esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis of Three-Dimensional Forward Modeling
### Single-Anomaly Model
#### 4.2.1 Individual Anomalies with Different Edge Lengths  
To assess the feasibility of detecting anomalies using our cross-hole grounded-wire-source TEM method, four 10 \(\Omega\cdot\)m cubic anomaly models with a common central point, located between the transmitters and receivers, were designed. The side lengths of the cubes were 5 m, 7.5 m, 10 m, and 12.5 m, with a background resistivity of 100 \(\Omega\cdot\)m, as shown in Figure 6. In the z = 0 plane, two boreholes, zk1 and zk2, were positioned at angles of \(30^{\circ}\) and \(-30^{\circ}\) along the y-axis. The grounded-wire source was placed in borehole zk1, with a length of 50 m, while the survey line was positioned in borehole zk2, with a 1 m spacing between measuring electrodes, resulting in a total of 101 points. The arrangement parameters were consistent with those used in the following models.  
For the model described above, the EM forward modeling of our configuration was performed using the 3D FE algorithm, and the simulation results are presented in Figure 7. Figure 7a-d show the multi-channel electric field curves along the zk2 direction, where solid and dashed lines represent the full-space background model and the low-resistance anomaly model. The cubic anomalies are highlighted as purple blocks, corresponding to their projected locations along the survey line. When the cube's volume was small (\(r\) = 5), the electric field curve closely matched the background. As the cube size increased (\(r\) = 7.5, 10 m, 12.5 m), the anomalies became more discernible, with the electric field exhibiting an \"S\"-shaped oscillation compared to the background. In Figure 7e-h, the solid and dashed lines represent the first-order differential results of the electric field for the full-space and low-resistance anomaly models. These differential results reveal a negative region at the anomaly positions and positive regions on either side when compared to the full-space  
Figure 6: The forward modeling grid for a single cube with side lengths of (**a**) 5 m; (**b**) 7.5 m; (**c**) 10 m; and (**d**) 12.5 m.  
model. The boundaries of the anomalous body are clearly defined by the positive and negative interfaces. As the cube volume increased, both the electric field response and the differential parameter showed larger value ranges, making the anomalies easier to detect. These results demonstrate that the cross-hole grounded-wire-source EM method was effective in detecting anomalies. Moreover, applying first-order differential processing to the electric field data enhanced anomaly detection sensitivity, allowing for the more precise identification of anomaly boundaries.
# Deep-Learning-Based Recovery of Frequency-Hopping Sequences for Anti-Jamming Applications
## 5 Conclusions  
In view of the fact that the transmitter of the frequency-hopping communication system adopts the intelligent frequency-hopping sequence, we designed a CNN-GRU network by combining a convolutional neural network (CNN) with a gated recurrent unit (GRU) for the communication receiving end, which realized the intelligent recovery of the frequency-hopping sequences. Our simulation results showed that the proposed method can estimate the frequency-hopping sequence of any length with high flexibility. With the designed method, the frequency-hopping receiving system achieved a BER performance close to that of the receiving system with known frequency-hopping patterns. In the  
Figure 10: BER of the receiving systems: (**a**) jamming 1, jamming 2, and jamming 3; (**b**) jamming 4, jamming 5, and mixed jamming.  
future, we need to consider the proposed algorithm's performance and compare it with other algorithms. At the same time, we need to design a large number of comparative experiments in the future, including the jamming effects in different scenarios and the effects of algorithm parameters.  
Conceptualization, J.Z., W.W. and Z.Z.; methodology, Z.Z. and A.W.; software, Y.X.; formal analysis, Z.Z. and K.Y.; data curation, A.W.; investigation, Y.X.; resources, J.Z.; data curation, Z.Z. and A.W.; writing--original draft preparation, A.W.; writing--review and editing, K.Y., R.L. and J.Z.; visualization, Y.X. and R.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grants U19B2016.  
Not applicable.  
The authors declare no conflict of interest.
