the transitional environmental changes during the metallogenic period, with the observed decrease in Sr/Ba in the top sandstone suggesting a shift to a freshwater-brackish swamp environment conducive to coal formation during diagenesis. In weathering processes, the Sr/Cu ratio serves as an effective indicator of climatic conditions. A ratio of 1.3 \(<\) Sr/Cu \(<\) 5 suggests a warm climate, while Sr/Cu \(>\) 5 indicates a hot climate [43]. In the Pinglu area, only the top sandstone samples Y2 and Y3 and the bottom samples Y10 and Y11 of the Jinyu profile have Sr/Cu values of 1.3 \(<\) Sr/Cu \(<\) 5, whereas the remaining are greater than 5, indicating a hot climate at that time. The CIA can also be employed to infer paleoclimate. When CIA \(>\) 80, it reflects intense chemical weathering in hot and humid climates; when 60 \(<\) CIA \(<\) 80, it signifies moderate weathering in warm and humid climates; and when 50 \(<\) CIA \(<\) 60, it indicates weak weathering in cold and dry climates [44]. In the study area, CIA values range from 84.6 to 99.8, signifying that the aluminum-bearing strata underwent intense weathering during sedimentary diagenesis. This suggests a hot and humid tropical rainforest climate [45], consistent with the North China Craton's position at 17\({}^{\circ}\)-18\({}^{\circ}\)N during the Late Carboniferous to Early Permian, belonging to a tropical-subtropical climate [46]. In summary, the layers enriched with bauxite were formed in a reducing environment predominantly shaped by marine conditions, with a sedimentary environment that exhibited a tendency toward weak alkalinity, but primarily acidic in nature. Through the analysis of the regional metallogenic environment, the lithofacies paleogeography and the size of the ore, [PERSON] [47] put forward the bauxite in the area also conforms to the environment of weak alkaline reduction. The water environment is primarily saline, accompanied by a hot and humid climate and intense chemical weathering. The depositional diagenetic stage underwent significant environmental changes, evolving into a freshwater-brackish environment influenced by marine-terrestrial transitional environments. During the humid and hot climate stage, weathering is further intensified, and the desilication and deferritization of aluminous rocks become more pronounced. At the same time, abundant rainfall and the flourishing of vegetation create extensive swamp environments in this area, providing favorable conditions and material sources for the formation of the extensive coal series found in the upper part of the bauxite deposits in this region, favorable to Late Carboniferous coal formation.
## 2 Materials and Methods
### Statistical Analysis  
This study conducted statistical analysis using ImageJ 28.0 software (IBM(r), Armonk, NY, USA). First, the Q-Q plot was used to examine the normal distribution of participants'background variables--including age, height, weight, BMI, SMM, and BFM. For the main research variables, including lower limb joint flexion angles (hip joint, knee joint, ankle joint), lower limb explosive strength (take-off time, ground reaction force, force rate, hang time, and jump height), advanced layup success rate, and sports injury rate--an analysis of covariance (ANCOVA) test and effect size calculations were performed. The partial eta squared (\(\eta^{2}\)) value was obtained to represent the proportion of variance explained by the variables, with the significance level set at \(p<0.05\). Additionally, partial eta squared (\(\eta^{2}\)) was calculated to measure the effect size, with \(\eta^{2}>0.14\) indicating a large effect size [36]. Furthermore, Pearson correlation coefficients were used to analyze the relationships among lower limb joint range of motion, lower limb explosive strength parameters, and advanced layup success rate. A multiple linear regression model was then constructed with advanced layup success rate as the dependent variable and lower limb joint range of motion and lower limb explosive strength as independent variables to explore the predictive effects on advanced layup success rate. Finally, a paired sample \(t\)-test was conducted to analyze the difference in sports injury rates between the pre-test and post-test measurements.