esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.3.2 Preprocessing for SlideNN  
In order to construct fixed-size input vectors, consecutive groups of rows were accumulated and flattened into one-dimensional (1D) arrays. Each model configuration defined a specific number of rows to be grouped based on the corresponding input size:  
* The first model combined 8 consecutive rows (\(8\times 8\)) into a 64-element vector.
* The second model used 16 consecutive rows (\(16\times 16\)), resulting in 128-element vectors.
* The third model added 32 consecutive rows (\(32\times 32\)), turning them into 251-element vectors.
* The fourth model used 64 consecutive rows (\(64\times 64\)), making 512-element vectors.  
Mathematically, let \(X\in\mathbb{R}^{n\times 8}\) be a matrix representing \(n\) consecutive hourly observations, each with 8 features. The reshaping process transforms this into a one-dimensional input vector \(x\in\mathbb{R}^{8n}\), as shown by Equation (7):  
\[x=\text{reshape}(X)=[x_{1,1},x_{1,2},\dots,x_{1,8},x_{2,1},\dots,x_{n,8}], \tag{7}\]  
where \(x_{i,j}\) is the \(j^{\text{th}}\) feature of the \(i^{\text{th}}\) hourly record. This operation preserves the temporal order of observations while converting them into a flat format suitable for fully connected feedforward neural networks. In Figure 5, each model's reshaping process is depicted for a better understanding.
## 3 Methodology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Source URL** & **Resolution/Scale/Number of Sites** \\ \hline DEM & [[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)) & 30 m \\  & (19 July 2019) & \\ Soil & [[https://nlftp.mlit.go.jp](https://nlftp.mlit.go.jp)]([https://nlftp.mlit.go.jp](https://nlftp.mlit.go.jp)) & 1:1,000,000 \\  & (29 July 2019) & \\ Land use & [[https://nlftp.mlit.go.jp](https://nlftp.mlit.go.jp)]([https://nlftp.mlit.go.jp](https://nlftp.mlit.go.jp)) & 1:25,000 \\  & (29 July 2019) & \\ Weather & [[http://www.data.jma.go.jp](http://www.data.jma.go.jp)]([http://www.data.jma.go.jp](http://www.data.jma.go.jp)) & 2 \\  & (30 July 2019) & \\ Hourly river flow & [[http://www.river.go.jp](http://www.river.go.jp)]([http://www.river.go.jp](http://www.river.go.jp)) & \\  & (5 August 2019) & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The description of the data sources for SWAT hourly simulation. extraction in SWAT-CUP. To use the hourly plug-in for this study basin, it was necessary to adjust the river outlet name in the \"fig.fig\" file and the number of simulated stations, sub-basin numbers, and simulation year in the \"SUFI2_extract_rch_hrly.def\" file. The SWAT-CUP uses a multiple regression approach to quantify the sensitivity of each parameter:  
\[\text{g}=\propto+\sum_{\text{i=1}}^{\text{n}}\beta_{\text{i}}\text{b}_{\text{i}}\]  
where g is the objective function value, \(\alpha\) is the regression constant, and \(\beta\) is the coefficient of parameters. The T-stat was then used as a way to identify the relative significance of each parameter b [28]. The sensitivities provided above are estimations of how the objective function changes on average when each parameter is altered, while all other parameters are in a state of flux. These estimations yield relative sensitivities based on linear approximations. The \(p\)-value determines the significance of the sensitivity. The SUFI-2 method employs Latin Hypercube sampling to manage parameters, and the calculation steps can be outlined as follows: first, define an objective function. Then, in the initial round of Latin Hypercube sampling, allocate sampling points within the initial parameter uncertainty range. Next, move on to subsequent rounds of Latin Hypercube sampling, calculating a series of measurement matrices to evaluate each sampling point. Finally, based on the evaluation results, proceed to update the parameter ranges further.
