esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Agent Training by Reinforcement Learning
#### 3.3.1 Action Space  
We used a deep neural network comprising CNN and LSTM to forecast the animal's displacement in the x- and y-directions in the subsequent time step. This was achieved by using remote sensing images and the animal's present position as input data. In the context of supervised learning, this objective may be achieved by undergoing training over numerous batches. However, in the field of reinforcement learning, the policy network may encounter difficulties, such as non-convergence or very slow convergence owing to the large environmental space and continuous action space. In order to tackle the difficulties presented by the ongoing range of actions in reinforcement learning training, we redefine the animal's action space by dividing it into two components: the direction of movement and the distance of movement. The purpose of this formulation is to simplify the training of the animal agent in reinforcement learning by using a discrete action space.  
\[Action=\{angle,distance\} \tag{10}\]  
The direction of movement is quantified in degrees and approximated to the closest whole number, spanning from 0 to 360. The displacement is quantified in metres and rounded to the closest whole number, ranging from 0 to 6600. In addition, we decrease the size of the action area even further by reducing the action resolution. We used a 3 m action resolution to strike a compromise between computing efficiency and the level of detail in the action space. By converting the continuous action space into a discrete one and decreasing the granularity of the action space, we greatly minimise the computational complexity and shorten the training time of the animal agent, all while meeting the necessary conditions for modelling animal behaviour.
## 3 Results and Discussion
### Argon Plasma Irradiation Induces the Production of Nitric Oxide in Keratinocytes  
Generally, nitric oxide is a free radical with an unpaired electron, and it is well known to regulate various epidermal functions, including epidermal proliferation, differentiation, wound healing, and barrier permeability in the skin [31; 32; 33]. In fact, nitric oxide regulates junctional, cytoskeletal, and extracellular matrix proteins. Hence, we believed that nitric oxide was involved in plasma-mediated permeability as a transdermal absorption donor. Therefore, we further investigated whether argon plasma irradiation induces the production of nitric oxide in keratinocytes. Argon plasma was applied to HaCaT cells for 10, 30, and 60 s, followed by incubation for 10, 30, 60, and 120 min. As shown in Figure 8, the exposure of plasma on the cells for 10, 30, and 60 s similarly caused the production of nitric oxide up to approximately 80 uM. Nitric oxide was significantly induced from 60 min after irradiation. These findings suggest that plasma-induced nitric oxides from keratinocytes are able to regulate the junctions between cells and the extracellular matrix in the epidermal layer, which would make the epidermal barrier permeable.  
Figure 7: Cellular permeability of 70 kDa and 150 kDa dextran by argon plasma irradiation in keratinocytes. (**A**) Fluorescence images, HaCaT cells were treated with argon plasma for 30 s and then subjected to fluorescein-dextran 70 or 150 kDa. (**B**,**C**) Statistical results of relative fluorescence in the cells treated with 70 kDa (**B**) and 150 kDa (**C**) dextran in the presence and absence of argon plasma irradiation. Scale bar indicates 100 uM. The fluorescent intensity was averaged from 40 cells. Data are presented as means \(\pm\) S.D. *** \(p\) < 0.001 compared to the nontreated control (unpaired two-tailed \(t\)-test).
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 4 Discussion  
In the previous text, the article analyzed the main methods in the current field of UAV sound recognition. Through research, the frequency band feature extraction method was proposed, and the improvement of the recognition rate of the proposed method was verified through comparative experiments with the classical MFCC methods. However, this study still has certain limitations.  
(1) Due to the limitations of the experimental conditions, only three types of UAV were used for the experiments in this article. This limits the universal verification of the proposed method on other types of UAV, and restricts the optimization and parameter iteration of the proposed method. The limitation of training data also leads to an imbalance of training samples.  
(2) The weight of the function \(f_{ufb}\) in the method is based on experience, which also brings about some limitations.  
(3) The research does not take into account the situation of strong noise interference. Strong noise will cover most of the frequency bands where the UAV sound is located, thereby causing the audio to lose the feature of the UAV. As a result, the sound features of the UAV cannot be correctly extracted, and recognition cannot be carried out. This is the limitation of the feature extraction method based on the frequency distribution of UAV sounds.