# A Novel SINS/SRS/CNS Multi-Information Fusion Global Autonomous Navigation Method
## 3 The Multi-Information Fusion Autonomous Navigation System Based on SINS/SRS/CNS
### The Principle of Spectral Redshift Navigation
\begin{cases}v_{r1}=\left(\mathbf{v}_{p}-\mathbf{v}_{1}\right)\cdot\mathbf{u}_{1}\\ v_{r2}=\left(\mathbf{v}_{p}-\mathbf{v}_{2}\right)\cdot\mathbf{u}_{2}\\ v_{r3}=\left(\mathbf{v}_{p}-\mathbf{v}_{3}\right)\cdot\mathbf{u}_{3}\end{cases}  \tag{7}\]  
where \(\mathbf{v}_{1},\mathbf{v}_{2},\mathbf{v}_{3}\) are the velocities of reference celestial bodies in the \(i\) frame, obtained from the ephemeris, and \(\mathbf{u}_{1},\mathbf{u}_{2},\mathbf{u}_{3}\) are the unit vectors between the celestial bodies and the carrier, observed by the star sensor. By substituting Equation (7) into Equation(6), we obtain:  
\[ \begin{cases}\left(\mathbf{v}_{p}-\mathbf{v}_{1}\right)\cdot\mathbf{u}_{1}-(1+z_{1})\sqrt{ c^{2}-\left|\mathbf{v}_{p}-\mathbf{v}_{1}\right|^{2}}+c=0\\ \left(\mathbf{v}_{p}-\mathbf{v}_{2}\right)\cdot\mathbf{u}_{2}-(1+z_{2})\sqrt{c^{2}-\left| \mathbf{v}_{p}-\mathbf{v}_{2}\right|^{2}}+c=0\\ \left(\mathbf{v}_{p}-\mathbf{v}_{3}\right)\cdot\mathbf{u}_{3}-(1+z_{3})\sqrt{c^{2}-\left| \mathbf{v}_{p}-\mathbf{v}_{3}\right|^{2}}+c=0\end{cases}  \tag{8}\]  
Then, the carrier velocity in the \(i\) frame can be obtained. The core equipment of SRS is the optical spectrum instrumentation. The spectral parameters can be measured by the optical spectrum instrumentation. Then, the redshift and the carrier velocity can be calculated. The calculated principle shows that the carrier velocity is obtained by geometrical relations, not by circulative iteration. This means that its error would not accumulate over time, and the correlation time is short. Therefore, the error of SRS can be regarded as white noise, and related studies have been conducted in this way [16; 19; 20]. The System Equations of the SINS/SRS/CNS Multi-Information Fusion Autonomous Navigation System in the Middle-Low Latitudes
## Nomenclature  
IE-FF Internal and external flow fields  
IAPC Ideal air pressure curve  
N-S equations Navier-Stokes (N-S) equations  
RANS equations Reynolds-averaged Navier-Stokes equations  
PID Proportion I Integral Differential  
ILC Iterative Learning Control  
P-ILC Proportion-Iterative Learning Control  
D-ILC Differential-Iterative Learning Control  
PD-ILC Proportion Differential-Iterative Learning Control  
PID-ILC Proportion Integral Differential-Iterative Learning Control  
A-ILC Adaptive iterative learning control  
\(\rho\) Density [kg/m\({}^{3}\)]  
\(p\) Static pressure [Pa]  
\(T\) Static temperature [K]  
\(u\) Velocity [m/s]  
\(e_{0}\) Internal energy [J/kg]  
\(h\) Enthalpy [J/kg]  
\(\lambda\) Thermal conductivity [W/(m\(\cdot\)K)]  
\(\mu\) Dynamic viscosity [Pa-s]  
\(x_{i}\), \(x_{j}\) (\(i,j=1,2,3\)) Scalar components of spatial-coordinates vector  
\(u_{i}\), \(u_{j}\) Scalar components of instantaneous fluid velocity \(u\)  
\(\tau_{ij}\) Scalar components of Reynolds-stress tensor  
\(k\) Turbulent kinetic energy [J/kg = m\({}^{2}/s^{2}\)]
## Evaluation and Results
### Grid Network Formation
#### 5.4.4 Summary  
A significant improvement in energy consumption can be observed when MADCAL is implemented, especially so when sink mobility is slow. This would result in an increase in network lifetime, with the nodes closest to the sink path consuming less energy and therefore, living longer before battery power runs out. While improvements are significant at lower speeds, once the MSN speed increases improvements are less clear in terms of frame delivery. However, over all tests there are only two occurrences of frame delivery going down and not significantly. Therefore, any  
Figure 12: Sink frame reception, grid topology. Interference range 62.02 m.  
