esa-sceva/satcom-chunk-collection huggingface dataset:
# A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis
## 5 Conclusions  
The proposed CDAN aims to solve the negative transfer problem in transfer learning based fault diagnosis models and improve the accuracy of unlabeled bearing fault diagnosis. By employing two types of feature extractors and multi-linear mapping, the classifier's predicted classification information is fed back to the domain discriminator to enhance the learning of domain-invariant features. To validate the feasibility of this model, experiments were conducted on two datasets. On the CRWU dataset, data under different loads and positions were respectively set as source domain and target domain datasets, verifying the superiority and stability of the CDAN. Similar results were observed on the PU dataset. Using t-SNE, the visualization of classification features learned by different models shows that the CDAN clustering has good intra-class and inter-class compactness. Compared with four other advanced methods, the CDAN improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults. The CDAN can be applied for fault diagnosis of supporting bearings in motors. Further research is needed to determine whether it can be used for fault diagnosis of rotors, coils, and other components in motors. These results indicate that the CDAN effectively reduces cross-domain distribution discrepancies in sample transfer learning and is a promising method for rolling bearing fault diagnosis. CDAN requires sufficient laboratory failure samples as support, which requires a lot of manpower and resources. The generation of fault data through digital twins can perfectly solve this problem, which is also the future research direction of this study.  
Conceptualization, Y.W. and Z.Z.; methodology, Y.W. and Z.Z.; software, C.X. and X.L.; resources, Y.W. and Z.Z.; writing--original draft preparation, Y.W. and Z.Z.; writing--review and editing, Y.W. and Z.Z.; project administration, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Fund of State Key Laboratory of Coal Mine Disaster Prevention and Control (2022 SKLKF14, 2022 SKLKF07), the China Postdoctoral Science Foundation (2024M753655), the Ministry of Industry and Information Technology Project under Grant 167; the Independent Key Project of Intelligent Collaborative Innovation Center of China Coal Technology and Engineering Group Chongqing Research Institute under Grant 2023 ZDZX04, the Innovative Research Group of Universities in Chongqing (CXQT21024), the Science and Technology Research Plan Project (KJQN202200835),and the Scientific Research Startup Project for High-Level Talents (No. 2256013).  
Figure 12: Feature clustering diagram of Model 3.
###### Abstract  
With the increase in the number of Global Navigation Satellite System (GNSS) satellites and their operating frequencies, richer observation data are provided for the tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS). In this paper, we propose an efficient and robust combined navigation scheme to address the key issues of system accuracy, robustness, and computational efficiency. The tightly combined system fuses multi-source data such as the pseudo-range, the pseudo-range rate, and dual-antenna observations from the GNSS and the horizontal attitude angle from the vertical gyro (VG) in order to realize robust navigation in a sparse satellite observation environment. In addition, to cope with the high computational load faced by the system when the satellite observation conditions are good, we propose a weighted quasi-optimal satellite selection algorithm that reduces the computational burden of the navigation system by screening the observable satellites while ensuring the accuracy of the observation data. Finally, we comprehensively evaluate the proposed system through simulation experiments. The results show that, compared with the loosely coupled navigation system, our system has a significant improvement in state estimation accuracy and still provides reliable attitude estimation in regions with poor satellite observation conditions. In addition, in comparison experiments with the optimal satellite selection algorithm, our proposed satellite selection algorithm demonstrates greater advantages in terms of computational efficiency and engineering practicability.  
