esa-sceva/satcom-chunk-collection huggingface dataset:
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
###### Abstract  
Simulating shallow water flows in large scale river-lake systems is important but challenging because huge computer resources and time are needed. This paper aimed to propose a simple and efficient 1D-2D coupled model for simulating these flows. The newly developed lattice Boltzmann (LB) method was adopted to simulate 1D and 2D flows, because of its easy implementation, intrinsic parallelism, and high accuracy. The coupling strategy of the 1D-2D interfaces was implemented at the mesoscopic level, in which the unknown distribution functions at the coupling interfaces were calculated by the known distribution functions and the primitive variables from the adjacent 1D and 2D lattice nodes. To verify the numerical accuracy and stability, numerical tests, including dam-break flow and surge waves in the tailrace canal of a hydropower station, were simulated by the proposed model. The results agreed well with both analytical solutions and commercial software results, and second-order convergence was verified. The application of the proposed model in simulating the surge wave propagation and reflection phenomena in a reservoir of a run-of-river hydropower station indicated that it had a huge advantage in simulating flows in large-scale river-lake systems.  
shallow water flows; 1D-2D coupled model; lattice Boltzmann method; large-scale simulation; river-lake systems +
Footnote †: journal: Applied Sciences
## 2 Establishment of EPS Performance Evaluation System
### Establishment of Multi-Level Evaluation Index System  
The process of evaluating the working performance of an EPS is a complex and fuzzy process involving multi-factor coupling and multi-level response. The working characteristics of an EPS and its utilization efficiency are influenced by various factors from different aspects. Therefore, to clearly define the evaluation level of the working performance of an EPS, it is necessary to carefully divide the working performance and infer the performance level of the target layer EPS through the performance status of each influencing factor.  
In conclusion, this paper refers to the performance requirements in the \"Basic Requirements for Automotive Steering Systems\" (GB 17675-2021) [9] and \"Technical Conditions for the Safe Operation of Motor Vehicles\" (GB 7258-2017) [10] standards, comprehensively considers various factors that affect the overall working characteristics and utilization efficiency of an EPS, selects various indicators to evaluate the EPS based on artificial testing, non-destructive testing, and other monitoring behaviors, and establishes a multi-level evaluation system for the EPS based on the principles described above, as shown in Figure 1.  
In Figure 1, the comment layer, target layer, factor layer, and state layer together constitute the system used to evaluate the performance of the EPS. The comment layer (EXCELLENT \(V_{1}\), GOOD \(V_{2}\), FAIR \(V_{3}\), POOR \(V_{4}\)) represents the quality of the EPS performance. Evaluating and classifying its working characteristics and utilization efficiency can provide a basis for the maintenance measures undertaken by technicians and clarify the maintenance priorities.  
The target layer is the ultimate goal of the system, which is an EPS performance evaluation (\(U\)). The factor layer categorizes the performance states of the EPS during operation and includes five characteristics. Steering portability (\(A\)) refers to the ability to turn the steering wheel lightly. Steering returnability (\(B\)) refers to the ability of the vehicle to automatically return to the straight direction after turning. The steering wheel middle area performance (\(C\)) refers to the performance of the steering wheel in the middle position. Steering wheel vibration (\(D\)) indicates the presence of vibration or abnormal steering wheel movement. Assisting characteristics (\(E\)) refer to the support of auxiliary systems for steering, such as electric power steering systems.  
The state layer further analyzes the factor layer. For example, the ease of steering may be influenced by certain factors that lead to changes; these include the steering wheel diameter (\(A_{1}\)), the maximum steering force of the steering wheel (\(A_{2}\)), the steering wheel maximum steering (\(A_{3}\)), vehicle speed (\(A_{4}\)), and the overall vehicle mass (\(A_{5}\)). This establishes a multi-level evaluation system for EPS performance.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 2 MQTT-SN Protocol  
The MQTT-SN [17] protocol is a connectivity protocol based on MQTT [5], which operates at the application layer. MQTT is a topic-based protocol, which makes it possible to create a publish/subscribe-based topology in which each device can subscribe to or publish information about a topic. Thus, there is a broker that manages the topics and the connected devices in the network. This topology is very useful for the IoT, as it allows the transmission of information generated by sensors to central nodes.  
