esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Consequently, it is almost impossible to use remote sensing data to obtain ET within 100 m spatial resolution with a temporal resolution of less than 1 h.  
The high temporal and spatial resolution, and large width of the GF-4 data, offers significant potential to advance the understanding of ET on a regional scale, in addition to more accurate understanding of land surface energy balances and exchanges. However, due to the lack of thermal infrared band of GF-4, we cannot obtain the land surface temperature at the coincidence time with the visible bands of GF-4. In addition, we find that because there was no remote sensing data with time resolution of minutes, the existing evapotranspiration models do not consider using these remote sensing data, and the existing evapotranspiration models are not suitable for simulating ET with time interval of minutes. Researchers have been unable to take advantage of GF-4 application in simulating ET, and most of the papers were focused on weather monitoring and land target recognition [25; 26]. Consequently, the aim of this research was to simulate ET based on GF-4, MODIS data and SEBAL (Surface Energy Balance Algorithm for Land) model, and compared the ET estimates derived from both MODIS and GF-4 imagery. The objectives are: (1) to characterize the variation at 3-min interval in ET estimated based on GF-4, MODIS and meteorological data across different land covers. (2) To compare ET estimates derived from MODIS and GF-4 captured at approximately the same time. (3) To verify MODIS and GF-4 simulated ET using field measured data.
# Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
## 3 PPP Mathematical Model  
After estimating the stochastic model using the LS-VEC method, we aim to investigate how introducing a more reliable stochastic model affects the performance of PPP using Android smartphones. PPP is an efficient technique to provide real-time precise positioning using precise satellite orbits and satellite clocks provided, e.g., by the International GNSS Service (IGS). Additional correction terms must also be considered in PPP processing. They include satellite and receiver antenna offsets, carrier phase wind-up, and site displacement effects, including solid Earth tides, ocean tide loading, and polar motion [30]. PPP can also be implemented in smartphone observations. Early smartphones only provided single-frequency and mostly GPS-only observations. In recent years, dual-frequency GNSS smartphones have also been launched. In this contribution, we focus on the noise assessment of GNSS observations from the Samsung S20 device, which is a dual-frequency smartphone supporting L5/E5a frequencies for GPS and Galileo. However, we mainly focus on the single-frequency PPP using the GPS and GLONASS observations as there are no Galileo observations as well as no E1 and E5a frequencies in our collected dataset. This approach can be extended to the dual-frequency multi-GNSS case in the future.  
This section consists of two subsections. Section 3.1 provides some explanations about the functional model (SF-PPP) used in the contribution while in Section 3.2, the PPP stochastic model used here is described.
## 4 Experiments and Results  
### Data  
We conduct experiments on the SEAME(South East Asia Mandarin-English) corpus [27], which was developed for spontaneous Mandarin-English code-switching research. We divide the SEAME corpus into three sets (train, development and test) by proportionally sampling speakers. The detailed statistics of the corpus division are presented in Table 1.
# SLMAS: A Secure and Light Weight Mutual Authentication Scheme for the Smart Wheelchair
## 4 Security Evaluation of the Proposed Methodology
### Informal Analysis
#### 4.1.9 Known Session Key Attack  
Attacker \(\mathcal{A}\) is aware of the \(SK\) for an individual session. As is well known, the hash value of the session key \(SK\) is created from random numbers and pseudonyms by the parties involved. The robust, resilient to collisions one-way hash function prevents \(\mathcal{A}\) from deriving the random integers from \(SK\). However, lacking knowledge of the most recent random values, \(\mathcal{A}\) cannot determine the correct \(SK\) for all the other sessions. The suggested approach can thus defend against known session key attacks.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure
#### 3.2.7 Distributed Storage  
For large volumes of information, the system must supply mechanisms for efficient access to the data. The infrastructure should provide distributed file systems with redundant storage mechanisms and fault tolerance. We must consider the hierarchy of storage levels offered by these systems (i.e., RAMDISK, SSD, HDD, external storage) to provide intelligent data storage techniques. Previous works [54; 55] leverage this hierarchical storage infrastructure and propose intelligent approaches to optimize data placement on modern distributed file systems, such as HDFS (Hadoop Distributed File System) used to allocate big data applications.