Figure 13: Sink frame reception, grid topology. Interference range 55.94 m.  
slight degradation can be offset by the benefit in energy consumption. It can also be argued that improvements in frame delivery are more difficult at faster speeds and that MADCAL is efficient in bringing the same levels of delivery to slower speeds that occur in faster speeds without the use of MADCAL. However, this highlights potential for future study with regard to optimal MSN speed. This research shows that even as speeds reach 40 mps (144 kmph), an improvement in energy consumption is possible while frame delivery remains stable.
###### Abstract  
The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.  
fuzzy logic; Q-learning; routing protocol; mobile ad hoc network (MANETs); opportunistic network +
Footnote †: journal: Electronics Electronics
## 5 Experimental Results  
In accordance with the experimental design outlined above, the training and validation performance for VTB stock from the MICEX dataset for both the state-of-the-art DeepLOB model and the proposed TFF-CL-GRU model is presented in Figures 10 and 11, respectively.  
The practicality of the TFF-CL-GRU model was also evaluated through trading simulations. The annual returns for the strategy based on our model's predictions and the buy-and-hold strategy for each of the three stocks (VTB, Sberbank, and Gazprom) are presented in Figures 12-14, respectively.  
As depicted in these charts, for all three stocks, the strategy based on our model's predictions generated median returns that were higher than those of the buy-and-hold strategy and in the positive zone, except for Sberbank. The negative returns for Sberbank stock can be explained by the fact that this stock was predominantly in a declining trend, and short sales were prohibited. For the same reason, it would not make sense to create trading simulations for Apple, Amazon, and Google stocks, considering they were all in a downward trend.  
However, the VTB stock exhibited a very strong growth trend, with some days experiencing price increases exceeding 2%, resulting in abnormally high annual returns. This consistent growth trend likely simplified the task of predicting the direction and contributed to better model performance in terms of F1 score, accuracy, precision, and recall for this stock compared to the other two. It is important to note that stocks tend to exhibit mean reversion, and a period of rapid growth will likely be followed by a significant decline, leading to more modest annual returns in practice.  
Another observation is that the outcomes of the buy-and-hold strategy were much more volatile. The real-world stock market factors previously discussed, such as the bid-ask spread, liquidity limitations, etc., could have also negatively impacted the strategy returns in the simulations.  
Figure 10: DeepLOB training and validation results for VTB stock: (**a**) Categorical cross-entropy loss; (**b**) Accuracy; (**c**) F1 score; (**d**) Precision; (**e**) Recall.  
Figure 11: TFF-CL-GRU training and validation results for VTB stock: **(a)** Categorical cross-entropy loss; **(b)** Accuracy; **(c)** F1 score; **(d)** Precision; **(e)** Recall.  
Figure 12: Trading simulation results for VTB.
## 3 Results
### Effect of Collection Angle on Electron Intensity in Amorphous Ice at 3 MeV  
As shown in Figures 2-4, the electrons collected within 0-10 mrad were retained closer to the center of the electron trajectories. Therefore, smaller collection angles were investigated. As the collection angle decreased from 0-10 mrad to 0-5 mrad and 0-1 mrad, the beam width decreased from 27.9 nm to 21.0 nm and 16.8 nm (Figure 6A). Meanwhile, the intensity of collected electrons decreased from 41.9% to 22.4% and 4.6%, respectively (Figure 6B). To reduce the beam size while maintaining a certain signal, 0-5 mrad might be better than 0-15 mrad and 0-1 mrad.  
