esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Geological Characteristics of Lushan Geothermal Springs  
#### 3.2.1. Geological Characteristics of Shangtang Geothermal Field  
(a) General Situation of Shangtang Hotspring  
Shangtang hot spring outcrops on the first terrace on the south bank of Sha River, 8-18 m higher than the river, with an altitude of 270-280 m. The hot spring has a large thermal anomaly range, with a length of 640 m from north to south and a width of 250 m from east to west. It outcrops in the form of spring group, with many spring holes. The measured temperature of No.1 geothermal well (under exploitation) was 61 \({}^{\circ}\)C, and the flow was 100 m\({}^{3}\)/h; the measured temperature of No. 2 geothermal well was 61 \({}^{\circ}\)C, and the artesian flow was 46.5 m\({}^{3}\)/h; the measured temperature of No. 3  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Items** & **Shangtang** & **Shangtang** & **Zhongtang** & **Wentang** & **Xitang** & **Xitang** & **Xiver** & **Seentang** \\  & **Hotspring** & **River Water** & **Hotspring** & **Hotspring** & **Hotspring** & **Hotspring** & **Water** & **Well** \\ \hline pH value & 8.48 & 7.41 & 8.7 & 8.62 & 7.42 & 7.04 & 7.91 \\ Mineralization degree (g/L) & 0.641 & 0.173 & 0.498 & 0.599 & 0.598 & 0.225 & 0.649 \\ Cl (mg/L) & 22.41 & 5.02 & 22.68 & 21.36 & 33.02 & 6.62 & 33.8 \\ SO\({}_{4}\)\({}^{-2}\) (mg/L) & 111.9 & 31.11 & 92.11 & 89.65 & 106.1 & 43.96 & 157.5 \\ HCO\({}_{3}\)\({}^{-}\) (mg/L) & 229.9 & 61.76 & 140.7 & 200.7 & 251.0 & 110.9 & 171.5 \\ CO\({}_{3}\)\({}^{-2}\) (mg/L) & 10.12 & - & 13.5 & 13.5 & 0 & - & 0 \\ F\({}^{-}\) (mg/L) & 15.91 & 0.76 & 16.78 & 16.23 & 17.3 & - & 13.54 \\ Na\({}^{+}\) (mg/L) & 120.0 & 11.8 & 126.0 & 148.0 & 180.0 & 12.80 & 160.0 \\ K\({}^{+}\) (mg/L) & 3.75 & 1.99 & 4.71 & 4.23 & 7.5 & 2.66 & 6.7 \\ Ca\({}^{2+}\) (mg/L) & 4.89 & 20.4 & 5.25 & 3.8 & 8.34 & 35.4 & 7.5 \\ Mg\({}^{2+}\) (mg/L) & 0.9 & 3.37 & 0.26 & 0.044 & 0.42 & 7.27 & 0.22 \\ As\({}^{-}\) (mg/L) & 0.029 & - & 0.024 & 0.024 & 0.0011 & - & 0.0026 \\ Cu & \textless{}0.05 & - & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & - & \textless{}0.05 \\ Zn & \textless{}0.05 & - & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & - & \textless{}0.05 \\ Fe & \textless{}0.05 & - & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & - & \textless{}0.05 \\ Mn & \textless{}0.05 & - & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & - & \textless{}0.05 \\ Ugeothermal well was 61 \({}^{\circ}\)C, and the artesian flow is less than 1.0 m\({}^{3}\)/h. There were many spring spots around the main spring group.
# A Secure and Scalable Authentication and Communication Protocol for Smart Grids
## 4 Experimental Setup  
This section describes the experimental setup used to evaluate the proposed protocol designed for smart grid environments. The setup features a server-client architecture and simulates real-world network conditions for a smart grid environment.