MQTT-SN was designed to be lightweight (even more than MQTT [5]) and works with wireless communications. This means that it is characterized by link failures, a short message size, low bandwidth, and low overheads, among other features. MQTT-SN was designed to be similar to MQTT, but there are a few differences. Firstly, MQTT-SN includes _TopicId_, which replaces the topic name in MQTT. _TopicId_ is a 16-bit integer, which acts as the topic name, with the _REGISTER_ command being able to negotiate the mapping between _TopicId_ and the topic name. Secondly, MQTT-SN provides a sleeping client mechanism. This feature enables clients to shut themselves down and save power for a while. MQTT-SN allows us to update or delete the _will_ message, which allows devices to notify other clients about an ungracefully disconnected client. Finally, another important aspect is that MQTT-SN works over the User Datagram Protocol (UDP) [20].  
UDP, which operates at the transport layer, introduces an 8-byte header. 6 LoWPAN, on the other hand, has header compression mechanisms (in the best case, it can use 4-byte headers), and it also has a Maximum Transmission Unit (MTU) of 127 bytes, which is really small compared to other protocols; for example, IPv6 offers an MTU of 12,800 bytes [21]. Payload sizes will be conditioned by the MTU. In our implementation of the different attacks on a 6 LoWPAN network, the maximum payload of 6 LoWPAN is 38 bytes, and consequently, the UDP payload is 30 bytes. It is important to mention that MQTT-SN operates over UDP, but does not need to operate over 6 LoWPAN. The study and implementation of the attacks described in this analysis focused exclusively on MQTT-SN, so the complete study of the protocols of other layers is outside the focus of this work. In Figure 2, which depictsan MQTT-SN-based network, we can observe how one operates. The publication on a topic is represented with an arrow of the same color as that topic. The grey rectangles symbolize MQTT-SN clients, and the colors inside them represent the topics to which they are subscribed. This scheme allows us to easily understand MQTT and MQTT-SN. The client with _Id_ 1 is subscribed to topics 1, 2, and 3; the client whose _Id_ is equal to 2 is subscribed to topics 1 and 2; finally, the client whose _Id_ is equal to 3 is subscribed to topics 1 and 3. If the client with _Id_ number 2 publishes on topic 3, this message is received by clients 1 and 3. The same happens with topic 2 is published by client 2.
## 3 Results
### Psychometric Testing of Final FGAGR
#### 3.2.3 Validity  
In regards to the concurrent validity, the pre-final FGA\({}_{\text{GR}}\) scale presented a strong correlation with the mini-BESTest (rs = 0.813, \(p\) < 0.001), the BBS (rs = 0.748, \(p\) < 0.001), and a moderate association with the TUG (rs = 0.606, \(p\) < 0.05).  
The convergent validity was presented as low with an expected negative association with the FES-I (rs = 0.365, \(p\) > 0.05).  
The scale also presented a discriminant ability to classify individuals who had high concern about falls (sixteen participants, FES-I mean total score 21.8 \(\pm\) 3.7) from those with low concern (eight participants, FES-I mean total score 24.5 \(\pm\) 2.9) (t\({}_{\text{(22)}}\) = 1.783, \(p\) = 0.04).  
The ROC curve results were obtained from 22 participants (19 with risk for future falls and 3 without risk for falls). The ROC curve for the FGA\({}_{\text{GR}}\) showed an AUC of 0.921 (95% CI 0.80-1.00; SEM = 0.063, \(p\) = 0.022), indicating the ability of the scale to identify individuals at risk for future falls (Figure 1).  