# SNR Analysis for Quantitative Comparison of Line Detection Methods
## 6 Conclusions  
In this study, the performances of line detectors were evaluated by the analytical quantifications of their SNRs and completeness and correctness. The correlations arising among pixels when a Gaussian smoothing filter was applied were first identified. Then, the amount of noise remaining in the derived values, such as gradients, SD, and SGAD, was derived based on the error propagation. Furthermore, the SNRs of line detectors were analytically derived based on the derived signal and noise strengths. In addition, the penalty function was proposed to consider the influence of the blur, smoothing, and the line-width on line detectors. Verification of the validity of the derived SNRs based on investigation of the relationships of the SNRs with completeness and correctness indicates that the derivations of the SNRs of line detectors proposed in this study were effective in quantifying their performance. The validation test of SNRs was performed using nine color images and will be extended to more image sets for further test completion in the future.  
Regarding feature extraction, it was observed that the edge features could be accurately extracted in the vector form using the methods proposed in [18; 41]. In contrast, the line features were considered to be accurately extracted in the vector form using the SGAD method followed by the subpixel line localization and location-linking methods as described in [5].  
Therefore, a set of methods for line detection, non-maxima suppression, subpixel line localization, and linking can produce high-quality ridge and valley features for various applications. Moreover, the error propagation scheme used in this study to derive the relevant theoretical SNRs can be used to develop high-performance operators for extracting features.  
Figure 15: Completeness and correctness against SNR for the natural images. The second and fourth columns show the zoom-in versions of the plots in the first and third columns with SNR range [0; 10], respectively.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2016R1D1A1B02011625).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 5 Performance Analysis and Discussion
### Deflection Coefficient Performance Analysis  
In addition to the CLT approximation, an alternative performance analysis can be made through the deflection coefficient [34,35]. The optimal detection performance of the LRT-based fusion rule can be obtained by maximizing the deflection coefficient when a Gaussian signal in Gaussian noise is detected [20]. The deflection coefficient is defined as:  
\[D(\Lambda)=\frac{[E(\Lambda\mid H_{1})-E(\Lambda\mid H_{0})]^{2}}{V_{\text{ ar}}(\Lambda\mid H_{0})} \tag{43}\]  
where \(E(\cdot\mid H_{j}),j\in\{0,1\}\) is the mean under \(H_{j}\) hypothesis and \(V_{\text{ar}}(\cdot\mid H_{j})\) is the variance under \(H_{j}\) hypothesis.  
From Equation (43), when the local detection performance indices are equal to each other, the deflection coefficient is given as:  
\[d_{1}^{2}= \frac{[E(\Lambda_{\text{BER}}^{\prime}\mid H_{1})-E(\Lambda_{ \text{BER}}^{\prime}\mid H_{0})]^{2}}{V_{\text{ar}}(\Lambda_{\text{BER}}^{ \prime}\mid H_{0})}\] \[= \frac{N(P_{1}-P_{0})^{2}}{P_{0}(1-P_{0})} \tag{44}\]  
Similarly, when the local detections of the deflection performance indices are not equal, the deflection coefficient is given as:  
\[d_{2}^{2}=\frac{[E(\Lambda_{\text{BER}}\mid H_{1})-E(\Lambda_{\text{BER}}\mid H _{0})]^{2}}{V_{\text{ar}}(\Lambda_{\text{BER}}\mid H_{0})} \tag{45}\]where \(E(\Lambda_{\text{BER}}\mid H_{1})\) and \(E(\Lambda_{\text{BER}}\mid H_{0})\) are the means given in Equations (33) and (38), respectively. \(V_{\text{ar}}(\Lambda_{\text{BER}}\mid H_{0})\) is the variance given in Equation (39). Unlike the closed-form performance analysis that needs the distribution of the fusion statistic, the deflection coefficient only requires the first and the second order moments under the \(H_{1}/H_{0}\) hypothesis.