Keywords:tightly coupled navigation system; GNSS/INS; computational efficiency; satellite selection algorithm +
Footnote †: journal:
## 3 Results
### Ammonium and Phosphate Adhesion onto Loaded MZP-PM
#### 3.4.2 Phosphate Speciation onto Loaded MZP-PM  
The finding of phosphate speciation by MZP-PM are given in Table 3. The results demonstrate that a considerable part of phosphate is retained onto MZP-PM because of the incorporation of La\({}^{3+}\) (La-P) with 64.3 \(\pm\) 3.86%. Therefore, it indicated that the La-OH groups of MZP-PM were mainly responsible for the adsorption of phosphate. The 20.78 \(\pm\) 5.31% of loosely bounded phosphate fraction (LB-P) immobilized was related and the 14.92 \(\pm\) 2.89 of residual phosphate (R-P) was lower, illustrating that the process of removal of phosphate by MZP-PM was not irreversible, which would have an important in the removal of phosphate application. Hence, it was concluded that the mechanisms of phosphate adsorption on MZP-PM mainly lie on ligand exchange and electrostatic interaction. The proposed adsorption mechanisms of NH\({}_{4}\)\({}^{+}\)-N and TP onto MZP-PM are given in Figure 6.  
Figure 5: Results of Nemerow index in the artificial rainfall event.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Q\({}_{e}\) (N)** & \multicolumn{2}{c}{**IE-N**} & \multicolumn{2}{c}{**PA-N**} \\ \hline mg/g & \multicolumn{2}{c}{mg/g} & \% & \multicolumn{2}{c}{mg/g} & \% \\
7.94 & \multicolumn{2}{c}{6.17 \(\pm\) 0.21} & 77.8 \(\pm\) 2.52 & \multicolumn{2}{c}{1.77 \(\pm\) 0.14} & 22.2 \(\pm\) 1.76 \\ Q\({}_{e}\) (P) & La-P & \multicolumn{2}{c}{LB-P} & \multicolumn{2}{c}{R-P} \\ mg/g & mg/g & \% & mg/g & \% & mg/g \\
2.07 & 1.33 \(\pm\) 0.08 & 64.3 \(\pm\) 3.86 & 0.43 \(\pm\) 0.11 & 20.78 \(\pm\) 5.31 & 0.31 \(\pm\) 0.06 & 14.92 \(\pm\) 2.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Adhesion of ammonium and phosphate adsorbed onto MZP-PM (25 \({}^{\circ}\)C, pH = 7, initial concentration of N = 20 mg/L, P = 5 mg/L, time = 24 h).
# Modelling Regime Changes of Dunes to Upper-Stage Plane Bed in Flumes and in Rivers
## 6 Results with River Conditions  
Although the new dune evolution model is still in a developing stage, from a practical point of view it is interesting to test how the model behaves under river conditions. For this purpose the model is run with similar conditions to the Dutch river Waal during the flood of 1998. At the peak of this flood wave, the total discharge was 6250 m\({}^{3}\)/s in the river Waal [47]. For simplicity, the dune evolution model is applied to only the main channel; interactions with the floodplains are not taken into account here. It is assumed that 60% of the peak discharge went through the main channel. The main channel is assumed to be 300 m wide, which corresponds well to the width of the main channel along the river Waal in the SOBEK model made by [PERSON] and used by [PERSON] (2012) [48]. This would make the specific discharge 12.5 m\({}^{2}\)/s, which is rounded to 13 m\({}^{2}\)/s for the current study. To simulate these conditions, a \(D_{50}\) of 1.2 mm (in the range presented by [PERSON] et al., 2008 [49]), a slope \(i\) of 1 \(\times\) 10\({}^{-4}\)[50], and a hydrograph as presented in Figure 10 are used as input for the model. The settling velocity corresponding to the used \(D_{50}\) is \(w_{s}\) = 0.13 m/s. This hydrograph leads to water depths between 8.5 and 11.1 m, which correspond well to the typical water depths in the Waal for the flood of 1998 as reported by [PERSON] et al. (2002) [47].  
Compared to the flood wave of 1998, as shown by [PERSON] et al. (2002) [47], we kept the duration of the rising limb the same, while we shortened the duration of the falling limb somewhat in order to create a symmetric flood wave. The entire duration of the flood wave presented here is exactly 12 days or 288 h. The calculation time is very reasonable; with a time step of 2 min, the model goes through 12 days modelled time in under 55 min real time. To let the model adjust to the hydrodynamic conditions, the flood wave is modelled  
Figure 10: Hydrograph of the river scenario, which is based on the 1998 flood in the river Waal.  
twice. The bed configuration of the first flood wave is used as input for the second flood wave. The results of the second run are reported in Section 6.1.  