Figure 1: Receiver operating curve (ROC) for identifying participants with risk for falls using the scores from the Greek functional gait assessment scale based on the “Timed Up and Go” (TUG) test (scores \(\geq\) 11 s indicated an increased risk for falls).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Item of FGA\({}_{\text{GR}}\)**} & \multicolumn{2}{c}{**Single Items Agreement**} & \multirow{2}{*}{**Item/Total Correlation**} \\ \cline{2-2} \cline{4-4}  & **Inter-Rater** & & **Test-Retest** \\ \hline
1 & 0.879 * & 0.818 * & 0.275 \\
2 & 0.875 * & 0.871 * & 0.490 * \\
3 & 0.903 * & 0.950 * & 0.485 * \\
4 & 0.943 * & 1.000 * & 0.732 * \\
5 & 0.833 * & 0.667 * & 0.575 * \\
6 & 0.632 * & 0.514 * & 0.339 \\
7 & 0.703 * & 0.652 * & 0.619 * \\
8 & 0.936 * & 0.749 * & 0.524 * \\
9 & 0.849 * & 1.000 * & 0.561 * \\
10 & 1.000 * & 0.941 * & 0.375 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Single items agreement between raters and repeated measures as this was measured via the weighted kappa coefficient from n = 24 older adults.  
The cutoff score that balances between high sensitivity and high specificity for the FGA\({}_{\text{GR}}\) was \(\leq\)25/30 which resulted in Sn of 84% and Sp of 100%. Taking this cutoff point into consideration, the FGA\({}_{\text{GR}}\) was able to identify 16/19 individuals with risk for falls and all 3 without risk for falls. All predictive values for the FGA\({}_{\text{GR}}\) are listed in detail in Table 4.
# Radiation-Induced Structural Changes of _Miscanthus_ Biomass
## 2 Materials and Methods
### Structural Properties of the Irradiated Miscanthus
#### 2.4.2 X-Ray Diffraction Analysis (XRD)  
The crystalline phase of irradiated _Miscanthus_ cellulose was demonstrated by a D/max 2500 diffractometer (Rigaku Corporation, Tokyo, Japan). The conditions were: acceleration voltage 40 kW and current 30 mA. The Cu-K\(\alpha\) wavelength was 1.54 A, grade range 2\(\theta\) ranged from 10\({}^{\circ}\) to 40\({}^{\circ}\) with the step size of 0.002\({}^{\circ}\). The crystallinity index (_CrI_) of cellulose was calculated using Equation (1) [18]  
\[CrI\left(\%\right)=\frac{I_{002}-I_{am}}{I_{002}}\times 100 \tag{1}\]  
where _CrI_ is the crystallinity index, \(I_{002}\) is for the highest peak intensity at 2\(\theta\) = 22.6\({}^{\circ}\), and \(I_{\mathrm{am}}\) is the intensity diffraction for amorphous cellulose at 2\(\theta\) = 16.0\({}^{\circ}\).
## 2 Materials and Methods
### Irradiation Experiments  
Irradiation experiments under simulated sunlight were carried out using a solar simulator (Solar Box 1500e, CO.FO.ME.GRA, Milano, Italy) set at a power factor 250 W m\({}^{-2}\) (equipped with a UV outdoor filter of soda-lime glass IR-treated, and with a BST temperature sensor), and under natural sunlight. The incident power of natural sunlight (Pavia, 45\({}^{\circ}\)11\({}^{\prime}\) N, 9\({}^{\circ}\)09\({}^{\prime}\) E, June-July 2017, 12.00 a.m.-3.00 p.m., 27-30 \({}^{\circ}\)C) was measured by means of an HD 9221 (Delta OHM, 450-950 nm) and a Multimeter (CO.FO.ME.GRA, 295-400 nm) pyranometers and resulted in the range 400-501 W m\({}^{-2}\) (Vis) and 22-34 W m\({}^{-2}\) (UV). The experiments were performed in a closed glass container containing 100 mL of untreated tap or river water samples (depth 40 mm, exposed surface 9500 mm\({}^{2}\)) at native pH, enriched with 50 \(\upmu\)g L\({}^{-1}\) of each drug, separately dissolved and magnetically stirred. Aliquots (0.5 mL) of each sample were withdrawn at planned times, filtered, and injected in HPLC-ESI-MS/MS system (multiple reaction monitoring mode, MRM) to follow the degradation profiles.  