# Study on Magnetic Resonance Characteristics of Coal Sample under Progressive Loads
## 4 Testing Results and Analysis  
### Porosity  
Table 1 and Figure 8 show the porosity and its evolution under progressive loads. As can be seen, before loading, the porosity of water-saturated coal samples C-1, C-2, and C-3 is 16.8470%, 16.8593%, and 17.5788%, respectively. When the load increases to 3 MPa and is held for 150 min, the porosity of C-1, C-2, and C-3 all decrease, though differing in their falling ranges. To be specific, the porosity of the three samples falls to 16.5783%, 16.5165%, and 17.2258%, respectively; and the decrease rate is 1.59%, 2.03%, and 2.01%, respectively. When the load is 3 MPa, the coal samples are in the compaction stage; the primary pores and fissures would close up after loading so that partial moisture content would  
Figure 6: Stress–strain curve of the Group S coal sample.  
Figure 7: Test plan design of the coal sample C-1.  
be squeezed out and the NMR signals of 1H would be attenuated, thus causing the slight decrease in porosity. As for coal sample C-1, when the load increases to 5 MPa, it evolves from compaction stage to elastic stage; the primary pores and fissures expand and secondary fissures begin to develop. Consequently, the porosity increases slightly to 16.6502%, with a growth rate of 0.43%. When the load increases to 7 MPa and 9 MPa, the porosity is 16.7930% and 17.0519%, respectively, and the growth rate is 0.86% and 1.54%, respectively. As the load increases gradually, the porosity growth rate of the coal sample rises correspondingly, indicating that the pores and fissures develop more and more drastically. When the load reaches to 11 MPa, macro-cracks appear in C-1; splitting occurs when the sample is water-saturated and its porosity is only 14.1662%. Under progressive loads, the bearing capacity of water-saturated coal samples reduces substantially, which further demonstrates that the strength of coal wall in the supporting pressure area would decline remarkably under the effect of long-term advance stress.  
As for coal sample C-2, when the load increases from 3 MPa to 7 MPa, the porosity grows from 16.5165% to 16.6819%, the growth rate being 1.00%. As for coal sample C-3, when the load increases from 3 MPa to 11 MPa, the porosity grows from 17.5788% to 17.8763%, with a growth rate of 1.69%. When the coal sample is in the elastic stage, the pores and fissures expand under the effect of stress and the porosity increases accordingly. Meanwhile, it is discovered that when the load upon C-1 increases from 3 MPa to 7 MPa, the growth rate of its porosity is 1.30%, which is 0.30% larger than that of C-2 under the same load. The test results show that progressive loading exerts greater impact on coal samples than one-time loading does and will cause more drastic development of pores and fissures.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## Appendix A
### Experimental Results After Applying Random Shock Noise  
Random shock noise experiments were conducted with a fixed shock occurrence probability of 0.1 and shock amplitudes set to 1.0, 1.5, 2.0, and 3.0 for Levels 1, 2, 3, and 4, respectively. The results indicate that, unlike periodic noise methods, random shock noise, as a non-periodic noise type, has a significantly greater impact on model performance. Performance decreases as noise intensity increases, with the degradation being more pronounced than that observed with the previous two noise methods.  
For the MFPT and PU datasets, the performance differences between the models are minimal. However, for the CWRU dataset, the MultipatchTST model shows superior performance as the noise intensity increases. The PU dataset shows a significant performance degradation for all models in the Level 4 condition, indicating increased sensitivity to random impulse noise. Nevertheless, the patch-based learning models, PatchTST and MultipatchTST, show greater resilience to random impulse noise compared to the other models, highlighting the effectiveness of patch-based learning in mitigating the adverse effects of aperiodic noise.  
Figures A3 and A4 show graphs of the experimental results for the CWRU and PU datasets, and Table A5 shows a table of the experimental results performance metrics for the mfpt dataset.  
Figure A3: Experimental results by random shock noise level for the CWRU dataset.  