# A Simulation-Based Optimization Method for Warehouse Worker Assignment
## 6 Simulation-Based Optimization
We propose a random neighborhood search algorithm to solve the problem, which is described in Algorithm 1. Accordingly, every time a 1-neighborhood \(X^{\prime}\) of the current solution is built randomly. If the new solution is feasible, the simulation will run with this assignment and generate the output statistics. The metric used for our objective is the service level. If the service level of the new solution is better than the incumbent solution, we will update the incumbent as this new solution. Usually, a random 1-neighborhood is not feasible, in the sense that the number of workers assigned to a task may be larger than \(M_{j}\). In this case, a 1-neighborhood of \(X^{\prime}\) will be built and checked again. This procedure will be repeated until a feasible solution is found, or the number of solutions we have generated is larger than a determined threshold. Note that our method is different from the 1-step neighborhood search. Before a new feasible solution is found, several workers may have been reassigned to other task(s). This can help to avoid the local optimum. As the random search is in a broader neighborhood, the probability of reaching a global optimum is higher. From the practical perspective, the number of new solutions and the number of simulation runs are both bounded, to make the total time reasonable for the industry application. ```
Data: the workload Result: an optimal assignment initialization: find a feasible assignment \(X_{0}\), set \(Y_{0}=X_{0}\), \(s_{opt}=s=0\), \(loop=iteration=0\) ; while loop<\(m_{1}\) and iteration<\(m_{2}\)do if\(s>s_{opt}\)then  set \(s_{opt}=s\);  set \(Y_{iteration}=X_{loop}\)  end if repeat  repeat  randomly select a worker \(i\) ;  randomly select a task \(j\) ;  modify \(X_{loop}\) by assigning worker \(i\) to task \(j\), resulting in \(X_{loop+1}\) ;  loop++; untilworker \(i\) can do task \(j\); until\(X_{loop}\) satisfy all the constraints;  run simulation and update \(s\) as the output value ;  iteration++;  end if
```  
**Algorithm 1**The random neighborhood search algorithm.
## 3 Datasets
### Gridded Precipitation Products (GPPs)
#### 3.1.6 Fifth Generation of ECMWF Reanalysis Precipitation Products (ERA5)  
ERA5 is the fifth generation of the European Center for Medium-Range Weather Forecasts (ECMWF) that provides a global reanalysis of climate variables [17]. ERA5 reanalysis data are based on the Integrated Forecasting System (IFS) and make use of the 4D-Var data assimilation scheme [80]. Multiple sources of data (e.g., microwave radiances, humidity sounder radiances, Infrared radiances, atmospheric motion vectors data, gauge-based data, etc.) and a large number of observations (i.e., nearly 24 million per day as of January 2019) were integrated to generate ERA5 datasets. Precipitation estimates are one of the climatological variables by ERA5, which are generated at a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution on an hourly time scale. Comprehensive information about data and technical configurations of the ERA5 dataset is described in [PERSON] et al. [17]. In this study, ERA5 reanalysis precipitation data from 2000 to the end of 2020 was utilized for performance evaluation.
## 5 Experimental Results
### Supfix Sorting Results  
In this section, we conduct experimental evaluations and comparisons of five suffix sorting methods: Loss-based membership inference attack, Zlib entropy-based membership inference attack (including \(\times\)Zlib and \(\div\)Zlib), neighborhood comparison-based membership inference attack, and the high-confidence token reward strategy. The chosen generation strategy is top-k = 2, with 20 experimental iterations. The primary evaluation metrics are precision \(E_{mia}\) and runtime \(t\).  
As shown in Table 9, our implemented membership inference attack method based on neighborhood comparison achieves high precision. However, the extensive time required to generate neighborhood samples results in a lower extraction speed. Additionally, the Zlib entropy-based membership inference attack (\(\times\)Zlib) demonstrates the highest extraction rate \(E_{mia}\) and shorter runtime, indicating superior performance. This result is somewhat unexpected. Upon examining the data to be extracted, we offer the following explanation: During the training data extraction process, the training data selected by [PERSON] et al. are used, and it appears that [PERSON] intentionally chose training data containing more information. Consequently, in this experiment, the Zlib entropy-based (\(\times\)Zlib) membership inference attack performs exceptionally well. Moreover, it can be concluded that the Zlib entropy-based (\(\times\)Zlib) membership inference attack is particularly suited for extracting training data that contain more valuable information. The basic loss-based membership inference attack also shows good performance. Conversely, the Zlib entropy-based membership inference attack (\(\div\)Zlib) and the high-confidence token reward strategy exhibit average performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & \(M_{1p}\) & \(M_{20p}\) & \(M_{p}\) & \(D_{edit}\) \\ \hline
0.5 & 0.417 & 0.601 & 0.509 & 14.648 \\
0.6 & 0.393 & 0.607 & 0.509 & 15.211 \\
0.7 & 0.356 & 0.611 & 0.505 & 16.231 \\
0.8 & 0.338 & 0.610 & 0.499 & 17.208 \\
0.9 & 0.309 & 0.603 & 0.495 & 18.126 \\
1.0 & 0.294 & 0.599 & 0.492 & 19.242 \\
1.1 & 0.260 & 0.585 & 0.481 & 20.138 \\
1.2 & 0.225 & 0.558 & 0.459 & 21.304 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The impact of temperature on suffix generation strategy (top-k).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Repetition\_Penalty** & \(M_{1p}\) & \(M_{20p}\) & \(M_{p}\) & \(D_{edit}\) \\ \hline
0.8 & 0.480 & 0.545 & 0.513 & 12.631 \\
0.9 & 0.476 & 0.541 & 0.512 & 12.666 \\
1.0 & 0.479 & 0.545 & 0.513 & 12.589 \\
1.1 & 0.479 & 0.547 & 0.512 & 12.788 \\
1.2 & 0.480 & 0.542 & 0.512 & 12.596 \\
1.3 & 0.482 & 0.540 & 0.511 & 12.613 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The impact of repetition_penalty on suffix generation strategy.