Each kinetic experiment was performed in triplicate, and the degradation kinetic constant (\(k_{\text{deg}}\)) was calculated by using dedicated software (Fig P application, Fig P Software Corporation, version 2.2a, BIOSOFT, Cambridge, UK).  
Before irradiation, each suspension (50 \(\upmu\)g L\({}^{-1}\) of each GC, 0.5 g L\({}^{-1}\) TiO\({}_{2}\)) was magnetically stirred in the dark for 20 min to promote the drug adsorption on the catalyst surface. For each GC, a control solution with no catalyst was also measured. No changes in GCs concentration were detected in the control samples.  
To investigate matrix effects, a set of experiments, as described above, were carried out with the addition of salts (SO4\({}^{2}\) - 50 mg L\({}^{-1}\), NO3\({}^{-}\) 20 mg L\({}^{-1}\)) and humic acids (HAs, 10 and 5 mg L\({}^{-1}\)).  
For identifying the photoproducts, 100 mL tap water samples were spiked with 10 mg L\({}^{-1}\) of each GC (separately dissolved) and irradiated under simulated solar light as described above. Aliquots (0.5 mL), treated as above, were injected into the HPLC-UV system before performing the HPLC-ESI-MS/MS analysis (in full scan, zoom scan, and MS/MS mode).
# Fusion of Land-Based and Satellite-Based Localization Using Constrained Weighted Least Squares
## 2 Measurement Models  
### Localization Scenario  
We assume a total of \(N_{1}+N_{2}\) stations, including \(N_{1}\) land-based stations and \(N_{2}\) satellites. These stations are used to locate the source on the Earth's surface. The longitude and latitude of the source are denoted as \(\omega_{w,1}\) and \(\omega_{u,2}\), respectively. The station is represented by \(O_{1},O_{2},\ldots,O_{N_{1}},O_{N_{1}+1},\ldots,O_{N_{1}+N_{2}}\), where \(O_{1},O_{2},\ldots,O_{N_{1}}\) represents the land-based stations and \(O_{N_{1}+1},\ldots,O_{N_{1}+N_{2}}\) represents satellites. The longitude and latitude of the \(j\)th station are \(\omega_{j,1}\) and \(\omega_{j,2}\), \(1\leq j\leq N_{1}+N_{2}\), respectively. There is a ground station for satellites, with a longitude and latitude of \(\omega_{t,1}\) and \(\omega_{t,2}\), respectively. Based on the Earth ellipsoid model, (1) provides the transformation relationship between the geodetic coordinate system \(\left[\omega_{1},\omega_{2},H\right]^{\mathrm{T}}\) (\(\omega_{1}\) and \(\omega_{2}\) denote the longitude and latitude, respectively, and \(H\) is the altitude) and the space rectangular coordinate system [19]:  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\begin{bmatrix}(\alpha+H)cos(\omega_{2})cos(\omega_{1})\\ (\alpha+H)cos(\omega_{2})sin(\omega_{1})\\ \big{(}\big{(}1-e^{2}\big{)}+H\big{)}sin(\omega_{2})\end{bmatrix} \tag{1}\]  
\(\alpha=R_{e}/\sqrt{1-\left(esin(\omega_{1})\right)^{2}}\), where \(R_{e}=6378.137\) km is the radius of the Earth's equator and \(e=\sqrt{1-R_{p}^{2}/R_{e}^{2}}\) denotes the eccentricity. \(R_{p}=6356.752\) km is the polar radius of the Earth. Therefore, based on (1), the position vector of the land-based station and satellite can be \(s_{j}^{o}=\left[s_{j,x^{\prime}}^{o}s_{j,y^{\prime}}^{o}s_{j,z}^{o}\right]^{ \mathrm{T}}\). The source position vector is represented as \(\mathbf{u}=\big{[}u_{x},u_{y},u_{z}\big{]}^{\mathrm{T}}\), and the ground station position vector is \(\mathbf{t}=\big{[}t_{x},t_{y},t_{z}\big{]}^{\mathrm{T}}\). Since the source is located on the Earth surface, \(\mathbf{u}\) satisfies the following equation:  
\[\mathbf{u}^{\mathrm{T}}\mathbf{\Lambda}_{1}\mathbf{u}-R_{e}^{2}=0 \tag{2}\]  
where \(\mathbf{\Lambda}_{1}=diag\{1,1,1/(1-e^{2})\}\).