Figure A4: Experimental results by random shock noise level for the PU dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **SNR** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{LSTM} & 27 dB & 0.9579 & 0.9609 & 0.9638 & 0.9622 \\ \cline{2-6}  & 17 dB & 0.9239 & 0.9278 & 0.9298 & 0.9287 \\ \cline{2-6}  & 11 dB & 0.7586 & 0.8731 & 0.7594 & 0.6995 \\ \cline{2-6}  & 3 dB & 0.65 & 0.825 & 0.6506 & 0.5347 \\ \hline \multirow{4}{*}{GRU} & 27 dB & 0.95 & 0.956 & 0.9534 & 0.9546 \\ \cline{2-6}  & 17 dB & 0.9173 & 0.9204 & 0.9222 & 0.9212 \\ \cline{2-6}  & 11 dB & 0.7692 & 0.8796 & 0.7695 & 0.721 \\ \cline{2-6}  & 3 dB & 0.6506 & 0.8251 & 0.6506 & 0.5348 \\ \hline \multirow{4}{*}{FCN} & 27 dB & 0.9552 & 0.9597 & 0.9579 & 0.9586 \\ \cline{2-6}  & 17 dB & 0.9173 & 0.9199 & 0.9222 & 0.9207 \\ \cline{2-6}  & 11 dB & 0.8164 & 0.8905 & 0.8169 & 0.8073 \\ \cline{2-6}  & 3 dB & 0.6506 & 0.8252 & 0.6506 & 0.5349 \\ \cline{2-6}  & 3 dB & 0.6506 & 0.8251 & 0.6513 & 0.5364 \\ \hline \multirow{4}{*}{InceptionTime} & 27 dB & 0.9531 & 0.9607 & 0.9559 & 0.958 \\ \cline{2-6}  & 17 dB & 0.9315 & 0.9354 & 0.9385 & 0.9367 \\ \cline{2-6}  & 11 dB & 0.7644 & 0.8746 & 0.7674 & 0.7135 \\ \cline{2-6}  & 3 dB & 0.6513 & 0.8253 & 0.6513 & 0.5364 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results on MFPT dataset with random shock noise.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Hyper-Parameters Analysis
#### 4.2.3 Similarity Calculation  
Two measurement strategies are introduced in Section 3.4. By adopting these two strategies, the similarity of the query and reference feature vectors is calculated to represent the similarity of the cross-view image pairs. In Table 1, the results demonstrate that employing Euclidean distance can better present the vector similarity, leading to a 5% improvement in accuracy compared to cosine similarity. The reason is that the range of Euclidean distance is larger than the range of cosine similarity, leaving a larger scope for optimization of the distance between positive and negative samples to avoid the model parameters being trapped in suboptimal sets. To ensure the accuracy of the model, Euclidean distance is used to complete the ablation experiments. However, cosine similarity has a faster calculation speed due to its lower computational overhead. The time consumption of inference is shown in Table 2. Although using Euclidean distance can achieve higher accuracy, using cosine similarity for image similarity calculation can reduce inference time, which can apply to UAVs with higher geo-localization inference speed requirement.  
Figure 4: The influence of the output feature dimension for the model.
# Q-Learning Empowered Highly Dynamic and Latency-Aware Routing Algorithm for Flying Ad-Hoc Networks
## 1 Introduction
### Organization of This Article  
The structure of this paper is as follows: Section 2 shows related works of the FANETs routing protocol. Section 3 introduces the modeling methodology for the communication structure of FANETs and reinforcement learning. Section 4 establishes the proposed multi-hop routing protocol for FANETs. Section 5 parametrically analyzes the protocol's performance in the NS-3 simulation environment. The last section concludes this paper.
