esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Preparation
#### 2.2.3 Auxiliary Data  
The temporal distributions of surface water from 1984 to 2019 at 30 m resolution are available from the datasets of the JRC Yearly Water Classification History v1.2 [65]. These datasets were generated using more than 4.1 million Landsat satellite images (TM, ETM+, and OLI sensors' images) between 15 March 1984 and 31 December 2019, in the Google Earth Engine. The overall producer's accuracy and the user's accuracy are 98.8% and 99.8%, respectively [65]. Thus, these datasets (Figure 1) could be used to calculate the backscattering threshold interval of surface water areas in our study.  
The DEM data are available from the NASA SRTM V3 digital elevation products, derived from the NASA Jet Propulsion Laboratory (JPL). The spatial resolution of these DEM products is 30 m. Before sharing, this product was filled with the invalid pixels using the open products of GMTED2010, ASTER GDEM2, and NED, to ensure the spatial continuity of the elevation [63]. Thus, this dataset could be directly used to analyze or calculate slopes using Google Earth Engine.  
The rice map for our study area in 2017 was acquired from the International Rice Research Institute (IRRI), derived using MODIS multispectral data with the moderate spatial resolution of 250 m and 500 m, high temporal resolution, and large study area coverage [14; 22]. A new algorithm of phenology- and pixel-based paddy mapping (PPPM) was developed, then successfully applied to China and India, and the MODIS-based rice paddy maps were obtained with a high accuracy [10; 54]. This data was used for intercomparison with our paddy extraction results.
# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 2 Methodology  
### Study Area  
Two carefully selected Australian locations, Perth and Brisbane, as described in Table 1, were identified for this research study. These stations were chosen due to their advantageous position along an East-West axis. A timeframe of 24 h, starting on 1 January 2024, as illustrated in Figure 1, was considered. This timeframe proved to be instrumental in conducting radar detection simulations and monitoring trajectories, which was an initial essential component of the study. A thorough assessment was carried out to evaluate the connectivity between LEO satellites and the Australian ground stations. This evaluation aimed to determine the visibility of the ground stations from the satellites' perspective. This analysis aids in pinpointing the satellites that are within the line of sight of the ground stations at any given moment.  
Figure 1: One-month projection for Globalstar satellite with orbit data including altitude (km), eccentricity, right ascension (deg), inclination (deg), argument of perigee (deg), and Date/Time on \(X\)-axis. Source: CelesTrak.
## 3 Experimental Results and Analysis  
### Droplet Size Parameter Analysis  
Table 2 show the droplet size parameters of the circular-hole rotating core micro-fog nozzle under different Ps and different Ss. It can be seen from the droplet size parameters of the atomizing nozzle in Table 1 that when the D was increased, the droplet characteristic particle size and the average particle size were reduced. D\({}_{[3,2]}\) and D\({}_{[4,3]}\) represent the Sauter mean diameter (SMD) and the volume-weighted mean diameter, respectively; D\({}_{50}\) is the characteristic particle size, representing a particle volume of less than 50% of the total volume of all the particles. The units are in \(\upmu\)m.  
For example, when the S was 30 cm, the P was 1 MPa, and the D was increased from 0.5 mm to 1.1 mm, the D\({}_{[3,2]}\) was reduced from 516.1 \(\upmu\)m to 274.6 \(\upmu\)m, and the D\({}_{50}\) was reduced from 681.3 \(\upmu\)m to 672.5 \(\upmu\)m; when the S increased, the D\({}_{50}\) and D\({}_{[3,2]}\) also decreased with the increase of the axial distance. For example, when the D was 0.5 mm, the P was 1 MPa, and the S increased from 30 cm to 70 cm, the D\({}_{[3,2]}\) decreased from 516.1 \(\upmu\)m to 87.5 \(\upmu\)m, and the D\({}_{50}\) decreased from 681.3 \(\upmu\)m to 110.2 \(\upmu\)m; both the D\({}_{50}\) and D\({}_{[3,2]}\) decreased with the increasing P. For example, when the D was 30 cm, the S was 0.5 mm, and the P increased from 1 MPa to 3 MPa, the D\({}_{[3,2]}\) decreased from 516.1 \(\upmu\)m to 208.1 \(\upmu\)m, and the D\({}_{50}\) decreased from 681.3 \(\upmu\)m to 674.6 \(\upmu\)m. Thus, it can be seen the influence of the S on the nozzle droplet size was much greater than the other two factors. In addition, when the S was 50 cm, both the D\({}_{50}\) and D\({}_{[3,2]}\) decreased sharply, whether by increasing the D or the P, and the decrease was particularly pronounced when the D was increased. To obtain liquid droplet size with better dust removal efficiency, that was, D\({}_{[3,2]}\) < 90 \(\upmu\)m, the pressure of a typical pressure atomizing nozzle needs to be adjusted to above 6 MPa. In contrast, the D\({}_{[3,2]}\) was less than 90 \(\upmu\)m when the circular-hole rotating core micro-fog nozzle was 3 MPa, which can be applied to some places with low water pressure.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Serial Number** & **S (cm)** & **D (mm)** & **P (MPa)** \\ \hline
1 & 30 & 0.5 & 1 \\
2 & 50 & 0.7 & 2 \\
3 & 70 & 1.1 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Factors and levels in the orthogonal experimental.