To test if a transition to the upper-stage plane bed can be modelled for river conditions, the model is also run with a 5% higher discharge than for the scenario above. This leads to water depths between 9 and 11.4 m, which is about 5% higher than the water depths in Waal for the flood of 1998 as reported by [PERSON] et al. (2002) [47]. The duration of the flood wave is the same as in the other scenario, and the calculation time is just under 35 min. This is less than before, because in this scenario dunes are washed out during a part of the run, which lets the model solve the flow equations more efficiently. Again, to let the model adjust to the hydrodynamic conditions, the flood wave is modelled twice. The results of the second run are reported in Section 6.2.
# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 5 Discussion  
The proposed TTCF model combines two measures to make full use of the surplus energy of the sensor nodes to improve the efficiency and reliability of WBAN while transmitting data. One is to compress data by using the proposed STPCSE model, which can effectively reduce the amount of data to be transmitted and reconstruct the original signal with high accuracy. The other is to develop a new forwarding strategy that comprehensively considers multiple attribute factors of nodes, and it can measure the forwarding capacity of relay nodes and system energy consumption and raise the performance of forwarding algorithm. A large of experimental results prove that the proposed TTCF strategy is superior to the comparison scheme in many aspects.  
Although the TTCF strategy improves the reliability of WBAN data forwarding to some extent, there are still some deficiencies. Firstly, WBAN relies on mixed wireless channels that mainly include a free space channel, body surface channel and body internal channel. This paper does not reflect on the differences of communication models under these three paths for the convenience of calculation, which will affect the accurate calculation of energy consumption. Secondly, this paper believes that node energy consumption and forwarding capacity are equally important, which may lead to the loss of objectivity in selecting relay nodes in forwarding path calculation. Therefore, we plan to solve the above two defects in the next step. First, to give the transmission power consumption closer to the real environment, we will replace the calculation method of power consumption based on the Friis model [57] with various losses \(L\) greater than 1, which can measure the actual power consumption in a non-ideal communication state more accurately, and can also well reflect the WBAN data transmission environment with mixed communication channels. Then, we consider using the adaptive weight approach [58] to fuse the related items in the Equation (15). The weight given by this adaptive weight method is not fixed and inconvenient. It is adjusted according to the useful information in the current solution set, thus forcing the algorithm to search in the direction of the Pareto frontier. Compared with the fixed weight assigned by the human, it has greater advantages and adaptability. These improved plans will be reflected in the contents of our further research.
## 5 Simulation and Experiments
### DRL Training Results
Figure 10 illustrates the test scenario, with the initial point and goal represented by a circle and a star, shown on the left side. Four people walked randomly during the experiment. The right side of the figure depicts the robot's trajectory in purple during the test. Based on the algorithm proposed in this work, the radar operates at 30 Hz and the MOT processes radar data at 15 Hz to determine the humans' observable states, including their positions and velocities. The robot's position is updated at 8 Hz using the AMCL package, while its velocity is calculated from odometry data at 25 Hz. The neural network computes the value function and determines the optimal action at 8 Hz, and the TurtleBot 3 motors operate at 10 Hz. The synchronization of these frequencies is crucial for mitigating time delays in system perception, decision-making, and execution, important considerations for real-time navigation, particularly in dynamic situations. The results of the autonomous navigation tests demonstrated impressive performance. The robot successfully reached the goal while avoiding obstacles in approximately 36 s, following a path length of around 8 m. The robot's average velocity was 0.22 m/s, in alignment with the maximum velocity of the TurtleBot 3. During the experiment, a limitation in the radar's field of view was identified. The radar covers only 120 degrees, making it challenging for the robot to detect objects behind it. To address this limitation, deploying at least two radars would be necessary to achieve broader coverage and improve movement capabilities.