## 2 Result and Discussions
### Contrasting _trans_ P-lethality / inhibition, and _cis_ Replicative Killing
Several examples reported suggest that P-lethality is a separate mechanism from P-inhibition of ColE1 replication: i) _grpD55_ cells with pcIpR-_P_*-timm were killed at 37-39 'C, yet 100% of the surviving cells retained the plasmid; ii) most of the cells with pcIpR-_oop-O-P_-timm plasmids retained the plasmids at 39 'C while suffering high P-lethality. Additionally (see Figure 4), cells defective in host protease genes _clpB_ or _hflA_ were sensitive to P lethality at 37-39 'C, yet the majority of the survivors retained the pcIpR-_P_-timm plasmid (data not shown; available from authors). Is P-inhibition due only to a decrease in available DnaB, or does it influence / perturb ongoing replication, or replication restart? Our results do not indicate if P fully sequesters DnaB or if a single P monomer bound to DnaB is sufficient. Moreover, the genetic evidence does not show whether this interaction is persistent or transient. There seems little doubt that competing DnaB away from DnaC is an important function of P; but the longer term consequence is that the interaction of P and DnaB yields a P-DnaB-ATP dead-end ternary complex [10], and the only known role for P is in early _orin_-dependent initiation events, of which only a few are required. We propose that the full sequestration of DnaB by P is not necessary in order for P to interfere with ongoing plasmid replication, because of the sensitivity of ColE1 replication / copy maintenance to leaky (sub-induction) levels of P (arising from a plasmid encoding _P_). The simple explanation for plasmid loss is that while the cells continue to replicate and divide, plasmid replication is differentially inhibited. For example, _grpD55_ cells very poorly support vegetative growth of \(\lambda\) at 30 'C, and not at all between 37-42 'C, whereas cellular growth is not noticeably perturbed. A low level of P, tolerated by cells, could fully inhibit ColE1 replication, in turn reducing plasmid copy number with each cell division, which could explain plasmid curing for cells grown between 34-36 'C. Less clear is how cells with pcIpR-_P_-timm grown at 30 'C are cured when shifted to 37-42 'C, where division is inhibited and cells elongate forming filaments (Sect 2.8. ).
## 3 System Architecture
### Decoding Based on Deep Learning  
The relationship between signal-to-noise (SNR) and communication distance is described in Section 4. The SNR values are smaller if the communication distance is long. Therefore, the receiver side experiences challenges in terms of defining the threshold between ON and OFF values. In [21], we proposed the matched filter method to optimize the SNR values and increase the communication distance. However, the matched filter did not perform well in a mobile environment, which is discussed in Section 4.  
The zero-crossing filter, developed in [21], separates ON/OFF levels at the receiver side using zero as the threshold level. When the system's SNR is high, this approach performs well, as shown in Figure 2a; however, when this value is low, distinguishing between ON/OFF statuses is difficult as illustrated in Figure 2b. The matched filter is a filter technique that determines the template signal in the real signal by comparing it to a template signal [21]. With additional random noise, the matched filter (a linear filter technology) maximizes the SNR. The matched filter has advantages when the SNR is small but does not work well for mobility channels as shown in Section 4. The blur effect occurs when the transmitter and receiver are move relative to one another. This creates inter-symbol interference for the OCC system, causing a reduction in the system's performance. A deep learning neural network was applied to detect a preamble and decode data by considering the mobility effect. In this paper, Root mean square error (RMSE) was applied to evaluate the error evaluation metrics for the proposed scheme. With 200 epochs, it can achieve high accuracy for the forecasting value error with low values (<0.1).  
After detecting the LEDs, the OCC signal was selected from multiple LEDs in the LED region using the down-sampling algorithm. The C-OOK signal was detected by extracting the central intensity point of the LEDs. The preamble was detected by the neural network as show in Figure 3.  
The raw data (10,000 samples) were collected by the rolling shutter camera at different distances (2 m, 6 m, 10 m, 16 m, and 22 m) at different velocity speeds, including the preamble and the payload parts. To avoid overfitting the model, we used a basic deep learning neural network model with two hidden layers. Following preamble detection, we were able to reliably detect the start of frame of C-OOK signals, which improved the OCC system performance compared with the conventional technology in mobility environment. In the case of overfitting, the accuracy of the test dataset is undermined when there are six or more hidden layers.