## 3 Results
### The Effect on the Ionosphere Spatial Gradient  
Two MGEX sites, OUS2 and DUND, with a distance of 9.585 km, were selected to study the effect of this storm on the ionosphere spatial gradient. Figure 10 demonstrated the spatial gradient obtained from the GPS/GLONASS observations. The different colors denoted different satellites. All the ionosphere anomaly gradients discovered during the storm were collected and a total number of 59 satellites were considered. Figure 10a showed the ionosphere spatial gradient at one of the geomagnetic quiet days in the month (day of year (doy) 156), and the ionosphere spatial gradient was evenly distributed in accordance to the time (UT) and the maximum gradient below 15 mm/km, which is reasonable according to previous investigations [14; 34]. Figure 10b shows the ionosphere spatial gradient during the main phase of the storm (doy 174), and the huge variation of the ionosphere spatial gradient was discovered from 02:00 UT to 07:30 UT, with the maximum value approaching 250 mm/km. The minus sign denotes a decrease. After 08:00 UT, the ionosphere spatial gradient gradually recovered to its normal status. The results fit well with the results discussed in Section 3.4.  
Figure 10: The ionosphere spatial gradient from the Ous2-Dund sites. (**a**) day 156; (**b**) day 174.  
Figure 9: The prompt penetration electric field from the 22 to 23 June. (yellow padding: first sub-phase storm; light blue padding: second sub-phase storm; red line: PPEF; green line: quiet electric field).  
The maximum ionosphere spatial gradient was analyzed as Figure 11 shows; the maximum ionosphere spatial gradient occurred at GPS PRN 32 with a magnitude of 247.2 mm/km, around 04:00 UT, when the second sub-phase of the storm was dominant. The large ionosphere gradient was possibly related to the ionosphere irregularity during the second sub-phase of the storm, covering 160\({}^{\circ}\) E-170\({}^{\circ}\) E, 40\({}^{\circ}\) S-50\({}^{\circ}\) S, as demonstrated in Figure 8b. In Figure 12, the related IPP locations covered 164\({}^{\circ}\) E-168\({}^{\circ}\) E, 48\({}^{\circ}\) S-46\({}^{\circ}\) S. The dramatic variations of the ionosphere gradient shown in Figure 12 indicate the bubble effect on the trans-ionosphere GNSS signals, leading to the ROTI enhancement.  
A regional TEC map was developed with ous2-dund observations, as Figure 12 demonstrated. A clear irregularity evolution was indicated from 02:15 UT to 05:00 UT, during the second sub-phase of the storm. The bubble reached its maximum size in the longitudinal direction at around 03:30 UT and gradually shrunk until 05:00 UT. The morphology was in accordance with Figure 8. This revealed that the intensity of the ionosphere gradient is also related to the longitudinal coverage of the irregularity. The maximum gradient presented by PRN32 was located in the wider part of the irregularity.  
Figure 11: The ionosphere spatial gradient of satellite PRN32 generated by irregularities.  
Figure 12: The ionospheric irregularities evolution with the regional TEC map.
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 1 Introduction
Specifically, the quantile function is used to model the probability distribution of cumulative discount returns. By means of quantile regression, the output of the value network is transformed from the expected value of the cumulative discount return to the entire probability distribution, thus providing a more accurate estimate of the value. In the process of learning the control strategy, the risk evaluation index in the financial field is used to effectively measure the loss caused by overcurrent, so that the reinforcement learning agent learns a control strategy that is more sensitive to environmental restrictions. As a result, it can effectively alleviate the interference of overcurrent on motor control. Six different control tasks are used to verify the performance of the developed method. The results show that the proposed risk-sensitive control algorithm not only triggers fewer overcurrents, its control performance is also better than others in most cases. The key contributions of this work can be summarized as follows: (1) This paper discussed the possible damage to the motor caused by the reinforcement-learning-based control method in the process of interaction with the environment, for the first time. (2) Proposed a risk-sensitive intelligent control algorithm to avoid the overcurrent problem when training a reinforcement learning agent. (3) Verified the proposed control algorithm's effectiveness via six different control tasks.
# A Multi-Scale Feature Fusion Hybrid Convolution Attention Model for Birdsong Recognition
## 2 Research Method
### Construction of Time-Frequency Spectrum Features
#### 2.1.3 Log-Wavelet Spectrogram Features  
Continuous wavelet transform (CWT) employs the concept of multi-resolution analysis to perform non-uniform partitioning in the time-frequency domain, achieving appropriate time-frequency resolutions for different time signals. This characteristic makes wavelets particularly suitable for speech signal processing. For speech signals, the high-frequency components require better time resolution to detect transient parts that change rapidly, while the low-frequency components need higher frequency resolution to accurately track features such as slowly varying formats over time.  
