esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results
### Projected Rainfall Erosivity and Soil Erosion Rates in Kenya
Figure 12: Erosivity density for the periods of the 2030s and 2080s under RCP 2.6 and 8.5 scenarios, driven by CC-CMA_CANESM2, CESM1-CAM5, CSIRO_MK3.6.0 and MIROC-5 GCMs. Figure 13: Soil erosion rates projections in Kenya for the periods of the 2030s and 2080s under RCP 2.6 and 8.5 scenarios, driven by CCCMA_CANESM2, CESM1-CAM5, CSIRO_MK3.6.0 and MIROC-5 GCMs. Figure 14: (**Top**) Predicted average rainfall erosivity in Kenya under all scenarios for the 2030s and 2080s time periods; (**Bottom**) predicted average soil erosion rates in Kenya under all scenarios for the 2030s and 2080s time periods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **LVR** & **CER** & **CR** & **NER** & **RVR** \\ \hline Baseline Soil Loss rate (t ha\({}^{-1}\) yr\({}^{-1}\)) & 15.82 & 4.82 & 2.11 & 0.70 & 7.05 \\ \hline CCCMA\_CANESM2 & 19.60 & 7.58 & 2.47 & 0.85 & 10.97 \\  & Change (\%) & 23.89 & 57.26 & 17.06 & 21.43 & 55.60 \\  & CESM1-CAM5 & 16.49 & 5.63 & 2.32 & 0.79 & 7.43 \\  & Change (\%) & 4.24 & 16.80 & 9.95 & 12.86 & 5.39 \\ RCP-2.6 (2030s) & CSIRO\_MK3.6.0 & 15.66 & 5.07 & 1.93 & 0.63 & 6.71 \\  & Change (\%) & \(-1.01\) & 5.19 & \(-8.53\) & \(-10.0\) & \(-4.82\) \\  & MIROC-5 & 15.33 & 4.67 & 1.96 & 0.68 & 6.10 \\  & Change (\%) & \(-3.10\) & \(-3.11\) & \(-7.11\) & \(-2.86\) & \(-13.48\) \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Projected change to the mean baseline soil loss rate (%) across the regions of Kenya. The exception here was the MIROC-5 model, which indicated negative soil erosion rate changes in all the projections under the RCP-2.6 scenario. It was notable that only the MIROC-5 model for the Rift Valley Region gave a reduction in soil erosion rates in all of the future RCP-8.5 (2080s) scenarios (Table 16).
# TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data
## 4 Methodology
### The Baseline for Triplet-Based Deep Cross-Modal Hashing
#### 4.4.3 Update \(V\)  
We have fixed parameters \(u_{T}\) and \(u_{I}\) as learning hash code matrix \(V\). By the relation between trace and norm of matrix, to matrix \(N\), \(||N||_{B_{I}}^{2}=tr(NN^{\top})=tr(N^{\top}N)\). Thus, the loss function can be simplified as shown in Formula (18):  
\[max_{V}tr(V^{\top}(\gamma(B_{I}+B_{T}))) \tag{18}\]  
\[s.t.V\in\{-1,1\}^{c\times n}.\]  
Let \(B=\gamma(B_{I}+B_{T})\):  
\[max_{V}\sum_{i,j}V_{ij}B_{ij} \tag{19}\]  
\[s.t.V\in\{-1,1\}^{c\times n}.\]  
Keep \(V_{ij}\) and \(B_{ij}\) the same sign:  
\[V=sign(\gamma(B_{I}+B_{T})). \tag{20}\]  
Given text modal data and image modal data, semantic quantization coding of text modal data and image modal data can be realized by TDCMR model. Meanwhile, semantic similarity of heterogeneous multimedia data can be measured by hamming distance. During the concrete process, the text modal data \(T\) generates semantic quantization coding \(v^{T}\), and the calculation process is shown in Formula (21):  
\[v^{T}=b^{T}(T)=sign(f^{T}(T;u_{T})). \tag{21}\]  
Image modal data \(I\) generate semantic quantization coding \(v^{I}\), as shown in Formula (22):  
\[v^{I}=b^{I}(I)=sign(f^{I}(I;u_{I})). \tag{22}\]  
As shown in the Algorithm 1, the following is our optimization procedure of the proposed TDCMR.  