## 2 Materials and Methods
### Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS)  
The multivariate processing of the analytical data recorded during a chemical degradation allows the consideration of all the species involved in the experiments. In this work, the chemometric multivariate curve resolution (MCR) method was adopted, based on an alternating least squares algorithm (MCR-ALS). The application of MCR to a degradation process allows the collection of information on all the species involved, such as their spectrum and concentration along the process. In this work, since the degradation was followed by spectrophotometry, a data matrix **D** (_u,m_) was arranged, in which the \(n\) samples (or individual spectra) represented the time evolution of the kinetic process, and \(m\) was the absorption values at the recorded wavelengths. MCR decomposes the matrix **D** in a bilinear model consisting of two smaller matrices, **C** and **S**T, and can be considered an extension of the Lambert-Beer's law to multiwavelength and multisample analysis:  
\[\textbf{D}=\textbf{C}\textbf{S}^{\text{T}}+\textbf{E} \tag{1}\]  
where **C** (_n,k_) describes the concentration evolution of the \(k\) components in the chemical system; **S**T (_k,m_) contains the pure spectra of \(k\) components; and **E** (_n,m_) is the variance unexplained in the modeling.  
At every ALS iteration, non-negativity, unimodality, and concentration-closure constraints were considered to achieve the final solutions with a chemical meaning, and to reduce the possible intensity or rotation ambiguity. Kinetic constraint was also applied using a hard/soft-MCR routine (HS-MCR-ALS), which allowed in-depth analysis of photodegradation kinetics and evaluation of the reaction-rate constants. The figures of merit used to describe the achievement of the results and the quality of the model were the percentage of lack of fit (%LOF) and the explained variance (%R\({}^{2}\)):  
\[\%LOF=100\times\sqrt{\frac{\sum_{ij}\left(d_{ij}-d_{ij}^{*}\right)^{2}}{\sum _{ij}d_{ij}^{2}}} \tag{2}\]  
\[\%R^{2}=100\times\frac{\sum_{ij}d_{ij}^{2*}}{\sum_{ij}d_{ij}^{2}} \tag{3}\]  
Detailed information on the mathematical principles underlying the MCR technique can be found in various articles [24; 25; 26].
## 1 Introduction
However, the uneven distributions of Ti and Al at the interfaces leave the accurate measurements of the thickness of the interfacial diffusion layer incomplete. Therefore, the diffusion kinetics and life prediction were also not studied. Afterwards, [PERSON] et al. designed a Ti-Mo mixture as the barrier layer for the \(n\)-type Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) matrix and found that the dispersion of the chemically stable Mo particles (\(\leq\)15 at %) disturbs the inter-diffusion between the Ti and Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) matrix, which results in an improved interfacial stability at 550 \({}^{\circ}\)C. Still, the diffusion kinetics and life prediction were not studied [31]. Besides, [PERSON] et al. studied the stability of the power generation characteristics of the Mo-Cu/Ti/SKD joints in terms of vibration and thermal cycling, and found that Ti helps to achieve good interfacial bonding [32]. In this paper, to develop an \(n\)-type SKD TE joint that is qualified for practical applications, we designed and fabricated \(n\)-type SKD joints with the structure of Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\). In this structure, Ti is reserved as the bonding layer for its active bonding behavior with common electrode materials including Cu and Ni. Furthermore, Ti is also expected to buffer the interfacial thermal stress. To suppress the activity of Ti at a high temperature and also barrier the inter-diffusion between TE materials and the electrode, Mo is chosen as the barrier layer. Both Mo and Ti were deposited onto the Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) surface by magnetron sputtering to achieve flat interfaces. Because of the evident difference between the CTEs of Mo and Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\), the thickness of the Mo layer was controlled to no more than 4.5 \(\upmu\)m to decrease the thermal stress at high temperatures. To reveal the interfacial diffusion kinetics of the Ti/Mo/Yb\({}_{0.3}\)Co\({}_{4}\)Sb\({}_{12}\) joints and predict their service life, the above \(n\)-type SKD TE joints were thermal shocked between 0 \({}^{\circ}\)C and 600 \({}^{\circ}\)C and isothermally aged between 550 \({}^{\circ}\)C and 650 \({}^{\circ}\)C. After each thermal shocking or isothermal aging step, the interfacial microstructures and the interfacial contact resistivity were both investigated. Based on the measurement results, the diffusion kinetics were investigated and the service life was predicted at different temperatures.