# Tamed Warping Network for High-Resolution Semantic Video Segmentation
## 4 Tamed Warping Network
### Overview  
Tamed warping network (TWNet) is a warping-based semantic video segmentation method. Warping-based methods sequentially divide a video clip into many groups of frames. Each group consists of one key frame followed by multiple non-key frames. In this way, the key frame is segmented in the same way as semantic image segmentation, and each non-key frame is segmented with the help of the results from the preceding frame.  
Accordingly, TWNet consists of a key-frame CNN (KFC), a non-key-frame CNN (NKFC), and two modules for correction, as illustrated in Figure 3. In TWNet, each key frame is performed by KFC, which is the same as the model used by per-frame methods discussed in Section 1 except that the context features of a selected interior layer are sent to the next frame. Each non-key frame is performed by NKFC, where the features from the preceding frame are warped and corrected by the context feature rectification (CFR) and residual-guided attention (RGA) modules. The corrected features are sent to the next CNN layer and the succeeding frame. The components of TWNet are detailed below.
## 5 Discussion
### Limitations  
According to Directive 2002/49/EC, the day-evening-night level (\(L_{den}\)) is defined over three time intervals--day (12 h), evening (4 h), and night (8 h)--with penalties of +5 dB in the evening and +10 dB at night. **Data limitation:** our records include only **daytime** (12 h) and **nighttime** (12 h) noise levels, without an independent measurement for the evening period. **Consequence:** it is not possible to compute the standard \(L_{den}\), which prevents direct comparison with studies using this full metric. **Future work:** it is recommended to include a third measurement for the evening period (4 h) to implement the official formula:  
\[L_{den}=10\log_{10}\Bigl{(}\frac{12\cdot 10^{L_{day}/10}+4\cdot 10^{(L_{evening +5})/10}+8\cdot 10^{(L_{night}+10)/10}}{24}\Bigr{)}\]  
and enhance the analysis of acoustic exposure.
## 5 Results
### Root Mean Square Error (RMSE) Performance Validation  
In this research, the performance evaluation of the proposed control system was determined by finding the root mean square error (RMSE) of the conventional PI controller, the variable PI controller, and the proposed DDPG controller. The RMSE is a standard metric used in model evaluation, as it is the optimal metric for \"normal\" errors to determine how the model \"fits\" the data [21]. In the power system and with respect to wind energy generation, there is an acceptable amount of deviation from the optimal set points that will still allow the system to remain in a steady-state condition. Due to this feature, RMSE is a better option than mean absolute error. To determine the RMSE, the formula depicted in the following Equation (28) was utilized:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(\;\hat{y}_{i}-y_{i})^{2}} \tag{28}\]  
where \(n\) is the number of observations, \(y_{i}\) is the theoretical value, and \(\hat{y}_{i}\) is the experimental value. In this context, the \(y_{i}\) values are the set points for the specific measured value, and \(\hat{y}_{i}\) is the simulated value obtained by the AI controller. The following Table 2 depicts the RMSE evaluation of the conventional controller, the VPI controller and the DDPG agents.  
Figure 16: PI, VPI, and DDPG control comparison during FDIA on rotor voltage reference points.  
From the case studies presented in the table as well as the plots presented in the previous sub-section, the performance of the DDPG agents during a cyber attack is clearly better than its conventional counterpart and the VPI implemented in this study. With more training and fine tuning of the inputs, the DDPG agent could perform even better. Implementing a DDPG agent to predict the stator rotor voltage set points could also help alleviate cyber attacks on the grid-side converter, especially in the presence of a DoS attack.
