esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Mathematical Modeling and the Control of Attitude Dynamics
### Linearized Model of Attitude Dynamics  
The linearized model for the angular velocities and attitude of the octocopter was obtained by keeping the vehicle in the hovering state and assuming decoupled attitude angles. To obtain the model, the forces and torques created by gyroscopic and gravity effects and aerodynamic effects owing to airflow were neglected. Only the input torques generated by the rotors alone was considered [5; 6; 7]. By considering the dynamics of the angular velocities and their rates of change, Equation (19) for the attitude dynamics was determined as follows [14; 15; 16; 17; 18; 19; 20; 21; 22; 23]:  
\[\left[\begin{array}{c}\dot{\phi}\\ \dot{\theta}\\ \ddot{\psi}\end{array}\right]=\left[\begin{array}{ccc}1&\sin\phi\tan\theta& \cos\phi\tan\theta\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi\sec\theta&\cos\phi\sec\theta\end{array}\right]\left[\begin{array}[] {c}p\\ q\\ r\end{array}\right], \tag{19}\]  
where \"\(\phi\)\" is the roll angle, \"\(\theta\)\" is the pitch angle, \"\(\Psi\)\" is the yaw angle, \"\(p\)\" is the body roll rate, \"\(q\)\" is the body pitch rate, and \"\(r\)\" is the yaw rate. However, in the hovering state, \(\phi=\theta=0\). Therefore, Equation (19) becomes,  
\[\left[\begin{array}{c}\dot{\phi}\\ \dot{\theta}\\ \ddot{\Psi}\end{array}\right]=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}p\\ q\\ r\end{array}\right];\;\text{and} \tag{20}\] \[\left[\begin{array}{c}\dot{\phi}\\ \dot{\theta}\\ \dot{\Psi}\end{array}\right]=\left[\begin{array}{c}p\\ q\\ r\end{array}\right]=\left[\begin{array}{c}\omega_{x}\\ \omega_{y}\\ \omega_{z}\end{array}\right],\]  
Figure 7: Illustration of body frame.  
where \(\omega_{x},\omega_{y}\) and \(\omega_{z}\) are the vehicle's angular velocities in the roll, pitch, and yaw axes, respectively. Moreover, the relation between the angular velocity \"\(\omega\)\" of the vehicle and the input torque \"\(\star\)\" is given as follows:  
\[\left[\begin{array}{c}\tau_{x}\\ \tau_{y}\\ \tau_{z}\end{array}\right]=\left[\begin{array}{ccc}I_{xx}&0&0\\ 0&I_{qq}&0\\ 0&0&I_{zz}\end{array}\right]\left[\begin{array}{c}\omega_{x}\\ \omega_{y}\\ \omega_{z}\end{array}\right], \tag{21}\]  
where \(\tau_{x},\ \tau_{y}\), and \(\tau_{z}\) are the input torques from the rotor for the roll, pitch, and yaw axes, respectively; and \(I_{xx},\ I_{yy}\), and \(I_{zz}\) are the moments of inertia of the octocopter about the roll, pitch, and yaw axes, respectively.  
From Equation (20), we can write:  
\[\left[\begin{array}{c}\dot{\omega}_{x}\\ \dot{\omega}_{y}\\ \dot{\omega}_{z}\end{array}\right]=\left[\begin{array}{c}\ddot{\phi}\\ \ddot{\theta}\\ \ddot{\Psi}\end{array}\right]. \tag{22}\]  
Equations (20)-(22) can be summarized as follows:  
\[\left.\begin{array}{c}\dot{\phi}=p=\omega_{x}\\ \dot{\theta}=q=\omega_{y}\\ \dot{\omega}=r=\omega_{z}\\ \ddot{\phi}=\frac{\tau_{x}}{I_{xx}}\\ \ddot{\theta}=\frac{\tau_{y}}{I_{yy}}\\ \ddot{\Psi}=\frac{\tau_{z}}{I_{zz}}\end{array}\right\}. \tag{23}\]
## Results
### Periodest Stability Measurement  
The intra-examiner agreement of the Periotest assessment was presented in Figure 5, showing a mean bias of 0.17 \(\pm\) 0.5 (95% CI: \(-\)2.914 to 3.258).  