## 5 2-Chain Based Routing
### Protocol Operations
#### 5.2.2 Chains Interconnection and Data Transmission  
In order to find a global optimum path, every node compares the distance from its parent node with the distance from its nearest neighbor in the other chains. If the respective nearest neighbor is closer than the parent node, then node transmits data to the respective neighbor node. It also updates its parent nodes on the basis of this comparison. We can say that any node \(i\) selects a parent node \(p_{i}\) in the other chain, if the nearest node \(k_{i}^{j}\) in its chain is farther than the node \(\bar{k_{i}^{j}}\) in the other chain. In the last phase of the scheme, all the nodes transmit their data towards their parent nodes which forward it to the sink.
# Analytical Model of an Energy Detector for Ultra-Wideband Chaotic Communications
## 2 Energy Detector Model
### Detection Probability  
Knowing the optimal threshold value, we obtained the following analytical solution for the receiver probability error in the case of ultra-wideband chaotic radio pulse with a large processing gain \(B\gg 1\):  
\[P_{err}\ =\ p(0)Q\bigg{(}\frac{X_{T}-\overline{E_{0}}}{\sqrt{2n\sigma_{noise}^{2}} }\bigg{)}+p(1)\bigg{(}1-Q\bigg{(}\frac{X_{T}-\overline{E_{1}}}{\sqrt{2n\sigma_{ noise}^{2}(1+\gamma)}}\bigg{)}\bigg{)}, \tag{14}\]  
Figure 4: BER as a function of SNR and processing gain \(B\). Solid lines correspond to simulation; dotted curves to analytical estimates (15).  
Figure 3: Threshold \(Y_{T}\) as a function of SNR for various values of the processing gain \(B\).  
where \(Q(x)\ =\ \frac{1}{\sqrt{2\pi}}\int_{x}^{\infty}{\rm e}^{-t^{2}/2}dt\) is the complementary CDF (cumulative distribution function) of the normal distribution.  
Using the previously introduced \(Y_{T}\ =\ X_{T}/\overline{E_{0}}\) and employing the symmetry of \(w_{1}(x)\), we get the final expression for BER:  
\[P_{err}\ =\ p(0)Q\Bigg{(}\sqrt{\frac{B}{2}}(Y_{T}-1)\Bigg{)}+p(1)Q\Bigg{(}\sqrt{ \frac{B}{2}}\bigg{(}1-\frac{Y_{T}}{1+\gamma}\bigg{)}\Bigg{)}. \tag{15}\]  
False alarm probability \(P_{FA}\) can be defined as the probability of detecting symbol \"1\" instead of actually transmitted symbol \"0\". Then, this probability is:  
\[P_{FA}\ =\ p(0)\int_{X_{T}}^{\infty}w_{0}(x)dx\ =\ p(0)Q\Bigg{(}\sqrt{\frac{B}{2}}(Y_{T}-1) \Bigg{)}. \tag{16}\]
## 3 Data and Methods
### Numerical Simulation Software and Steps  
This study employs GeoStudio software to analyze the stability of dump site slopes, which are characterized by their complex geometric configurations, mixed distribution of multi-layered soil and rock materials, and significant seepage effects. Slope stability issues become particularly pronounced under the influence of external factors such as rainfall and groundwater fluctuations, as well as internal factors like stacking loads and geostress redistribution. GeoStudio integrates multiple modules and combines the Limit Equilibrium Method (LEM) with the Finite Element Method (FEM), making it highly suitable for addressing such complex slope stability problems.