## 3 Second Step of One-Bounce Scattering Signal Identification Algorithm
### Identification of Scattering Signal Based on AOA  
For the \(W\) one-bounce scattering signals of scatterer S, \(3\sim W\) signals are randomly selected from \(W\) signals as a combination, so there are \(sum=C_{W}^{3}+C_{W}^{4}+\ldots+C_{W}^{W}\) combinations. For each combination, the coordinate of the scatterer \((\hat{x}_{s},\hat{y}_{s})\) can be estimated by Equation (12) or Equation (21). Thus, for the scattering signal from target scatterer S to \(i\)th BS in combination \(s\), its estimate of AOA denoted by \(\theta_{i}\), can be computed as follows  
\[\theta_{i}=\arctan\left(\frac{y_{i}-\hat{y}_{s}}{x_{i}-\hat{x}_{s}}\right) \tag{22}\]  
Suppose that the corresponding measurement of scattering signal from target scatterer S to \(i\)th BS is \(\alpha_{i}\), the scattering signal should satisfy \(\alpha_{i}=\theta_{i}\) under the condition of absence of measurement error. However, due to the presence of measurement error, \(\alpha_{i}=\theta_{i}\) is not guaranteed. For each combination of scattering signals, the sum of absolute values of the difference between \(\theta_{i}\) and \(\alpha_{i}\) can be expressed as  
\[DF=\sum\limits_{i}\lvert\theta_{i}-\alpha_{i}\rvert \tag{23}\]  
The scattering signals in the combination that minimizes \(DF\) are selected to be the one-bounce scattering signals from the same scatterer S, which can be used for the localization of scatterer S.  
Based on the aforementioned derivation, the second step of the identification algorithm for a scatterer can be summarized as Algorithm 3.  
The complete identification algorithm including the first and second steps is demonstrated in Figure 2 and is referred to as the two-step identification algorithm for short in the simulation analysis.  
```
Input:\(W\) scattering signals from scatterers S to \(W\) BSs. Output: scattering signals used for localization of scatterer S. 3\(\
eg W\) scattering signals are randomly selected to form \(sum\) scattering signal combinations. for each combination of the \(sum\) scattering signal combinations do  Compute the coordinate of the scatterer \((\hat{x}_{s},\hat{y}_{s})\). for each scattering signal of combination do  Compute \(|\theta_{i}-\alpha_{i}|\). endfor  Compute \(\sum_{i}|\theta_{i}-\alpha_{i}|\). endfor  Select the scattering signals in the combination that minimize \(\sum_{i}|\theta_{i}-\alpha_{i}|\).
```  
**Algorithm 3** Second step of identification algorithm for a scatterer
## 4 Multiplex Configuration  
The investigated DAB+ multiplex in Gdansk, a regional version of the national Polish multiplex, operates on channel 5B (176.64 MHz) and transmission mode I. Each service utilizes EEP 3-A error correction and has a coding efficiency of 1/2. The configuration of the analyzed ensemble is described in Table 6. At that time, the multiplex offered 12 services, among which 10 included audio programs (transmitting speech and/or music signals), whereas two were only data services. Additional information on transmission, frame structure, error correction coding, and related signal measurements in the DAB+ radio system may be found in [42; 43].  
As shown, the bitrate did change when it comes to some programs. In the case of the Pop Music radio channel, the bitrate raised from 112 to 128 kbps. On the other hand, in case of the Regional Polish radio channel, it was also raised, in this case from 104 to 112 kbps. It should be also mentioned that there was an additional program initially broadcasting Electronic (and later on Pop) music with a bitrate of 96 kbps. However, over time between 2018 and 2019, it was shot down.  
The profile of each radio program was obtained from the information displayed on the radio receiver. Of course, there exist numerous methods for, e.g., MGR (Music Genera Recognition). Further information on managing and labelling music datasets, audio signal feature extraction and MIR (Music Information Retrieval) is available in [44; 45; 46]. On the other hand, additional audio datasets, including speech and music content, along with links to other resources and online repositories may be found in [47; 48; 49].  
In order to evaluate and determine the quality of each radio program simulcasted in analog FM and digital DAB+ standards a subjective study has been carried out.
## 4 Conclusions  
In this paper we have analyzed the performance of different cryptographic algorithms in PDAs and we have compared it with device's basic costs: operating system, screen, and network interface. This study provides evidence to determine the amount of overhead that a security protocol can introduce in a system.  