```
1:Input Textual dataset \(\mathcal{T}\); Image dataset \(\mathcal{I}\); Tuple label \(\Gamma\);
2:Output Text feature extraction network parameters \(u_{T}\); Image feature extraction network parameters \(u_{I}\); Hash code matrix \(V\);
3: Initialize parameter \(u_{T}\) and \(u_{I}\);
4: The number of samples taken for each iteration \(N_{T}=N_{I}=128\);
5: The maximum number of iterations \(t_{T}=\frac{n}{N_{T}}\)\(t_{I}=\frac{n}{N_{I}}\);
6:for\(p=1\) to \(N\)do
7:for\(i=1\) to \(t_{T}\)do
8: Random sampling of \(N_{T}\) text samples from \(\mathcal{T}\) to build a batch of dataset;
9: Taking anchor samples from batch data to build a set of triples samples;
10: For each text sample \(T_{i}\), calculated \(G_{si}=f^{T}(T_{i};u_{T})\) by forward propagation;
11: Gradient \(\frac{\partial L}{\partial u_{I}}\) in Equations (3)-(10);
12: Update parameters \(u_{T}\) by backward propagation;
13:endfor
14:for\(j=1\) to \(t_{I}\)do
15: Random sampling of \(N_{I}\) text samples from \(\mathcal{I}\) to build a batch of dataset;
16: Taking anchor samples from batch data to build a set of triples samples;
17: For each text sample \(I_{i}\), calculated \(B_{Isi}=f^{I}(I_{i};u_{I})\) by forward propagation;
18: Gradient \(\frac{\partial L}{\partial u_{I}}\) in Equation (15);
19: Update parameters \(u_{I}\) by backward propagation;
20:endfor
21: Update \(V\) in Equation (18);
22:endfor
```  
**Algorithm 1** Optimization procedure of the proposed TDCMR
# Real-Time Tomography of Gas-Jets with a Wollaston Interferometer
## 4 Examples of Gas Jet Density Measurements
### Error and Stability Analysis  
For the phase measurements of the piezo valve, an average of 1000 images is taken in order to reduce noise. This leads to an error of below 1 mrad (standard deviation \(\sigma\)), which is needed in order to measure low densities down to \(1.5\times 10^{15}\) cm\({}^{-3}\) with an uncertainty of \(0.4\times 10^{15}\) cm\({}^{-3}\).  
In the case of the solenoid valve, the purpose is to create and measure a shock front with densities up to three magnitudes larger than in the scenario described above. Combined with the fact that tomography requires more data (phase projections from different angles), it is decided to reduce the number of images per measurement drastically. For instance, the peak density in the shock front at 35 bar, 1 mm away from the nozzle, is 1.5 rad and has a standard deviation of 0.05 rad. After averaging, the phase signal has noise in the order of 0.01 rad. As the maximum of the phase signal is around 1 rad, the ML-EM test (Section 3.2.2) is realistic with a noise level of \(\sigma=0.01\) (the generic distribution for this test is normalized, such that the maximum of the projections is equal to one). In agreement with the ML-EM convergence test, the number of iterations for the 3D density reconstruction is set to 10. Figure 20 shows the density distribution of the shock front at 35 bar after 10 iterations and the noise of the reconstructed image. The noise is calculated as follows. The data are fitted with a Savitzgy-Golay filter, which smooths noisy data by interpolation within a symmetric window around each data point (window size: 51 pixels, polynomial order: three) [29; 30]. The fit is subtracted from the noisy data, then divided into segments of 30 pixels (0.2 mm), over which the standard deviation is calculated. This provides a local error estimate of the reconstructed density. The noise (\(\sigma<1.4\times 10^{17}\) cm\({}^{-3}\)) in the region of the shock front is a factor of 20 lower than the peak density \(2.8\times 10^{18}\) cm\({}^{-3}\), i.e., the density change in the shock front is reconstructed with sufficient precision.