## 3 Background
### EAT Token Structure and Encoding  
A significant portion of the standardization efforts surrounding attestation tokens has focused on defining the basic structure of the token container, along with establishing interoperable claims and verification mechanisms. A token is generally composed of three main components: the header, the payload, and the signature. Listing 1 depicts a sample attestation token formatted according to COSE specification.  
The payload is wrapped inside a container that can be a COSE_Sign1 or COSE_Encrypt object. The protected header contains metadata such as the algorithm used for signing the token or other contextual information such as the reference to the public key. The unprotected header is a component of the message structure that contains metadata not subject to integrity protection such as negotiation parameters or context identifiers. The payload contains a list of claims which contains device attributes such as the device ID or security state.  
The claims in the payload can be categorized as follows:  
* Standardized claims, where there is widespread agreement on how the information should be structured;
* Claims that require standardization to ensure interoperability;
* Claims that can be defined by individual parties according to their specific needs.  
Standardized claims, such as those found in the COSE header, are included in an algorithm's registry and are publicly available. Each claim set is associated with a fixed integer key ID (label) that has a clearly defined function. For example, in the COSE Header registry [36], Integer Label 1 corresponds to the algorithm used. Additionally, the values for these algorithms are specified by the standard.  
The claims that are not standardized are assigned private-usage IANA registry keys, such as values less than \(-\)65,536. If the token includes more complex structures, such as JSON, the object can be serialized into the token format, where the claim labels can have a byte string format.  
```
<< { / protected/<<{ /alg/1:-7/ECDSA256/ }>>, / unprotected/{ / kid/-75000:h'41737964665747269634543445341323536' }, / payload/<<{ / boot_seed/-75004:h'3d4 ffc935d4 df450 cb...', / challenge/-75008:h'2c50207544b0...', / client_id/-75001:-1, / hardware_id/-75005:'04080401000007', / implementation_id/-75003:h'7b37279c6 db435063...', / instance_id/-75009:h'00506a3491d39 ca57a4d9b9...', / security_lifecycle/-75002:'SL_NON_PSA_ROT_DEBUG' }} >>, / signature/-75010:h'5e7a1f2a89b1f39a...'} >>
```  
Listing 1: A sample Entity Attestation Token and its format.  
The entire COSE container, including the protected header, unprotected header, payload, and signature, is serialized using CBOR. CBOR uses different major types for encoding, such as integers, byte strings, text strings, arrays, and maps. In COSE, the headers and payload are typically encoded as maps, with keys and values using various CBOR types like integers or strings.
## 4 Results and Discussion  
This paper utilizes wave propagation, LSM, and analytical methods to investigate the vibration response of the shield tunnel lining with a partly circumferential crack. The shield tunnel lining material is concrete, with \(R=2.85\) m, \(L/R=40\), \(h=0.3\) m, \(\mu=0.2\), \(\rho=2500\) kg/m\({}^{3}\), and \(E=34.5\) GPa. The vibration response analysis of the shield tunnel lining with a partly circumferential crack has three components. The first segment explores the natural frequencies of shield tunnel lining. It describes the depth and location influence of the partly circumferential crack on these frequencies. The following part extracts the displacement response of the shield tunnel lining with a partly circumferential crack, revealing the displacement variation law at different positions and directions. Section 5 establishes different point load positions, observing the displacement changes across different positions and directions.
# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 4 Experiments
### Baselines and Evaluation Metrics  
WERECE is a type of unsupervised learning paradigm for domain-specific term extraction. Its utility was tested by taking five well-known unsupervised learning algorithms as baselines: TF-IDF, TextRank, K-means, isolation forest, and one-class support vector machine (SVM). Both TF-IDF and TextRank served as a heuristic to determine candidate concepts' domain relevance; these have been widely used for educational concept extraction [17; 19; 29]. Different from these two methods, we fit seed concepts to the K-means, one-class SVM, or isolation forest approaches to promote novelty detection. The identified novel terms or phrases could then be deemed educational concepts as suggested in recent studies from multiple fields [29; 58; 59; 60]. All baselines were trained and tested with our experimental datasets.  