The formula is shown in Equation (3), where \(Log-CwtSpectrum(t,f)\) represents the log-wavelet spectrum at time \(t\) and frequency \(f\) The term \(cwtmatr(t,\omega)\) denotes the wavelet coefficient matrix obtained from the continuous wavelet transform, representing the wavelet coefficients at time \(t\) and frequency \(\omega\). An example of the extracted birdsong audio is illustrated in Figure 1c.  
\[Log-CwtSpectrum(t,f)=\log(|cwtmatr(t,\omega)|+\epsilon) \tag{3}\]  
Figure 1: The spectral feature maps used in this article.
## 1 Introduction
[PERSON]'s study not only provides important observational insights and highlights the ability of ERA5 to fundamentally capture the observed patterns, but it also reveals many small- and meso-scale structures that are not captured by the remote-sensing instruments [16]. Despite its wide applications, the applicability of ERA5 datasets over mountainous areas requires further exploration because the spatial resolution may not be fine enough to capture all the complex physical processes and atmospheric motions induced by the rolling topography at the kilometer scale [8; 9; 10], especially for the mountainous areas in southeast China, a place appearing with frequent and specificmeteorological phenomena, such as uplift effects and the local circulation forced by the complex terrain [17]. Given the importance of atmospheric thermodynamic stratifications in both environmental and climatic research [18; 19; 20], we adopted the high-precision Doppler wind lidar and microwave radiometer, which has been widely applied in various terrains [17; 21; 22] and has achieved important results [23], to measure the thermodynamic profiles over a typical mountain site in the Wuyi mountains in southeast China to assess the vertical accuracy of the ERA5 reanalysis datasets in this area. The results provide an in-depth understanding of the vertical meteorological characteristics of the mountainous meteorology. It is also of great significance for giving important reference data for meteorological predictions and climate modeling in the southeast China mountainous region.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 4 Simulation Results and Discussion
Figure 4 represents the BERs of three iterative detection schemes versus SNR, with the transmitters using the R = 121/240 outer channel RM turbo code and the 4-QAM for \(N_{t}=N_{r}=3\), and the R = 676/988 outer channel RM turbo code accompanied by the 4-QAM for \(N_{t}=N_{r}=4\), respectively. The BER of the proposed scheme is not only a few orders of magnitude lower than those of the IMMSEPICDD, but also lower than that of the ISIGFDD. However, when the source employs the R = 121/240 outer channel RM turbo code, though the BER of the proposed scheme is somewhat lower than that of the ISIGFDD, the difference between them is not significant. This is because, in this circumstance, the information coded with the lower overall code rate, i.e., R = 121/240, the performances of these two detection schemes are almost characterized by the error protection (UEP) property of the shortened codes. However, when the overall code rate is higher and the number of transmit antennas is increased, the higher diversity order makes the proposed scheme achieve the best performance. These conclusions also coincide with the results provided by [4; 17] for the RM turbo code since both of the proposed scheme and the ones in [4; 17] are endowed with stronger component codes, i.e., the RM codes, as extended Hamming codes, to obtain additional coding gain though the scenario for them to operate are far from each other. Moreover, according to \(d_{gain}=\lim\limits_{SNR\rightarrow\infty}-\frac{\log P_{e}(SNR)}{\log(SNR)}\) where \(P_{\rm e}\) is the bit error rate [20] and SNR is the signal-to-noise ratio, we can discover that the diversity gain of the proposed scheme approximates diversity order 9 and 16 [20] as the SNR increases. Again, this demonstrates that the diversity order of the proposed algorithm is \(N_{t}\times N_{r}\). These conclusions are also consistent with the results provided by [14; 15] for the turbo MIMO systems. Its performance is superior to all other schemes considered in this context.