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 3 Method
### Model Selection  
The representative model for image restoration is the Auto-encoder, which is capable of handling noise in images [24]. However, this model tends to lose texture details when dealing with large-area image defects. Subsequently, Generative Adversarial Network (GAN) were proposed to address the issue of large-area image restoration [33]. Nevertheless, GANs perform poorly in repairing irregular image defects, often resulting in noticeable layering at the junctions of the restored regions. To address the limitations of existing models, this study applies the U-net network model to the restoration task of Dunhuang murals, with its basic net-work structure shown in Figure 2[34].  
The primary types of damage in Dunhuang murals are large-area defects and irregular defects. The U-net model consists of 23 convolutional layers, including a feature extraction process on the left and a feature expansion process on the right. Each layer of the encoder is connected to the corresponding features in the decoder to capture local image details. The introduction of shift connection in its structure enables the model to infer semantic and structural information of the image, achieving texture detail restor  
Figure 2: Structure of the U-Net model. (Each blue box in this figure corresponds to a multi-channel feature map; the number at the top of the box indicates the number of channels; the size and dimension of the feature map are labeled in the lower left corner of the rectangular box; the white box represents the replicated feature mapping).
## 5 Results
### Baselines  
To demonstrate the effectiveness of our model, we compared our DWSTTNs with seven baseline methods and tuned the parameters for all models. Our model was compared with the following state-of-the-art baselines.  
**ARIMA.** In the time series forecasting problem, the ARIMA model is a well-known model to predict the future values of time series.  
**Nearest Neighbors (NN).** In this model, the pick-up point of the taxi is given. This model processes and generates the output. The outputs of NN are the longitude and latitude of the closest cluster centroid.  
**Multi-Layer Perception (MLP).** This model is implemented with the winning challenge ECML/PKDD 2015. In this model, the input features are extracted. The extracted features receive the representation of the taxi trajectory. The trajectory is composed of the first five GPS points and the last five GPS points. This combination of the GPS points is associated with metadata. The MLP network contains standard hidden layers as well as 500 ReLu activation functions [41]. The MLP network follows the SoftMax layer with neurons m\(=|\)C\(|\). If we compare our approach with the MLP approach, we assign each longitude and latitude to the given set of the cluster centroid. Thus, the output through the predicted layer is \(\hat{y}\), which is the weighted average between the destination cluster centroid and the SoftMax layer. In the MLP, the problem is optimized with multi-class classification. The loss function is used for evaluation as cross-entropy. The Stochastic Gradient Descent trains the MLP model. The batch size is 200 and the learning rate is 0.001.  
**Multi-layer perception-SEQ (MMLP-SEQ).** The MMLP-SEQ is trained with the same MLP. The only difference is not using the first five GPS points and the last five GPS points as input.  
**Fully Connected (FC-LSTM).** The LSTM is the most straightforward traditional approach. LSTM uses only the input representation constituted by the driver and time  
Figure 7: Driver’s trajectory fed into our model (\(P_{t}\) are pick-up points and \(D_{t}\) are drop-off points).  
representation. The longitude and latitude coordinates of the zone are fully connected and fed as the location in the embedding layer. The weights are adjusted randomly. The training process is allowed to update the weights.  
**Long- Short-Term Memory (BOC+W2V).** Bag of Concept + W2V works with LSTM. The purpose of the W2V is to represent the recurrent neural network included with both BOC and W2V features. One more difference is that, instead of the zone embedding with BOC, the coordinates of the zone are embedded with W2V.  
