esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The 81113 Implementation Method and Effect Analysis of Common Pressure Equalization on Working Face
#### 3.3.2 Implementation Effect of Fracture Zone Pumping  
1. The gas in the gob covered by layer 12# 8723 was reduced from 10.5% to 4.5% through the gas extraction of the gob covered by layer 12# 8723 for 5 months before mining. Both the gas sampling and laboratory results of the gob and the on-site manual observation data show that the gas concentration in the gob showed an obvious downward trend. The absolute gas emission in the 307-panel area is 7.39 m\({}^{3}\)/min, the gas extraction concentration in 307 is 6.55%, the gas extraction pump quantity is 107.84 m\({}^{3}\)/min, the negative pumping pressure is 11 kpa, and the extraction rate is 52%.
2. During the mining of the 8723 working face of layer 14#, the goaf of layer 12# 8723 continues to be pumped to ensure that the working face of layer 14# 8723 can still produce normally without taking pressure-boosting measures, and the gas concentration in the upper corner is stable at about 0.3% during this period. Compared with the gas concentration of 0.7% in the upper corner of layer 14# 8721 of the previous working face, the gas concentration has decreased significantly. The change in gas concentration in the corner of the working surface of layer 12# 8723 goaf and layer 14# 8723 are shown in Figures 12 and 13.  
Figure 11: The 307-panel pumping system diagram and drilling layout diagram.  
(3) The analysis of the overall change trend of gas is as follows: 1 The gas concentration in the goaf shows an overall decline trend because the gas concentration in the goaf decreases after a long time of extraction; 2 the oxygen concentration in the goaf increases as a whole because the fresh air flow from the air inlet trough of the working face 8723 of layer 14# enters goaf 12# through the working face and maintains a low balance after stopping; 3 and the carbon monoxide concentration has no significant change, which has been maintained below 24 ppm.  
Figure 12: It can be seen in Figure 12 that the gas content in the upper corner of the working face is low after mining. After the first collapse, 3/4 days later, the gas content increases to about 0.3%.  
Figure 13: It can be seen in Figure 13 that the oxygen concentration in the goaf varies greatly, which is related to the pump opening time.
# Study of Load Calculation Models for Anti-Sliding Short Piles Using Finite Difference Method
## 1 Introduction
Subsequently, it applies the Euler-Bernoulli elastic foundation beam theory [31; 32]. The embedded section is treated as a linear elastic foundation, the sliding surface is simplified as a fixed support, and the load-bearing section is considered as a cantilever section. Simultaneously, with an understanding of the applicable conditions and stress characteristics of anti-sliding short piles [33; 34; 35; 36; 37], the paper addresses the boundary conditions at the pile's top and bottom. It employs an enhanced finite difference method for structural stress calculations in anti-sliding short piles. The paper proposes a comprehensive model for calculating the internal forces of anti-sliding short piles and validates this model through large-scale indoor physical model experiments. This validation ensures the model's reliability and provides design theory references for future landslide control engineering.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### How the Optimizations Were Achieved
#### 5.1.5 Impact of Optimizations  
In the light of above-mentioned optimizations, the proposed protocol exhibited practically constant route request and route reply to setup time and less overhead when compared to the old AODV and mesh protocols. The BLE mesh had very high overheads due to packet retransmissions, which the proposed protocol eliminated. We used the network's relay feature to control the number of packet retransmissions to support directed multihop transmission. In addition, for improved protocol efficiency, we increased the buffer size in the controller for the queuing of a few extra Tx and Rx PDUs that showed better results in terms of packet loss and PDR.
# State Merging and Splitting Strategies for Finite State Machines Implemented in FPGA
## 2 Materials and Methods
### Estimation of Optimization Criteria
#### 2.2.3 Estimation of Transformation Quality Ratio  
If we want to use different criteria to evaluate the quality of the merging or splitting the states, a weighted sum can be used, which is one of the well-known methods of discrete multicriteria optimization [38]. Due to its ease, this method is probably the most widespread solution. In this method, a scalar cost function is specified as an aggregation of costs with weights.  