# Data Naming Mechanism of LEO Satellite Mega-Constellations for the Internet of Things
## 4 Simulation Settings
### Design of LEO Mega Constellation Simulation Platform
Second, the top-level control is responsible for maintaining and updating the intersatellite and satellite-to-ground communication links. In addition, it is responsible for data packet storage and routing between nodes in the network.
## 4 Datasets and Signals
### Datasets  
Regarding the datasets used for road surface anomaly detection and classification, authors have decided to generate or employ real datasets or generated datasets through simulation environments. For instance, [PERSON] et al. [18] use the Pothole Lab dataset introduced in Ref. [14] to generate a simulated dataset for road anomaly detection and classification. Another dataset that was used in this study is the [PERSON] et al. [17] dataset; this is one of the few datasets that are publicly available, which facilitates the reproducibility and comparison of the methodologies, algorithms, and results. [PERSON] et al. [32] also employed the datasets mentioned earlier in his study.  
One of the major drawbacks in the current state of the art is that the study must be limited to describing the methodology or algorithm proposed and the experimental settings of the data collection process. However, the dataset in most cases is not available by the authors, which limits the potential reproducibility of the studies and, consequently, the validation of the algorithms or methodologies. The above is crucial for learning-based techniques since they depend on the sampled data to provide a performance metric that allows a homogeneous comparison. Examples of studies without publicly available datasets are Refs. [1; 2; 3; 4; 5; 8; 35; 37; 38].
## 1 Introduction
For example, the Pearson correlation analysis, applied to investigate the relationship between growing season and seasonal Normalized Difference Vegetation Index (NDVI) and climatic variations in the ARB, was reported in [37] and it was concluded that growing season NDVI was mainly regulated by precipitation. A linear trend analysis of vegetation, temperature, and precipitation, and a comparison between the correlation of NDVI-temperature and NDVI-precipitation in northeastern China was carried out by [PERSON], et al. [43]. However, the analysis methods used in most of the previous studies focused solely on a temporal scale or spatial scale, and therefore, they do not provide a comprehensive analysis to deal with variability between vegetation dynamics and environmental factors at both spatial and temporal scales simultaneously [44; 45; 46]. Additionally, a few efforts have been made to investigate the relationships between hydrological factors and vegetation dynamics in the ARB, with close interactions between hydrological factors and vegetation growth having been found in many studies [47; 48; 49]. Therefore, exploring the spatial-temporal vegetation dynamics and their relationships with climatic, anthropogenic, and hydrological factors in the ARB is essential for better understanding the mechanisms driving vegetation changes, and can provide more detailed scientific support for environmental sustainability and agricultural production management. The objectives of this study are specified as follows:  
1. Analyze the spatial trends of three parameters (LAI, FVC and GPP) that can be considered as representative of the growth condition of surface vegetation in the study region using [PERSON]'s slope methods to understand the spatial variability of vegetation growth conditions. 2. Explore the spatial autocorrelation characteristics of vegetation indexes in the ARB, based on the results derived from [PERSON]'s I technique on remotely sensed vegetation information, for determining the rapid shift of eco-system changes over previous decades. 3. Utilize partial least squares regression (PLSR) and geographical and temporal weighted regression (GTWR) models to further evaluate the relationships between land surface parameters and climatic factors, land use/cover types, and hydrological variables simulated with the soil and water assessment tool (SWAT) model in the ARB to clarify spatial-temporal vegetation dynamics and their relationships with climatic, anthropogenic, and hydrological factors in the Amur River Basin.
## 2 Materials and Methods
### Seismic Observatory Stations  
KIGAM stations are identified by 3- or 4-letter acronyms signifying the location of the seismic observatory station. There are 62 stations under KIGAM operations, but 60 are considered in this study, as one station ceased operations early on (station GKP1) and another did not provide acceleration recordings (station CHNB). The locations of KIGAM stations are shown in Figure 1. The figure shows that although there is good spatial distribution, many of the stations are located in the southeastern area of South Korea as well as several island locations.  