Figure 4: Electron beam profile in amorphous ice at incident electron energies of 3 MeV at a depth of 5 \(\upmu\)m. (**A**) Trajectories of 10,000 electrons for depths up to 5 \(\upmu\)m. (**B**) Trajectories of 6708 electrons collected within 0-10 mrad. (**C**) Beam radius R\({}_{0.68}\) as a function of sample depth. (**D,E**) The calculated transverse beam profiles of 10,000 electrons and 6708 electrons collected within 0–10 mrad for depths up to 5 \(\upmu\)m. (**F**) Electrons scattered in ice as a function of depth, including single elastic, plural elastic, and inelastic scattering. The dimensional values in (**A,B**) and (**D,E**) are in nm.
## 3 Theory and Methods
### TOC and Element Geochemical Analysis  
To determine the TOC content, the sample must first be ground to 200 mesh, then loaded into a ceramic crucible. The sample is rinsed with 5% dilute hydrochloric acid to remove inorganic carbon. After drying, a combustion aid and iron filings are added to the crucible, which is then burned in an instrument at 1100 \({}^{\circ}\)C to obtain the TOC value. The instrument used for TOC measurement in this study was the Leco-CS744 Carbon Sulfur Analyzer (LECO, Laboratory Equipment Corporation, San Jose, CA, USA).  
For major element determination, the selected samples were first ground to 200 mesh and then dried at 105 \({}^{\circ}\)C for 3 h. After drying, the samples were cooled to room temperature in a desiccator. Subsequently, 0.70 g of each sample was mixed with 7.00 g of lithium borate (Li\({}_{2}\)B\({}_{2}\)O\({}_{7}\)) flux and fused in a furnace to form glass discs. Major element concentrations were measured using a PANalytical PW2424 X-ray fluorescence (XRF) spectrometer (PANalytical B.V., Almelo, The Netherlands) with an analytical error of less than 5%. For trace element analysis, 0.1 g of the dried sample was first subjected to a desalting treatment. Then, 40 mg of the treated sample was digested in a sealed vessel using a 1:10 mixture of HF and HNO\({}_{3}\) at 180 \({}^{\circ}\)C for 24 h. After digestion, the solution was evaporated to near dryness, and the residue was redissolved and diluted with 5% HCl. Trace element concentrations were analyzed using an iCAP Q inductively coupled plasma mass spectrometer (ICP-MS) (Thermo Fisher Scientific, Waltham, MA, USA) with an analytical error also below 5%.
# Revolutionizing Soccer Injury Management: Predicting Muscle Injury Recovery Time Using ML
## 2 Materials and Methods
### Feature Processing  
From the general features group shown in Table 1, parameters P4 (place of injury) and P5 (body side) are discarded, along with recovery feature P17. The features P1 (date of an examination) and P19 (medical notes) were not provided by the club to save the players' identities. However, the injury duration, needed for this study, was calculated by subtracting the last date of examination (player is considered fit) and the first date (start of an injury). The injury-specific features P12 and P13 were also discarded because these parameters are not relevant for muscle injuries. The rest of the injury-specific parameters are preserved with some additional processing. Initial muscle injury classification (P6) distribution is shown in Figure 2. To standardize the values, all contusions (C) are mapped to values 0, while values 1A and 1B are mapped to 1, values 2A and 2B to 2, 3A to 3 and, finally, 3B to 4. This mapping scheme corresponds to the injury severity grade.  
Another important feature was the position of the injury according to the muscle (P7). To account for the possibility of a single injury affecting multiple muscle groups, the available choices--proximal, abdominal and distal muscles--were one-hot encoded. In this context, 'abdominal' refers exclusively to injuries affecting the abdominal wall region. To streamline the analysis, the possible outcomes for the depth feature (P8) were combined into a single feature. This consolidated feature contained values ranging from 0 to 3, corresponding to no information, superficial fascia, middle muscle and deep fascia, respectively.  