## 1 Introduction
D-S evidence theory can effectively fuse data from different evidence sources through Dempster's synthesis rule, so that it can perform well in scenarios where multiple sources of information need to be integrated. However, the traditional DS evidence theory still has some limitations, which is more sensitive to high-conflict evidence. Different information sources can describe the risk state of foundation pit from different angles, so there may be great conflicts between them. In practical engineering, when two evidence pieces from different sources have high support for the same proposition, but due to the conflict between them, the fusion results may significantly reduce the support for the proposition, or even draw the opposite conclusion. When dealing with the problem of high-conflict evidence fusion failure, it can be divided into two categories according to the source of conflict. The first type is the conflict between the evidence bodies, which is caused by the inconsistency or contradiction of the evidence itself. For example, different evidence sources have great differences in the distribution of trust in the same event. The solution can be described as follows: before evidence synthesis, the original evidence can be preprocessed to reduce conflicts. By discounting the evidence, that is, weighting according to the conflict degree and uncertainty of the evidence, the influence of unreliable evidence on the synthesis result is reduced. The second type is the defect of the evidence fusion rule, and the limitation of the fusion rule itself in high conflict scenarios, such as the DS rule magnifies the conflict due to normalization. The solution can be described as follows: by introducing fine-tuning items, weighted averages, etc., the synthesis rules are improved to improve the rationality and accuracy of the synthesis results. Therefore, a fusion early-warning method for subway pit collapse risk based on machine learning and improved cloud D-S evidence theory is proposed. The prediction accuracy of three small sample machine learning models is compared using the multi-step rolling method to obtain the optimal prediction results, and the cloud model is used to transform them into basic probability assignment (BPA). A new evidence correction parameter is defined based on conflict degree and uncertain degree. A fusion early-warning model resolves high-conflict multi-data fusion failures, providing optimized methodologies for risk early-warning in subway foundation pit construction.
###### Abstract
The main contributions are as follows:  
* The relationship between representation noise and represented values of a stochastic bit stream is theoretically analyzed, and it is found that stochastic computing can achieve high accuracy in certain value intervals, providing a potential way to improve the accuracy even with the same stochastic bit stream length. * An adaptive scaling algorithm (ASA) is proposed for the SFIR to scale both the input signals and coefficients into low-noise regions. * A novel antithetic variables method (AV) is proposed to further improve the accuracy, and the theoretical proof is also provided. * The hardware architecture of the proposed ASA-SFIR and ASA-AV-SFIR is designed and implemented, which demonstrates high-accuracy performance advantages with respect to the existing SFIR filters. The remainder of this paper is organized as follows. Section 2 introduces the theoretical background of FIR filter, stochastic computing, and stochastic filter designs. Afterward, the proposed design with ASA and AV is presented in Section 3. Section 4 demonstrates the performance evaluation and hardware implementation of the proposed design. The last section concludes the performed work and discusses the potential future work.
# Fault Detection Algorithm Based on Dynamic Global-Local Preserving Projection
## 3 Fault Detection Based on DGLPP
### DGLPP Fault Detection Process
Thus, the system is prevented from operating normally, and a failure occurs. Four methods are used to detect this fault. The detection results are shown in Figure 4. All four methods can detect faults and realize continuous alarms. However, their fault detection sensitivities are nonidentical. PCA, LPP, GLPP, and DGLPP start to detect faults at 176, 171, 169, and 165 sample points, respectively. The test results demonstrate that the DGLPP method can detect the fault early, indicating that it has the best detection capability. The fault detection capability of the GLPP method is superior to the fault detection capabilities of the PCA and LPP methods. The reason is that GLPP considers the global and local features of the data in the modeling process and realizes a comprehensive feature extraction of the training data under normal conditions. Thus, its fault detection ability is improved. The DGLPP method further considers the dynamic characteristics of the data based on the GLPP method, and the data characteristics are retained accurately, further improving the detection performance. Figure 4: _Cont._  
Figure 3: Monitoring charts for Fault 15: (**a**) PCA; (**b**) LPP; (**c**) GLPP; (**d**) DGLPP. Fault 10 is a random disturbance in the feed temperature of Material C. This disturbance causes fluctuations in the feed temperature of Material C, resulting in a fault. The effect of fault detection is shown in Figure 5. When both T\({}^{2}\) and SPE statistics do not exceed the limit, it indicates that the system is operating normally at this time. Otherwise, it will cause a malfunction. Therefore, as shown in Figure 5, the PCA method can only detect a small number of faults. The SPE statistics of the LPP, GLPP, and DGLPP methods can hardly detect the faults, whereas the T\({}^{2}\) statistics can detect most of the faults. This is because there is a fault between process variables, and the relevant measurement coefficients between them remain stable. This caused a significant jump in the T\({}^{2}\) index value, while the SPE index remained within the threshold range with relatively small changes. However, these three methods also have underreporting in the subsequent detection process. DGLPP has relatively fewer missed sample points than the LPP and GLPP methods. With sample points 510-550 as the example, LPP, GLPP, and DGLPP miss 22, 21, and 19 sample points, respectively. Therefore, DGLPP has less under-reporting and higher detection accuracy than LPP and GLPP. Figure 4: Monitoring charts for Fault 2: (**a**) PCA; (**b**) LPP; (**c**) GLPP; (**d**) DGLPP.
# Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems
## 7 Conclusions  
We propose an optimization model to jointly consider the usage of carrier vehicles and bike trailers. Additionally, we build a profit objective to calculate the value of carrier vehicle routing and bike trailers, by considering a variety of constraints with respect to vehicle routing and bike repositioning. In the future, we will develop a budget feasible mechanism that solves the uncertainties in completion time of trailer tasks and build an iterative scenario generation approach in which a trailer can pick bikes from one or several stations and then drop them at one or more stations. It is also interesting to investigate the possibility of learning action models [29; 30; 31; 32] and recognizing plans [33; 34; 35] to help improving bike repositioning under the framework of DRRPVT.  
Conceptualization, X.Z. and M.T.; methodology, X.Z.; software, X.Z. and M.T.; validation, X.Z., M.T., Y.L., Z.X. and H.H.Z.; formal analysis, X.Z.; investigation, X.Z.; resources, X.Z. and Z.X.; data curation, X.Z. and M.T.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z. and M.T.; visualization, X.Z. and M.T.; supervision, X.Z. and H.H.Z.; project administration, X.Z. and M.T.; funding acquisition, X.Z., Y.L., Z.X., and H.H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 62076263), Guangdong Natural Science Funds for Distinguished Young Scholar (Grant No. 2017A030306028), Guangdong special branch plans young talent with scientific and technological innovation (Grant No. 2017 TQ04X866), Pearl River Science and Technology New Star of Guangzhou and Guangdong Province Key Laboratory of Big Data Analysis and Processing, Key Project of the Humanities and Social Sciences Research in Universities of Guangdong Province (Grant No. 2018 WZDDXM032), Guangzhou Science and Technology Program 2020 Research on Inter/Intra Frame Prediction Optimization based on Deep Learning Network (Grant No. 202002030473).  
Data Availability Statement:Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 3: The maximal profit of the DRRPVT with the ratio of base stations to: (**a**) main stations, (**b**) vehicles, and (**c**) trailers.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 4 Performance Evaluation
### Federated Reinforcement Learning
#### 4.1.3 Validation of Learning Result  
In order to validate the result of learning, we performed a evaluation using the trained network, and Figure 7a shows the initial state of this experiment. As shown in the Figure 7a, UAVs are initially placed in the middle and traffic is distributed on the left side of the map. After sufficient steps proceeded to give UAVs enough time to move to the desired location as shown in Figure 7b, the distribution changes to place traffic on the right side of the map as shown in Figure 7c. Again, after predetermined steps to give UAVs enough time to change their location, the traffic is rearranged back to the left side of the map, and the above processes were repeated several times. As shown in the figures, the UAVs recognized the  
Figure 5: Average value of scores as the episode passes.  