Let \(F=(F_{1},\,\ldots,\,F_{d})\) be a \(d\)-dimensional function and let \(\lambda=(\lambda_{1},\,\ldots,\,\lambda_{d})\) be a vector which fulfils the following conditions:  
\[\forall j\in[1\ldots d],\lambda_{j}>0, \tag{10}\]  
\[\sum_{j=1}^{d}\lambda_{j}=1. \tag{11}\]  
The \(\lambda\)-aggregation of \(F\) is the following function:  
\[F^{\lambda}=\sum_{j=1}^{d}\lambda_{j}F_{j}. \tag{12}\]  
Naturally, the elements of \(\lambda\) correspond to the relative significance (weight). In this case, we have two criteria: power \(P_{i}\) and speed \(S_{i}\). The weights for them can be specified by the user, appropriately \(w_{P}\) (power) and \(w_{S}\) (speed). With respect to above consideration, the transformation quality (aggregation) function \(Q_{i}\) for single state (for splitting) or any pair of states (for merging) can be specified as follows:  
\[Q_{i}=w_{P}\hat{P_{i}}+w_{S}\hat{S_{i}}, \tag{13}\]  
where \(\hat{P_{i}}\) and \(\hat{S_{i}}\) are normalized criteria parameters \(P_{i}\) and \(S_{i}\). The normalization is performed to eliminate the influence of wide range of magnitudes for the considered parameters. The normalization can be described by the formula  
\[\hat{K_{i}}=(K_{max}-K_{i})/(K_{max}-K_{min}), \tag{14}\]  
where \(K_{i}\)--one of considered criteria parameters (\(P_{i}\) or \(S_{i}\)), \(K_{max}=2\cdot K_{i}\), \(K_{min}=0\). The assumed values of \(K_{min}\) and \(K_{max}\) ensure that the initial value of the transformation quality ratio will be equal to 0.5.
# Precise Orbit Determination for GNSS Maneuvering Satellite with the Constraint of a Predicted Clock
## 3 Experiment Analysis
### Test on the Real Maneuver Cases
It is noticeable that the unhealthy epoch in broadcast ephemeris of G12 is later than the actual maneuver time, which may mislead real-time users and degrade the positioning results. We further investigated the proposed method by comparing the forward and backward clock-constrained RPPP POD solutions, of which the orbit differences in the radial, cross-track and along-track directions are plotted in Figure 8. As shown in this figure, the orbit differences of \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{2}{c}{**Unhealthy Epochs in BRDC**} & \multicolumn{2}{c}{**Detected Maneuver Time**} \\ \cline{3-6}
**PRN** & **DOY** & \multicolumn{2}{c}{**Start and End Time**} & **Duration** & **Start and End Time** & **Duration** \\ \hline G22 & 125 & 09:59:44–14:00:00 & 2 h 0 min 16 s & 10:11:00–10:12:30 & 1.5 min \\ G12 & 139 & 01:59:44–07:57:36 & 5h 57 min 52 s & 01:50:00–0 cd 1:51:00 & 1 min \\ C02 & 136 & 09:00:00–16:00:00 & 7 h & 09:24:00–09:49:30 & 25.5 min \\ C03 & 139 & 07:00:00–15:00:00 & 8 h & 08:10:30–08:49:00 & 38.5 min \\ C01 & 142 & 03:00:00–10:00:00 & 7 h & 05:18:00–05:54:00 & 36 min \\ C05 & 144 & 23:00:00–05:00:00 (+1d) & 6 h & 00:29:00 (+1d)–00:51:30 & 22.5 min \\ C04 & 151 & 01:00:00–09:00:00 & 8 h & 01:44:30–03:31:30 & 107 min \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Unhealthy epochs in broadcast ephemeris and detected maneuver time (May in 2017). Figure 7: Orbit differences between the RPPP solutions and the forward and backward integration orbits for G22, G12, C02 and C04 around maneuvers. The vertical dashed lines indicate the detected start and end time of maneuver events. GPS satellites vary in the range of \(-20\) cm to \(20\) cm in all directions, even in the maneuvering periods. The orbit differences of BDS satellites exhibit similar small variation but obvious bias in all directions. The fixed parameters in the maneuver POD arc in the forward and backward clock-constrained RPPP POD processing are generated from different dynamic POD arcs. Precise force models and good geometric conditions, such as GPS, contribute to precise and consistent parameter estimation and therefore consistent POD solutions for the forward and backward clock-constrained RPPP. Otherwise, the forward RPPP POD solution will be inconsistent with the backward RPPP POD solution, resulting in biases in the orbit differences, such as BDS. The biases of C04 orbit differences are much larger than that of C02, which can be attributed to the short POD arc in the backward processing as mentioned before.