## 2 Materials and Methods
### Retrieval Methodology
#### 2.2.2 The Singular Vectors in Forward Model  
In the SIF retrieval process within the FLs band using data-driven methods, the construction of the forward model typically involves utilizing the singular vectors derived from a set of spectra devoid of SIF emissions. For the GOSAT satellite, bare soil areas can be distinguished from surfaces such as vegetation, water, clouds, and snow by using the ratio of apparent reflectance from three FTS bands [13; 23]. Building upon this, we further refined the selection of SIF-free spectra using MODIS GPP products [24], thus extending the temporal coverage of the SIF-free spectrum dataset to encompass the entire year. Specifically, GPP products within an 8-day interval were projected and gridded at a resolution of 0.2\({}^{\circ}\). Regions with GPP approximately equal to zero were selected to represent bare soil areas, ensuring observations devoid of Fs. Additionally, SIF-free spectra were acquired exclusively from high-gain mode, with spectral radiance constrained within the range of [1.5-8]\(\cdot\)10\({}^{-7}\) W\({}^{-1}\cdot\)m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)cm\({}^{-1}\), and scenes with solar zenith angle (SZA) greater than 70\({}^{\circ}\) were excluded to reduce uncertainty. The non-SIF spectral collection also includes observations over snowy areas to increase the range of radiance in the spectral set and to add slope characteristics similar to those of satellite spectra over vegetation canopies.  
We selected the top 5 singular vectors corresponding to the largest singular values from the SIF-free spectrum dataset for constructing the forward model. Among these vectors, the first one encompasses the primary spectral shape information, the second one is used to fit the intensity difference on either side of the Fraunhofer lines caused by sensor discretization, the third one primarily fits the spectral slope, the fourth one reflects the instrumental line shape (ILS) widening the spectral bands, and the fifth one is mainly employed to fit the higher variability in atmospheric absorption bands. To validate the effectiveness of this set of singular vectors, we exhaustively tested all combinations involving the top 10 singular vectors for SIF retrieval. The results indicate that employing the first 5 singular vectors yields the highest correlation with the Enhanced Vegetation Index (EVI) and maintains the lowest zero-level offset.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 4 Discussion
### Strengths and Limitations of the Automatic Extraction Method Used
Considering that the morphology of cadastral boundaries is complex [7], compared to physical boundaries, such as boundaries of roads or rivers, delineation of cadastral boundaries cannot be fully automated at this time, and additionally, the verification of the results has to be done with the participation of landowners and other land rights holders. The limitations on extracting only visible object boundaries lie in the fact that not all visible boundaries (land cover boundaries) represent cadastral boundaries (land right boundaries). For instance, when two agricultural cadastral units leased to the same farmer are farmed as one unit, and vice versa. However, visible object boundaries which coincide with the land right boundaries can be automatically detected and used in cadastral applications. In addition, the UAV-based spatial data acquisition is usually affected by special operational regulations that restrict the use of this technology, in particular in urban areas [18].