## 2 Proposed Method  
Figure 1 illustrates the overall progress of the proposed MOTL method. First, signal processing is performed to convert the collected vibration signals into an image representing a time-frequency distribution. An initial pre-trained model is obtained using the source domain data and fine-tuned once with the target domain data. ResNet50 [33] is used as the model structure for all model training. Then, the KLD and MMD are calculated from the source domain data and the initial deep transfer model. Multi-objective instance weights are computed from these two indicators. The instance-weight vector is used to train the optimized pre-trained model as a training weight. The final target fault diagnosis model is obtained by transferring the optimized pre-trained model and fine-tuning it. The two-stage transfer process is performed to derive the optimal multi-objective instance weights and effectively minimize the influence of domain discrepancy. A detailed flowchart of the proposed method is presented in Figure 2.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
###### Abstract  
Numerous investigations of urbanization effects on vegetation spring phenology using satellite images have reached a consensus that vegetation spring phenology in urban areas occurs earlier than in surrounding rural areas. Nevertheless, the magnitude of this rural-urban difference is quite different among these studies, especially for studies over the same areas, which implies large uncertainties. One possible reason is that the satellite images used in these studies have different spatial resolutions from 30 m to 1 km. In this study, we investigated the impact of spatial resolution on the rural-urban difference of vegetation spring phenology using satellite images at different spatial resolutions. To be exact, we first generated a dense 10 m NDVI time series through harmonizing Sentinel-2 and Landsat-8 images by data fusion method, and then resampled the 10 m time series to coarser resolutions from 30 m to 8 km to simulate images at different resolutions. Afterwards, to quantify urbanization effects, vegetation spring phenology at each resolution was extracted by a widely used tool, TIMESAT. Last, we calculated the difference between rural and urban areas using an urban extent map derived from NPP VIIRS nighttime light data. Our results reveal: (1) vegetation spring phenology in urban areas happen earlier than rural areas no matter which spatial resolution from 10 m to 8 km is used, (2) the rural-urban difference in vegetation spring phenology is amplified with spatial resolution, i.e., coarse satellite images overestimate the urbanization effects on vegetation spring phenology, and (3) the underlying reason of this overestimation is that the majority of urban pixels in coarser images have higher diversity in terms of spring phenology dates, which leads to spring phenology detected from coarser NDVI time series earlier than the actual dates. This study indicates that spatial resolution is an important factor that affects the accuracy of the assessment of urbanization effects on vegetation spring phenology. For future studies, we suggest that satellite images with a fine spatial resolution are more appropriate to explore urbanization effects on vegetation spring phenology if vegetation species in urban areas is very diverse.  
urbanization effects; vegetation spring phenology; spatial resolution; satellite image; time series 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020-2030, 2020,
# HI-Sky: Hash Index-Based Skyline Query Processing
## 2 Related Study
### Traditional Skyline Computation  
The traditional skyline query methods compare all data to compute the skyline. One such technique is the block nested loop (BNL), a basic skyline query method proposed by [PERSON] et al. [15]. The BNL conducts the dominance test according to the order in which data are stored on a disk. However, the BNL is inefficient due to frequent changes in skyline candidate data point selection. [PERSON] et al. [16; 17] proposed the sort filter skyline (SFS), which computes the skyline by relying on the fact that data with better entropy scores are more likely to become skylines. To utilize this feature, the SFS calculates the entropy score while sorting the data and then performs the dominance test similarly to the BNL. The entropy score provides a monotonic ordering characteristic to the skyline candidate, which enables the SFS to compute the skyline more effectively than the BNL. However, the requirement of additional computation for entropy score calculation and sorting for all data makes this method unsuitable for the fast computation of skylines from large amounts of data.