WERECE, against the baselines, was evaluated according to precision, recall, and F1 scores, as has been done elsewhere [29; 58; 61]. Precision represents the ratio of correctly classified domain concepts to all words or phrases that a method holds as domain concepts. Recall is the ratio of correctly classified domain concepts to all domain concepts in a dataset. The F1 score denotes the harmonic tradeoff between precision and recall. These metrics are respectively computed as follows:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{8}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{9}\]  
\[\text{F1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}, \tag{10}\]  
where TP (i.e., true positive) is the number of domain concepts identified correctly; FP (i.e., false positive) is the number of non-target concepts incorrectly classified as domain concepts; and FN (i.e., false negative) is the number of domain concepts misclassified as non-target concepts.  
All methods and evaluation metrics were implemented in Python. In addition to constructing a text preprocessing pipeline, the Chinese NLP toolkit Jieba was used to implement the TF-IDF method. Scikit-learn, a machine learning package in Python, was employed to establish the K-means, one-class SVM, and isolation forest models. The evaluation metrics were developed in scikit-learn as well.
## 6 Experiments and Results
### The Result of the Optimized Sampling for Energy Efficient Data Collection  
Figure 9: The optimized node sampling method compared with random sampling method. (**a**) The network lifetime of the two sampling methods with different sampling rates in the simulated network; (**b**) The network lifetime of the two sampling methods with different sampling rates in the real network; (**c**) The data recovery error of the two sampling methods with different sampling rates in the simulated network; (**d**) The data recovery error of the two sampling methods with different sampling rates in the real network.  
To evaluate performance of the optimized node sampling method, we implement data collection in the whole network lifetime with different node sampling rate. The results are compared with the random sampling method, as shown in Figure 9a for the simulated network and Figure 9b for the real network, respectively. It is shown that the optimized node sampling method has better performance than the random sampling method for both the simulated and real networks. Especially in the lower sampling rate, the optimized node sampling method could obtain better network energy distribution and prolong the network lifetime. For example, when the sampling rate is lower than 30%, the network lifetime of the optimized method will be prolonged two times than the network lifetime of the random method.  
The data recovery errors are also computed for the two node sampling methods and the results are reported in Figure 9c,d. In the simulated network or the real network, the optimized node sampling method almost has the same performance to the ideal random sampling method which is considered the best way to get good recovery results. From these, we can conclude that the optimized method can not only extend the network lifetime but also keep the accuracy of the data recovery. This is due to the balanced model concerning the node uniform sampling and the energy distribution of network in Section 5.
## 3 Results
### Method Validation  
We verified the rationality and validity of the detection method through a simulation. The simulation signal included the master station and the western secondary station. The SNR of the master station is \(-5\text{ dB}\), and the signal amplitude ratio of the master station to that of the west secondary is \(-9.5\text{ dB}\). The proposed method was used to detect signals from the master station. Figure 5 shows the generated Alpha navigation signal waveform and spectrum diagram.  
Figure 5 shows that although the time-domain waveform has been completely submerged in the noise, the spectrogram clearly shows the transmission signal of the two stations in different time periods. Subsequently, bandpass filtering was performed on the waveform and notch to extract the three frequency signals.  
Figure 6 shows that the signal is more pronounced after applying the bandpass filter and that the noise is well suppressed. However, because the signal from the secondary station is relatively strong, the expected signal from the master station remains difficult to distinguish. Hence, we used three notch filters to extract the three frequency signals, and we used an orthogonal correlation to detect the master station signal.  
Figure 7 shows the signals extracted by the notch filters and the correlation results. The three frequency signals were separated by notch filters, and obvious correlation peaks were obtained via the orthogonal correlation method. Since the signal extracted by the notch filter is a continuous wave signal of three frequencies, multiple values will exist in the detection, resulting in increasing detection errors. In addition to not aliasing, the orthogonal correlation of different frequency signals has a unique correlation peak. According to the threshold calculation method and the detection conditions of the master station described in Section 2.2.3, the positions of the three frequency signals of the master station can be accurately detected, thereby proving the effectiveness of the proposed method.  
Figure 5: Alpha navigation signal. (**a**) Time-domain waveform. (**b**) Spectrogram.  
Figure 6: Signal after band-pass filtering. (**a**) Signal waveform. (**b**) Signal spectrogram.