The conclusions are the following:  
\(\bullet\) The best symmetric algorithm for ARM processors, both regarding time and energy costs, is AES.  
The throughput of AES-256 is around 25% smaller than AES-128. Both are more efficient than DES.  
Besides, AES has a greater security level than DES or 3 DES.  
\(\bullet\) Hash algorithms present a similar throughput as symmetric algorithms. MD5 is the fastest of them, although it presents some collisions and its use is not recommended.  
\(\bullet\) In public key cryptography, key generation is in general very complex and demanding. DSA presents the worst results, while ECC has very good performance.  
\(\bullet\) The results of digital signature functions are quite different depending on the algorithm. RSA-512 is the most efficient, nearly followed by DSA-512. In the next security level RSA-1204 is the lightest of all, with a performance around 75% slower than RSA-512. This difference is more pronounced in slower processors. It is also remarkable that RSA is very light especially in verifications. The average cost of signing and verifying in DSA-1024 and ECDSA-112 is quite similar than that of RSA-1024, however they have better results in signature generation. In the top security level, the best algorithm is by far ECDSA-224.  
\(\bullet\) The global costs of signing and verifying a message involve the expenses of hash operations or some compression algorithms. In this sense, it is remarkable that BB pairing based signatures are more efficient (above all in time) than the others when working with medium and large files.  
\(\bullet\) The drain on the battery sets the energy expenses of the device. The consumption of running cryptographic algorithms when the batteries are low charged is around 16% higher than when they are full.  
\(\bullet\) The use of cryptographic algorithms in network protocols, specially multi-hop protocols, introduces an important overhead due to the network interface costs during waiting times. The problem is not so in computationally cryptographic costs, but the total protocol completion time that involves notorious energy consumption. Thus, security protocols shall be designed to reduce delay times as much as possible, for example applying appropriate scheduling techniques [23].  
The main contribution of this paper is that it presents a benchmark of a wide range of algorithms and a consistent comparison between them. Results can be used to estimate the costs of network security protocols, design them appropriately and evaluate them.
# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
## 3 Results
### Planet Imagery and Cover Crop Biomass--ANOVA
No other VIs and bands were affected by CC species/mix. Hairy vetch produced the lowest biomass (4.14 Mg ha\({}^{-1}\)) from cover crop treatments (other than fallow plot) at study site 4. Biomass was similar for clover, vetch, and clover + vetch cover crop treatments. However, rye and its mix with legumes (clover and vetch) produced higher biomass. Compared to rye cover crops, rye + clover and rye + vetch produced 12.3% and 17.3% more biomass, respectively. Compared to rye, rye + clover + vetch produced 24% more biomass. On average, legume-based cover crops (clover, vetch, and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**CC** & **Seeding** & **Biomass** & **Red** & **NIR** & **NDVI** & **EVI** & **OSAVI** \\ \hline  & **Kg ha\({}^{-1}\)** & **Mg ha\({}^{-1}\)** & **\%** & **\%** & & \\ \hline