Figure 6: Traffic map and UAV deployment as the episode passes. (**a**) 0 episode—traffic map. (**b**) 100 episode—traffic map. (**c**) 200 episode—traffic map. (**d**) 600 episode—traffic map. (**e**) 1000 episode—traffic map. (**f**) 0 episode—deployment. (**g**) 100 episode—deployment. (**h**) 200 episode—deployment. (**i**) 600 episode—deployment. (**j**) 1000 episode—deployment.  
traffic distribution and moved to the left, and then the UAVs moved to the right as the traffic distribution changed. The video of the experiment can be found at [39]. As shown in the figures and video, we can see that UAVs quickly deal with the change of traffic distribution and are properly deployed without clumping together to provide network services intelligently. As explained before, the number on the lines shows the amount of transmission. As shown in the figure, UAVs provided communication services using all of their service capability which was set to 5000 in this evaluation, which means that the UAVs were in proper locations.
## 3 Tests Results
### Friction and Wear
The self-lubricating material was coated evenly on the fiber surface, which covered the fiber's grooves almost entirely. This configuration makes the fiber-matrix interface less prone to damage and enables effective stress transfer to the carbon fiber, thereby enhancing the material's structural strength and durability. The quality of the transfer film plays a key role in the wear resistance of the bearing bush, which is due to the continuous improvement of the uniformity and continuity of the transfer film in the friction process. With time's passage, the frictional torque, which was being measured, exhibited a slow downward trend as a result. It is directly proportional to the test pressure, according to Equation (1), that the friction torque of the bearing bush is. \[T=P\frac{\pi D^{2}}{4}\mu r \tag{1}\]  
where \(T\) is the frictional torque, \(P\) is the test pressure, \(D\) is the piston diameter, \(\mu\) is the friction coefficient, and \(r\) is the roller diameter. As the test pressure mounts, the friction torque also experiences an uptrend. However, under certain pressure conditions, the friction torque does not uniformly escalate with the test pressure. Instead, it may exhibit a trend of stability or even diminish. The addition of SCF fully fills the intermolecular voids between the PEEK and polytetrafluoroethylene (PTFE) chains, resulting in a decrease in the friction torque of the composite material. By calculating the friction torque under different pressures, the friction coefficient of the SCF-modified bearing bush under different pressures can be obtained, as shown in Figure 6.  
The friction coefficient of the SCF-modified bearing bush exhibits a consistent trend as the experimental pressure varies. Specifically, as the experimental pressure increases from 15 MPa to 25 MPa, the friction coefficient of the SCF-modified bearing bush decreases significantly, from 0.420 to 0.296. However, when the experimental pressure increases from 25 MPa to 30 MPa, the friction coefficient of the SCF-modified bearing bush rises, but the increase is not substantial. Figure 6: Friction coefficient of modified bearing bush under different test pressures.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 1 Introduction
These performance degradations are due to the inability of the LS estimator to dynamically track the channel variations in the time domain. On the other hand, the performance of the STA technique is in general poor regardless of the relative speed, acceleration, frame length, or scattering conditions since the frequency-domain averaging operations are not effective in compensating the channel's non-stationary characteristics. Therefore, we introduce a modification to the original STA technique that considers only the temporal averaging. This modification aims to improve the BER performance of the system in non-WSSUS channels and overcomes the challenge of equalizing large data frames, which is a drawback of the LS estimator. In general, our results show the applicability of the proposed simulator to evaluate the performance of V2X systems under different propagation conditions in road safety scenarios. The rest of this paper is organized as follows. In Section 2, we expand the discussion of wireless communications in road safety scenarios and of the state-of-the-art in vehicular channel simulators. In Section 3, we review the specifications for V2X communication systems given in the IEEE 802.11p and the LTE-V2X standards. In that section, we also present a mathematical model of a multicarrier data frame that is valid for both the IEEE 802.11p and the LTE-V2X standards. In Section 4, we present the reference channel model. Then, in Section 5, we introduce the simulation model for the described reference channel. In Section 6, we describe the IEEE 802.11p estimation techniques and present the simulation results in three road safety scenarios. Finally, we present the conclusions of this research work in Section 7. Notation: Capital bold letters denote matrices. Lowercase bold letters correspond to vectors. The scalar element placed in the \(m\)-th row and the \(k\)-th column of a matrix \(\mathbf{M}\) is represented by \(\langle\mathbf{M}\rangle_{m,k}\). The set of the complex numbers is denoted as \(\mathbb{C}\).