## 3 Results
### Results of the Unsupervised Method  
The unsupervised clustering method \(k\)-means was used on the same dataset as presented in Section 3.1. The dataset was split into day profiles so that the days could be clustered. The time between 06:00 and 21:00 was used for the profile, which made the day profile size of 16 values. In Figure 10, the three graphs presented contain day profiles with one of the variables, where lines represent the consumption of every weekday in the dataset. The data in each graph serves as an input for the \(k\)-means algorithm. From these graphs, it can be seen that equipment electricity consumption has two parts, where some days had significantly lower consumption and the rest with higher. Furthermore, higher consumption days could be divided into additional two parts: the lower part with fewer days (less dense part of the graph) and the higher part (dense part). It should be noted that it was more difficult to divide the graphs with lighting and especially with water consumption. The method used for selecting which variables (consumptions) could be used in the clustering algorithm and in how many (\(k\)) clusters should the dataset be divided, is discussed in the following section.  
Figure 9: Occupancy prediction error (RMSE) for different testing periods as a function of training duration.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 4 Experimental Results and Analysis
### Underwater Imaging Experiment with Different Turbidity  
In the imaging experiment under different water turbidities, metal badges are placed as the targets. By evenly diluting a certain amount of milk into clear water, the simulation of water with different turbidity is realized. In the experiment, six kinds of turbid solutions with milk concentrations of 0.3 mL/L, 0.6 mL/L, 0.9 mL/L, 1.2 mL/L, 1.5 mL/L and 1.8 mL/L are prepared. The light intensity image and the degree of polarization image are collected for the target at the depth of 30 cm. The results are shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Metal 1**} & \multicolumn{3}{c}{**Metal 2**} & \multicolumn{3}{c}{**Metal 3**} \\ \cline{2-10}  & **EME** & **H** & \(\overline{\mathbf{G}}\) & **EME** & **H** & \(\overline{\mathbf{G}}\) & **EME** & **H** & \(\overline{\mathbf{G}}\) \\ \hline Images \(I\) & 4.3865 & 5.3077 & 1.0372 & 4.8567 & 5.1512 & 0.9687 & 8.4450 & 3.9828 & 1.4285 \\ Images _DOP_ & 12.2876 & 7.1146 & 6.5648 & 15.8649 & 7.1613 & 7.3524 & 20.6643 & 7.4604 & 9.5242 \\ \hline \end{tabular}
\end{table}
Table 1: Quality evaluation of experimental images in low-visibility underwater scenes. Information entropy average gradient.  
Figure 12: Image I and DOP of different targets: (**a**) image I of metal 1; (**b**) image DOP of metal 1; (**c**) image I of metal 2; (**d**) image DOP of metal 2; (**e**) image I of metal 3; (**f**) image DOP of metal 3.  
Through the subjective evaluation method, it can be concluded that with the increase in the concentration of milk in the turbid solution, the clarity of the light intensity image and the degree of polarization image gradually decrease, and the saturation of the light intensity image is affected by the concentration of the milk in the solution. With the enhancement of the water scattering, the saturation decreases seriously, but the brightness will gradually brighten as the scattering increases. However, the correlation between brightness and the scattering of the polarization image is small.  