**Study site 1** & & & & & & & \\ R & 163 & 4.45 b  & 0.07 a  & 0.33 a  & 0.64 a  & 0.45 a  & 0.46 a  \\ R + C & 201 & 5.88 a  & 0.07 a  & 0.33 a  & 0.63 a  & 0.44 a  & 0.45 a  \\ F & 0 & 2.48 c  & 0.07 a  & 0.32 a  & 0.63 a  & 0.44 a  & 0.45 a  \\ \hline
**Study site 2** & & & & & & & \\ LoMix-L & 50 & 3.15 a  & 0.08 a  & 0.31 a  & 0.59 a  & 0.40 a  & 0.42 a  \\ LoMix-C & 50 & 3.58 a  & 0.08 a  & 0.30 a  & 0.57 a  & 0.39 a  & 0.40 a  \\ HiMix-LC & 135 & 2.88 a  & 0.08 a  & 0.30 a  & 0.58 a  & 0.40 a  \\ \hline
**Study site 3** & & & & & & \\ LoMix-L & 50 & 3.08 b  & 0.09 a  & 0.30 a  & 0.54 a  & 0.37 a  & 0.39 a  \\ LoMix-C & 50 & 3.30 ab  & 0.09 a  & 0.30 a  & 0.52 a  & 0.36 ab  & 0.37 a  \\ HiMix-LC & 135 & 3.50 a  & 0.09 a  & 0.29 b  & 0.52 a  & 0.35 b  & 0.37 a  \\ \hline
**Study site 4** & & & & & & \\ R & 163 & 5.36 b  & 0.09 a  & 0.33 ab  & 0.57 cd  & 0.40 bc  & 0.41 cd  \\ C & 38 & 4.19 d [FOOTNOTE:d]Footnote d:clover + vetch) produced 4.28 Mg ha\({}^{-1}\) biomass, while legume mixed with rye (rye + clover, rye + vetch and rye + clover + vetch) produced 6.32 Mg ha\({}^{-1}\) biomass. Canopy reflectance and VIs were highly correlated with biomass at study site 4, with the red, NIR, NDVI, EVI and OSAVI Pearson correlation coefficients (r) being \(-\)0.73, 0.79, 0.68, 0.70 and 0.70, respectively. Reflectance in red bands was highest in the falllow plot, followed by rye cover crops, and lowest in the plots with rye + clover + vetch where biomass was highest. Reflectance in the NIR band was opposite to red and was highest in rye +clover + vetch plots and lowest in falllow plots. Vegetation indices NDVI, EVI, and OSAVI showed a similar pattern to that of NIR, with the highest value being in rye + clover + vetch plots and lowest values in falllow followed by rye cover crop plots.
# Mid-Infrared Multispectral Gaseous Stimulated Raman Scattering Laser
## 2 Experimental Setup  
The experimental setup is shown in Figure 1. A Q-switched solid-state Nd:YAG laser (Nimma-900, Beamtech Optronics Co., Ltd., Beijing, China) at 1064 nm was used as the pump source. The laser beam divergence angle was approximately 0.5 mrad, and the beam diameter was approximately 9 mm. The pulse width was 9 ns, and the repetition rate used in this experiment was 1 Hz. The output beam passed through a half-wave plate (HWP) and a polarizing beam splitter prism (PBSP). The pump power could be adjusted continuously by rotating the HWP. Then, the linearly polarized pump laser passed through a quarter-wave plate (QWP) and turned to the circular polarization. The pump laser was focused into a Raman cell through a convex lens L1 with focal length of 2 m. The Raman cell, created from a 4 m stainless steel tube and two calcium fluoride (CaF\({}_{2}\)) windows, was filled with a mixed gas of 0.4 MPa H\({}_{2}\) and 2.0 MPa D\({}_{2}\) of 99.995% purity.  
The SRS and FWM processes occurred in the Raman cell, and a series of mid-IR Raman lasers were generated and separated by a CaF\({}_{2}\) lens L2, a CaF\({}_{2}\) prism and a slit. Photographic paper was used to locate the burn marks of Raman components with different wavelengths, as shown in the inset of Figure 1. The spectra and waveform of the output Raman lasers were detected by a monochromator (Micro HR MHRA-2A-MS, HORIBA, Beijing, China) and a photoelectric probe (PVI-2 TE-4, VIGO System, Ozarow Mazowiecki, Poland), and the laser pulse energy was detected by an energy meter (QE25 LP-S-MB-D0, GENTEC-EO, Beijing, China).
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 2 Cavitation Mechanism and Flow Dynamics in Cavitating Jets
### Self-Resonating Cavitating Jets  
[PERSON] et al. [1] found that the utilization of energy in cavitating jets can be increased by a proper choice of jet nozzle geometry. Different jet nozzle geometries were proposed by authors for specific industrial applications. The self-resonating jet, also called the self-excited pulsed jet, was first proposed and studied by [PERSON] and [PERSON].  
Using fluid dynamics principles for a structured jet, the cavitation number can be expressed using the following equation [23]:  
\[\sigma=\frac{\Gamma^{2}}{2\pi^{2}V^{2}r_{c}^{2}} \tag{4}\]  
where \(\Gamma\) is the circulation around the vortex.  
\[\Gamma=\oint v\times ds\ =V\times\lambda \tag{5}\]  
where \(\lambda\) is the acoustic wavelength.  