## 2 Materials and Methods
### Evaluation of the Effectiveness of the Clustering Results  
In this paper, the effectiveness of recognition results is evaluated by using the following five metrics:  
1. Purity [23]: \[PUR=\frac{1}{n}\sum_{k^{A}}n_{k^{C},k^{A}}\,\ k^{C}=k^{A}\] (17)
2. Cluster similarity [24]: \[CSM=\frac{1}{k^{A}}\sum_{k^{A}}\max_{1\leq k^{C}\leq K^{C}}\frac{2n_{k^{C},k^{ A}}}{n_{k^{C}}+n_{k^{A}}}\] (18)
3. Normalized mutual information [25]: \[NMI=\frac{\sum_{k^{A}}\sum_{k^{C}}n_{k^{C},k^{A}}\log\frac{n^{-n_{C},k^{A}}}{n _{k^{C}}-n_{k^{A}}}}{\sqrt{\left(\sum_{k^{A}}n_{k^{A}}\log\frac{n_{k^{A}}}{n} \right)\left(\sum_{k^{C}}n_{k^{C}}\log\frac{n_{k^{C}}}{n}\right)}}\] (19)
4. Cluster-based cross entropy [23]: \[ClucE=\frac{-\sum_{k^{C}}\frac{n_{k^{C}}}{n}\sum_{k^{A}}\frac{n_{k^{C},k^{A}}} {n_{k^{C}}}\log\frac{n_{k^{C},k^{A}}}{n_{k^{C}}}}{\log K^{A}}\] (20)
5. Class-based cross entropy [23]: \[ClaCE=\frac{-\sum_{k^{A}}\frac{n_{k^{A}}}{n}\sum_{k^{C}}\frac{n_{k^{C},k^{A}}} {n_{k^{A}}}\log\frac{n_{k^{C},k^{A}}}{n_{k^{A}}}}{\log K^{C}}\] (21)  
In the above five formulas, \(k^{C}\in\{1,2,\cdots,k^{C}\}\) represents the category obtained after clustering, \(k^{A}\in\{1,2,\cdots,k^{A}\}\) represents the real category of the sample set,\(n\) represents the total number of samples in the sample set, \(n_{k^{C}}\) represents the total number of samples of class \(k^{C}\) in the clustering results, \(n_{k^{A}}\) represents the total number of samples of class \(k^{A}\) in the clustering results, and \(n_{k^{C},k^{A}}\) represents the number of intersections between class \(k^{C}\) samples in the clustering results and class \(k^{A}\) samples in the sample set.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 2 Related Work  
The identification and management of SAR operations' patterns is a well-known research topic that has attracted a lot of interest [13; 14; 15]. The task of automating the surveillance of vessel trajectories in a region to detect when SAR patterns occur can be valuable for identifying when vessels perform a SAR mission or they may behave suspiciously.  
The general patterns followed by vessels involved in a search-and-rescue mission are generally pre-defined as \"Norms\", in terms of Maritime Safety/Safety Regulations, although in many cases these patterns need to be modified based on the needs and the area of each SAR mission. Based on the idea of adjusting the SAR pattern to fit the area to be searched, [PERSON] [16] examined a heuristic method to implement a tool for automatic search pattern generation.  
As part of this work, the problem of vessel pattern identification can be treated as a task of classifying time-series shapelets. The main drawbacks of this task is the computational cost for measuring the accuracy of all possible shapelet candidates as well as the fact that this is a time consuming process. In this context, the authors of Reference [17] proposed an algorithm for shapelet discovery by transforming the raw real-valued and high-dimensional data into a discrete and low-dimensional representation which allows hashing of the data and the use of collision history for searching.  
In a similar context, the authors of Reference [18] introduced the idea of a shapelet transformation in order to disconnect the task of identifying shapelets from the classification algorithm. To do so, they propose the extraction of the k best shapelets from a dataset in one pass and use the identified shapelets to transform the data using the distance calculation from a series to each shapelet. Another approach to overcome the time complexity of shapelets classification, as described in Reference [19], which introduces FSS algorithm (Fast Shapelet Selection), is to start by sampling the time series of a training dataset, identify the Local Farthest Deviation Points (LFDPs) and select the subsequences between two non-adjacent points. In Sections 2.1-2.3, which follow, we elaborate more on the details of SAR maneuvers and shapelets used in our experiments and other works that are related to these methods.