Body position features (P9) also played a significant role in our analysis, providing information about the affected body region. To simplify this, the features were grouped into five categories: hamstring, quadriceps, adductors and abductors, calf and abdominal wall. As with parameter P7, it is possible for an injury to affect multiple muscle groups. Therefore, each category was one-hot encoded to accommodate such cases.  
Clinical examination features, such as swelling, tone, crepitation and elasticity (P10-P13), contained values ranging from 0 to 3, describing the level of each. Pain ratings of palpation, contraction, and stretching (P14-P16) were described as either painful or not painful. Finally, recovery features were considered, such as the expected recovery duration (P18), which was converted into days for easy comparison.  
Figure 1: Distribution of muscle injuries based on the required recovery time. The vertical dashed line indicates the cut-off threshold of 35 days (5 weeks). Injuries with longer recovery times were excluded from the analysis.
## 2 Materials and Methods
### Statistical Analysis and Regression Models  
The Mann-Whitney-Wilcoxon non-parametric test was used to check the differences in the features of images of tomatoes stored for different lengths of time, applying the Python scipy.stats library ([[https://docs.scipy.org/doc/scipy/tutorial/stats.html](https://docs.scipy.org/doc/scipy/tutorial/stats.html)]([https://docs.scipy.org/doc/scipy/tutorial/stats.html](https://docs.scipy.org/doc/scipy/tutorial/stats.html))) accessed on 11 December 2024.  
In addition, SPSS (IBM SPSS Statistics, version 29.0, Chicago, IL, USA) was used to analyze the results of ATR-FTIR spectroscopy and physicochemical measurements in tomato samples and of spectrophotometric assays in phenolic extracts. The analysis was conducted at a significance level of \(p<0.05\), via one-way ANOVA and post hoc analysis.  
Furthermore, regression analysis was conducted using image-derived features, employing the Random Forest Regressor to predict the storage duration of tomatoes and the Gradient Boosting Regressor to estimate physicochemical properties such as moisture, firmness, and antioxidant/antiradical activity. To develop the regression models, 70% of the dataset was randomly allocated for training, while the remaining 30% was used for validation. Various feature combinations were tested, with a maximum of six features (representing 10% of the dataset size) included in each model. The approach ensured that the model with the highest predictive accuracy, evaluated through the R\({}^{2}\) score, was selected.
## 4 Results
### Proposed Supplementary Adaptive Droop Controller
#### 4.2.2 Quadratic Droop with ADP  
The quadratic function in Equation (3) was used for designing adaptive droop using ADP. The neural network was trained and tested in similar manner as in the linear droop case. Figure 13 shows the voltage profile using quadratic droop APC method with ADP. The maximum voltage is in marginal boundary of 1.058 p.u. (\(V_{cr12}\)). The power curtailment for houses 11 and 12 increased slightly but decreased for houses 7 to 10. The total energy loss due to the PV curtailment has reduced to 12.98 kWh using the quadratic droop-based control with ADP.
## 3 Spelling Correction
### Error Correction  
After detecting a spelling error in a text, the correction process consists of generating a correction candidates list and ranking them. The construction of this ranked list usually invokes some measure of probabilistic estimate between the incorrect sequence and the possible candidates [68; 69].  
In this context, the most studied algorithms for spelling correction are: (i) rule-based techniques [73], for which the candidate generation process consists of applying all the rules applicable to an incorrect sequence and keeping all valid words; (ii) similarity keys [74], which maps all character strings to a key, in which strings with similar spellings have similar keys; (iii) edit distance [75], which considers the three editing operations (insertion, exclusion, substitution) for one string to be converted into the other, and then to rank candidates based on the minimum number of operations performed; (iv) \(N\)-gram with probabilistic techniques (language model) [76; 77], represent that a given character will be followed by another, in which they can be estimated by collecting \(N\)-gram frequency statistics in a large text corpus [69]; and finally, (v) neural networks [78], the most recent approach.