Site-specific \(V_{\text{S30}}\) information was provided via KIGAM online data sources for several stations, but not all [47]. This is due to issues such as civil complaints and environmental concerns. However, a publication by KIGAM personnel lists discrete \(V_{\text{S30}}\) data from downhole testing [48]. These data are compiled and shown in Table 1. The table shows 29 stations with a field-measured \(V_{\text{S30}}\) via PS-suspension logging. Several of these stations were installed at depth, usually at 30 m, and are indicated in the table. Stations not listed herein did not have a downhole PS-suspension logging test measured \(V_{\text{S30}}\) in any official capacity. This results in 19 surface stations with a measured \(V_{\text{S30}}\) for analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Station** & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & \(V_{\text{S30}}\) **(m/s)** \\ \hline BOG & 35.70 & 129.21 & 633 \\ CGU & 35.82 & 129.27 & 779 \\ CRB & 37.39 & 127.96 & 1059 \\ DES & 36.15 & 129.31 & 502 \\ DUC & 35.74 & 129.18 & 755 \\ GCN & 35.82 & 129.14 & 808 \\ GHR & 34.49 & 126.90 & 612 \\ GKP2 \({}^{\text{a}}\) & 35.89 & 128.61 & 925 \\ GSU \({}^{\text{a}}\) & 35.16 & 128.10 & 1497 \\ HCH & 36.08 & 129.31 & 494 \\ \hline \end{tabular}
\end{table}
Table 1: KIGAM seismic observatory stations with an available \(V_{\text{S30}}\)[47].  
Figure 1: Locations of KIGAM seismic observatory stations.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 3 Results
### Impacts of Assimilation on Land Surface Variables Prediction
#### 3.1.3 Sensible Heat Flux
Firstly, it is probably because SM simulations are generally higher than the benchmarks (as shown in Figure 3), making H prediction lower than expected. Secondly, irrigation at this site in the middle of the growing season may also be a factor influencing simulation on H. Moreover, the H and LE are mainly affected by the atmospheric state variables (i.e., air temperature and specific humidity) in wet and/or densely vegetated sites [25; 26], not land surface variables (e.g., LAI, soil moisture). As it is very humid for scenario III (from Mid-July to Mid-August), the differences between OL and MLDAS will be small.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 6 Simulations and Results
### Datasets  
The data were gathered from two outdoor multi-hop sensors. They were the sensed data of temperature and humidity. Each vector was composed of data collected at three successive instances _10_, _11_, _12_, and each instance was constructed from two temperature measurements and two humidity measurements _T1_, _T2_ and _H1_, _H2_. After that, different type of faults (offset, gain, stuck-at, out of bounds, spike, and data loss) were induced randomly at different rates (10%, 20%, 30%, 40%, and 50%). A total of 40 datasets were prepared with a set of 9566 observations (vectors) and 12 dimensions each. The datasets were labeled with a target column marked one for normal and \(-1\) for abnormal observations.
## 4 Tunnel Propagation Characterization via S-Parameters  
As a result of the measurements that were performed in the tunnel, the complex \(S_{21}\) parameter was obtained along the tunnel. Since the results represent the characteristics of a single tunnel, the results were compared to the simulation results in order to confirm the correctness of the model. In this way, simulation results can be obtained in other unmeasured cases as well, which supplies us with a broader understanding of electromagnetic wave propagation in tunnels.  
The first result is essentially one of the basic parameters measured by the network analyzer, the power received versus frequency. The received power was plotted in Figure 7, for 5, 10, 20 and 45 m of distance between the transmitter and receiver. These distances constitute a representative sample along the tunnel. The results from the experiment were plotted with the results from the simulation, so a comparison can be performed between them.  
The received power visualizes the selective fading of the frequency that is caused by the tunnel. As is clearly noticeable, the power has peaks and dips in various frequencies, as a consequence of constructive and destructive interference. Hence, the powers received at distinctive frequencies differ from each other.  
The experiment results were plotted with the simulation results, so that a comparison can be made between them. The comparison shows a high similarity between the experiment and simulation. Among the general results obtained, there is a good match, but especially in the location of the peaks and dips there is a precise match. The precise match  
Figure 7: The frequency response of the transmission scattering parameter \(S_{21}\) for various distances.  
in the location of the peaks and dips is of high importance since they have the greatest effect on the communication performance, more than the power obtained at any specific location.