The change line of image quality evaluation data in different environments is shown in Figure 14. Obviously, with the increase in turbidity in the experimental solution, the EME, information entropy and average gradient in the light intensity image and the polarization image have decreased. It is verified that the scattering effect of medium particles is intensified with the increase in water turbidity. The increase in image noise leads to a reduction in target information and the degradation of image quality. In addition, the quality of the polarization image is generally higher than that of the light intensity image, and it can still maintain good detection ability in low-turbidity water.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 6 Conclusions  
This paper proposed a novel alternative framework for advancing the accuracy of non-parallel many-to-many voice conversions. The main idea was to employ a sinusoidal model with continuous parameters to generate converted speech signals with an adversarial training network. The main advantages of the sinusoidal model are the high accuracy of harmonic parameter estimation and increased fidelity in converting the source speaker to the target speaker. From the empirical studies, it was confirmed that the proposed approach has a better ability to convert the source speaker to the target one than the state-of-the-art system. The results of the listening test also revealed the effectiveness of the suggested system for improving the quality of synthetic speech comparable with StarGAN.  
As future directions, we will attempt to increase the usability of our sinusoidal approach by using the Griffin-Lim algorithm [41] and \(d\)-vector technique [42]. The GLA is a phase reconstruction method that involves an iterative procedure for estimating a signal from the short-time Fourier transform (STFT) magnitude. The GLA is based only on the consistency and does not take any prior knowledge about the target signal into account. We also plan to adopt a deep vector or \"d-vector\" method for voice conversion. A d-vector is a fixed-dimensional representation of a speech utterance that enables more natural voice conversion. We hope that our work enables users to generalize more VC tasks (e.g., multi-emotion and multi-pronunciation).  
Conceptualization, M.S.A.-R. and T.G.C.; Formal analysis, M.S.A.-R. and T.G.C.; Investigation, M.S.A.-R.; Methodology, M.S.A.-R. and T.G.C.; Project administration, G.N.; Resources, M.S.A.-R.; Software, M.S.A.-R. and T.G.C.; Supervision, G.N.; Funding acquisition, G.N.; Writing--original draft, M.S.A.-R.; Writing--review and editing, T.G.C. and G.N. All authors have read and agreed to the published version of the manuscript.  
The research was partly supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 825619 (AI4 EU), and by the National Research Development and Innovation Office of Hungary (FK 124584 and PD 127915).  
Not applicable.  
Figure 7: Results of the speaker similarity test.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable. The study did not report any data.  
**Acknowledgments:** The Titan X GPU used was donated by NVIDIA Corporation. We would like to thank the subjects for participating in the listening test.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# A Review on Internet of Things for Defense and Public Safety
## 4 Operational Requirements
### Coverage Capabilities  
Defense and PS should invest in resilient, flexible and interoperable capabilities to operate at extended ranges under adverse weather conditions and harsh environments (including Line Of Sight (LOS) and Non Line-Of-Sight (NLOS) scenarios) in enemy territory, and enhance connectivity in denied areas. One of the technologies that can deliver mobile and persistent connectivity is CubeSat: nano satellites that can be deployed in large number to create potentially more resilient constellations [103]. CubeSat deployment is also faster than in larger satellites, as they can be launched into orbit in clusters or piggybacked on other loads. It supports Software Defined Radio (SDR) to enable reconfigurability of data management, protocols, waveforms, and data protection.  
Other technologies are High-Altitude Platforms (HAPs) and Unmanned Air Vehicles (UAVs), such as drones that operate above the range of terrestrial communication systems and can be equipped with communication relays. Unlike satellites, which eventually become defunct, HAPs can be upgraded and enhanced as technologies evolve. They also have significant advantages over manned communications platforms, as they can stay airborne continuously for long periods.  