Therefore, enhanced cavitation and erosion can be achieved with larger \(\lambda\), for a given nozzle, liquid, and flow velocity. This can be achieved using a self-resonating jet with discrete vortex rings in its shear layer instead of several small vortices for unexcited jets [24; 25]. A self-resonating jet can be achieved passively for specific flow rate using a proper nozzle geometry and injection pressure of the liquid. A comparison between an unexcited jet and a structured jet is shown in the Figure 1.  
Figure 1: Flow comparison of impinging submerged cavitating jets [23].  
[PERSON] et al. [23; 26] developed an organ-pipe cavitating jet called \"CAVIJET\" for achieving a structured jet through a passive resonating principle (Figure 2). The nozzle was shaped such that the hydrodynamic frequency (of the vortex ring) is close to the natural acoustic frequency of the supply system. Therefore, a resonance occurs in the supply chamber that will further excite the jet. Such aero-acoustic coupling is similar to that found in rectangular cavities [27; 28; 29].  
Many experimentations and acoustic analyses of cavitation jets led to the following formula for the estimation of the organ pipe length for a maximum resonance [10; 30; 31; 32]:  
\[L=\frac{K_{N}\times d}{M\times S_{d}} \tag{6}\]  
where  
\(L\) is the organ pipe length;  
\(d\) is the nozzle outlet diameter;  
\(S_{d}\) is the critical Strouhal number;  
\(M\) is the Mach number;  
\(K_{N}\) is the model parameter.  
It was found that the optimum erosion effect is obtained from the organ-pipe nozzle for \(M\) between 0.08 and 0.09 and \(S_{d}\) is about 0.3 [30].
# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 2 Materials and Methods
### Scenarios
#### 2.2.1 Static Scenarios
For each sender node position in each scenario, the whole experiment takes 610 s, corresponding to 22 \(\times\) 10 = 220 s for each of the 433 and 868 frequencies and 17 \(\times\) 10 = 170 s for the 2.4 GHz band. The RSSI value for each of the received packets is recorded from the corresponding transceiver, as well as the PRR calculated for each power level and frequency. The results from these experiments will be presented in the following section.
## 3 Results and Discussion
### Results Analysis  
The NHLBVNet model proposed in this study is based on an improvement of XGBoost, achieving \(R^{2}>0.99\). A comparison of its prediction results with those of XGBoost is shown in Figure 6. It is evident that the XGBoost predictions of LBV have a significant error in the range of 20-60 cm/s, even exceeding 25% error. In contrast, NHLBVNet addresses this issue, providing accurate predictions within the 20-60 cm/s range, with errors generally less than 10%.  
Figure 5: Correlation heatmap of the dataset.  
\begin{table}
\begin{tabular}{l l} \hline
**Dataset** & **Proportion** \\ \hline Train Dataset & 70\% \\ Test Dataset & 15\% \\ Verification Dataset & 15\% \\ \hline \end{tabular}
\end{table}
Table 3: Details of data partitioning.  
This paper also selects MLP (Multi-Layer Perceptron), LR (Logistic Regression), XGBoost, and XGBoost+MLP as baseline models for comparison, with the results shown in Table 4. The multi-layer perceptron (MLP) is characterized as a feedforward type of artificial neural network, which encompasses an input layer, an output layer, and a series of intermediary layers known as hidden layers. The MLP compared in this paper is a basic three-layer perceptron. Due to its limited model capacity, it did not achieve good results across the three metrics. Logistic regression stands out as a prevalent linear statistical model that is often employed for a variety of tasks, and its results are better than those of MLP. XGBoost is an optimization and improvement of the Gradient Boosting Decision Tree (GBDT) algorithm and has achieved excellent results in regression tasks.  
In the experiments, XGBoost was the best-performing model among all the baseline models, achieving good results. XGBoost + MLP adds a multi-layer perceptron layer after the output of XGBoost to further learn the data distribution, and its performance is further enhanced based on XGBoost.  
In summary, the NHLBVNet model results in multi-layer improvements to XGBoost and enhances the prediction accuracy of LBV of \(\text{NH}_{3}/\text{H}_{2}/\text{Air}\). It outperforms current mainstream methods in both \(R^{2}\) and MSE as well as MAE metrics. The results indicate that the NHLBVNet model provides more accurate predictions of the LBV for ammonia-hydrogen mixtures.