**Spatio-Temporal Graph Convolutional Networks (ST-GCN).** ST-GCN purely depends on the spatial and temporal components. ST-GCN uses graph weights with spatial-temporal features to embed with transformer architecture. The traffic transformer captures the continuity and periodicity of time series and models the spatial dependency.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 3 Proposed PLSR-SVR Hybrid Predictor
### PLSR for the Prediction of the DVL Measurements
To improve the prediction accuracy and reliability, both the current and the past calculating velocities of the SINS are used as the inputs for PLSR. Assume the DVL works well during time \(T-s\) to time \(T\), the data matrix of independent variable \(X\) and the data matrix of dependent variable \(Y\) are represented respectively as  
\[X=\left[V^{n}_{eSINS(T-s)},V^{n}_{nSINS(T-s)},V^{n}_{uSINS(T-s)},\ldots,V^{n} _{eSINS(T)},V^{n}_{nSINS(T)},V^{n}_{uSINS(T)}\right]_{\mid\times(3s+3)} \tag{22}\]\[Y=\left[V^{n}_{eDVL(T)},V^{n}_{nDVL(T)},V^{n}_{uDVL(T)}\right]_{l\times 3} \tag{23}\]  
where \(l\) is the training sample size for PLSR. The PLSR predictor is trained with the above data matrices. As previously stated, the aim of training is to get the linear regression model of \(X\) and \(Y\) by extracting the latent vectors (\(t_{1}\),..., \(t_{h}\)). Steps 1-6 describes the training process in detail. Then the PLSR prediction model can be obtained according to Equation (21). When the DVL fails, the PLSR predictor is working to predict the DVL measurements. Assume the DVL malfunctions at time \(T_{0}\), the input for the PLSR predictor is correspondingly described as \(\left(V^{n}_{eSINS(T_{0}-s)},V^{n}_{nSINS(T_{0}-s)},V^{n}_{uSINS(T_{0}-s)}, \cdots,V^{n}_{eSINS(T_{0})},V^{n}_{uSINS(T_{0})},V^{n}_{uSINS(T_{0})}\right)\). Then the PLSR predictor outputs the prediction of the DVL measurements, symbolized by \(\hat{V}^{n}_{DVL,P(T_{0})}\). Figure 3 shows the flow chart of the above algorithm. The dashed box in Figure 3 indicates the training process of PLSR. Figure 3: The prediction based on PLSR.
## 5 Experimental Results
### Comparisons  
**Performance of Residual-Transformers:** We report the performance of various _Residual-Transformers_ against baseline in Table 1, where the results demonstrate the benefits of employing the transformers, consistently achieve the best performance on both Market-1501 and DukeMTMC datasets. Specifically, \(RT_{(L_{4}M_{1}h_{4})}\) achieves the best results around 3.41% and 5.77% for top-1 accuracy (R@1) and mean average precision (mAP), respectively on Market-1501. Similarly, the lowest-performing is \(RT_{(L_{4}M_{3}h_{4})}\) on Market-1501 which still gets a considerable boost of 1.75% and 3.41% for R@1 and mAP, respectively. Moreover, the accuracy is 5.07% and 3.01% for the highest and lowest for \(RT_{(L_{4}M_{1}h_{4})}\) and \(RT_{(L_{4}M_{1}h_{4})}\), respectively on DukeMTMC dataset. This further demonstrates the superior performance of the transformer frameworks integrated into the residual networks. **Performance of Dense-Transformers:** Table 2 shows the performance of _Dense-Transformer_. The best performance on Market-1501 is 1.19% (R@1) and 2.99% (mAP) more than baseline achieved by \(DT_{(L_{1}M_{3}h_{4})}\) while for DukeMTMC the increase is 1.34% (R@1) and 1.43% (mAP) by \(DT_{(L_{1}M_{3}h_{4})}\). The lowest increase is about 0.36% for Market-1501 while for DukeMTMC some of the _Dense-Transformers_ obtain less performance. **PCB-Transformers Performance:** As a last quantitative comparison, we provide the results of _PCB-Transformers_ in Table 3. The performance of the PCB-Transformers is very limited, although it uses the residual network as a backbone. The reason may be that the PCB [37] applies different strategies such as employing triplet loss and training the model for more epochs i.e., 120. The learning rate is dropped by half every 10 epochs between 60-90 epochs. Furthermore, the transformers may also have limited performance due to limited data available for more complex methods; hence, training the PCB-Transformers with ImageNet [41] and then fine-tuning with re-ID may lead to improved performance.