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 3 Experiment
From these results, it was inferred that the peak at \(t_{1}\) represented signals reflecting at the transmission line transition of the BSL. The antenna is suitable for the phase calibration method used in this study because it allows us to calculate the distance from the antenna's reflection coefficients to the phase origin. The phase calibration was performed using the measured reflection coefficients. Python was used to calculate the calibration data. The discrete inverse Fourier transform, and the discrete Fourier transform were calculated using the ifft and the fft functions from the Numpy library. Kaiser-Bessel functions were computed using the Kaiser function in the signal module of the Scipy library.
# A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal
## 5 Conclusions  
In this paper, three primary ideas are proposed. First, a DnCNN-based method for extracting highly structured chirp signals in low SNR conditions is proposed. It takes advantage of deep learning feature extraction ability to recover the noiseless chirp signals. Second, a TCE is proposed, we directly use the IF of denoised signal to pre-estimate chirp parameters, which not only have a considerable accuracy but also contribute to improving the performance of ML. Finally, by resorting to the local search method, a non-iterative local searching estimator based on ML is proposed. Simulation results show that the proposed LsML outperforms several methods in terms of parameter estimation accuracy and efficiency in low SNR scenarios.  
Figure 10: lnRMSE of initial frequency and chirp rate estimation versus \(\sigma\). (**a**) is lnRMSE of initial frequency, (**b**) is lnRMSE of chirp rake.  
**Author Contributions:** [PERSON] wrote the draft; [PERSON]. ([PERSON]) and [PERSON] gave professional guidance and edited; [PERSON] and [PERSON]. ([PERSON]) gave advice and edited. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Dual Quaternion Embeddings for Link Prediction
## 5 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**FB15k**} & \multicolumn{6}{c}{**WN18**} \\ \hline
**Model** & **MR** & **MRR** & **Hit@1** & **Hit@3** & **Hit@10** & **MR** & **MRR** & **Hit@1** & **Hit@3** & **Hit@10** \\ \hline TransE \(\clubsuit\) & - & 0.463 & 0.297 & 0.578 & 0.749 & - & 0.495 & 0.113 & 0.888 & 0.943 \\ ComplEx & - & 0.692 & 0.599 & 0.759 & 0.840 & - & 0.941 & 0.936 & 0.945 & 0.947 \\ HolE & - & 0.524 & 0.402 & 0.613 & 0.739 & - & 0.938 & 0.930 & 0.945 & 0.949 \\ TorusE & - & 0.733 & 0.674 & 0.771 & 0.832 & - & 0.619 & 0.943 & 0.950 & 0.954 \\ SimplE & - & 0.727 & 0.660 & 0.773 & 0.838 & - & 0.942 & 0.939 & 0.944 & 0.947 \\ RotatE \(\clubsuit\) & 40 & **0.797** & **0.746** & **0.830** & **0.884** & 309 & **0.949** & **0.944** & 0.952 & 0.959 \\ DihEdral & - & 0.733 & 0.641 & 0.803 & 0.877 & - & 0.946 & 0.942 & 0.948 & 0.952 \\ QuatE \(\clubsuit\) & 41 & 0.770 & 0.700 & 0.821 & 0.878 & 388 & **0.949** & 0.941 & **0.954** & 0.960 \\ \hline DualQuatE-1 & **31** & 0.751 & 0.659 & 0.825 & **0.884** & 241 & 0.947 & 0.939 & 0.952 & 0.959 \\ DualQuatE-2 & 50 & 0.766 & 0.696 & 0.818 & 0.877 & 220 & 0.948 & 0.942 & 0.953 & **0.961** \\ DualQuatE & **35** & 0.754 & 0.664 & 0.827 & **0.884** & **183** & **0.949** & 0.943 & 0.952 & 0.960 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Link prediction on datasets FB15k and WN18. \(\clubsuit\) represents the results that came from ([PERSON] et al., 2019) and the others came from the original papers. \(\clubsuit\) denotes the results of QuatE without type constraints from the paper. The results with bold are the best and the underlined ones are the second best results.