The U.S. military has already deployed four EQ-4B Global Hawk Block 20 Drones with the Battlefield Airborne Communications Node (BACN) system, but it will need significantly greater capacity to deliver connectivity to a full suite of connected devices across multiple theaters. DoD is now involved in the development of Northrop Grumman RQ-4 Global Hawk Block 30 and 40, ground stations, and Multi-Platform Radar Technology Insertion programs. The U.S. Navy will get a persistent maritime ISR capability through the MQ-4C Triton. DoD is now funding the procurement of two Low Rate Initial Production (LRIP) systems and continues to fund development activities associated with software upgrades [104; 105].
# Model Test Analysis of Groundwater Level Fluctuations on
###### Abstract  
Engineering practice and real-life cases show that the geological conditions of the Guilin overlying karst site are complex. In particular, the groundwater, which drives the accelerated formation of soil cavities, and the thickness of the overlying soil layer, which affects the speed of the groundwater subsidence process. Therefore, this paper is based on a physical model to evaluate the effects of groundwater level changes caused by different factors on the deformation of karst cover. The model tests are simulated for different cover thicknesses (6 cm, 9 cm, 12 cm, 15 cm, 18 cm) under rainfall and other recharge, cavity supply, and drainage conditions at the same density (1.40 g/cm\({}^{3}\)) and initial water content (30%), respectively. The results show that with the increase of rainfall and other recharge time, the basic change trend of different cover thicknesses is that the infiltration curve changes faster at the beginning and slows down at the end, but the thicker the cover, the slower the overall deformation; at a certain rate of cavity recharge and drainage, the thicker the cover, the smaller the deformation caused by the fluctuation of groundwater level. The cavity recharge makes the cover displacement obvious, in the order of 0.304 cm, 0.173 cm, 0.118 cm, 0.068 cm, and 0.056 cm. After the formation of the cavity, the rainfall, other recharge, and the cavity supply and drainage accelerate the destruction and deformation of the soil body and the upward development of the cavity. The research results provide theoretical support for the subsequent prevention and control of karst collapse in covered karst areas, and have certain practical engineering significance.  
karst collapse; physical model; groundwater level changes; cover deformation Article  
Karst Cover Deformation Taking the Monolithic Structure of Guilin as an Example.
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 2 Materials and Methods
### Machine Learning Model Selection and Its Optimization
#### 2.3.1 Machine Learning Model Selection  
With the development of machine learning algorithms applied to agricultural prediction models with increasing accuracy, Extreme Gradient Boosting (XGboost), decision tree regression, Long Short-Term Memory (LSTM) regression, SVR, GPR, and random forest regression (RFR) are the methods that have been proven to be more effective [30; 31; 32; 33].  
XGboost is a gradient boosting algorithm that is highly flexible and accurate, performs well with large-scale data and complex features, is able to handle nonlinear relationships, and is robust to missing values. Decision Tree is an intuitive and easy to explain algorithm with a strong ability to fit non-linear relationships between features, and is computationally fast compared to other algorithms. LSTM model is a deep learning model for time series data that captures long-term dependencies in time series and performs well for datasets with memory properties. However, XGboost, Decision Tree and LSTM models are sensitive to the overfitting problem on small datasets, and their performance on small datasets is unstable, where Decision Tree is susceptible to the effects of noise and local features in the data, and the LSTM model usually requires a large amount of data for training, which is a long training time compared with traditional machine learning algorithms.  
The three methods, SVR, GPR and RFR, have some advantages in targeting the small dataset containing soil physicochemical properties and yield in this experiment. SVM is able to deal with high dimensional data and non-linear relationships, has a strong generalization ability for small sample datasets and has some advantages in controlling model complexity. GPR is a non-parametric model that can flexibly adapt to the data and provides an estimate of prediction uncertainty, which is advantageous for modelling small data sets. Random Forest is an integrated learning algorithm that performs well for noisy data and datasets with complex relationships. It is also relatively robust to feature selection and missing values.  
For this study, the three methods of SVR, GPR and RFR are more suitable for constructing accurate prediction models, which can effectively control the complexity of the model when dealing with small datasets, and have better stability and generalization ability.