The Periotest measurement showed lower stability of narrow thread implants (B-D), compared to the wide diameter design (A-C) (\(p\) < 0.01) (Figure 4C, Table 5). The increasing diameter produced significantly higher implant stability levels in both screw types (\(p\) < 0.01) (Table 6).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Periodest** & **Mean Diff.** & **95.00\% CI of Diff.** & **Adjusted \(p\) Value** \\ \hline CRYSTAL 3.8 vs. CRYSTAL 4.8 & \(-\)1.534 & \(-\)2.771 to \(-\)0.2966 & 0.0085 \\ \hline CRYSTAL 3.8 vs. ELISIR 3.8 & 0.5813 & \(-\)0.6559 to 1.818 & 0.6632 \\ \hline CRYSTAL 3.8 vs. ELISIR 4.8 & 4.239 & 3.002 to 5.476 & \(<\)0.0001 \\ \hline CRYSTAL 4.8 vs. ELISIR 3.8 & 2.124 & 0.8866 to 3.361 & 0.0001 \\ \hline CRYSTAL 4.8 vs. ELISIR 4.8 & -1.534 & -2.771 to \(-\)0.2966 & 0.0085 \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANOVA post-hoc of Periotest comparisons for the study groups.  
Figure 5: Detail of the Bland–Altman plot of the Periotest assessment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Periodest** & **Mean** & **SD** \\ \hline CRYSTAL 3.8 & 4.3 & 1.1 \\ \hline CRYSTAL 4.8 & 5.1 & 1.4 \\ \hline ELISIR 3.8 & 4.1 & 1.6 \\ \hline ELISIR 4.8 & 2.0 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the Periotest means for the study groups.  
Moreover, an increased micromovement was detected for the two-stage implants of group C and group D, compared to the lower means of the monolithic implants of group A and group B (\(p\) < 0.01).
## 2 Related Work
[52] improved the YOLOv3 model, while [PERSON] et al. [54] improved the YOLOv3 Tiny model. [PERSON] et al. [53] developed the YOLO-Tomato, based on the YOLOv3 model, to detect tomato under complex environmental conditions and [PERSON] [56] proposed fusing those same models with different activation functions to detect unripe and ripe tomatoes. The great results obtained by these authors \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Task** & **DL Model** & **No. of Ripeness Classes** & **Results** & **Authors** \\ \hline \multirow{3}{*}{Detection} & SSD MobileNet, SSD Inception, & \multirow{3}{*}{2 classes} & F1-Score 66.15\% & \multirow{3}{*}{[PERSON]\%} \\  & SSD ResNet, SSD ResNet 101 & & (SSD MobileNet v2) & \\  & and YOLOv4 Tiny & & & \\  & YOLOv4, SSD Inception v2, & & & \\  & SSDMobileNet v2 and SSD & & & \\  & ResNet 50 & & & \\  & SSD VGG16, SSD MobileNet & 3 classes & Average Precision 98.85\% & \multirow{3}{*}{[PERSON] et al. [28]} \\  & and SSD Inception V2 & & & (SSD Inception v2) & \\  & Improved YOLOv3 & N/D & F1-Score 94.18\% & \multirow{3}{*}{[PERSON] et al. [51]} \\  & Improved YOLOv3 & 1 class & Mean Average & \multirow{3}{*}{[PERSON] et al. [52]} \\  & & Precision 76.90\% & & \\  & YOLO-Tomato & 1 class & F1-Score 93.91\% & \multirow{3}{*}{[PERSON] et al. [53]} \\  & Improved YOLOv3 Tiny & 1 class & F1-Score 91.92\% & \multirow{3}{*}{[PERSON] et al. [54]} \\  & YOLOv3, YOLOv3 Tiny, & 2 classes & F1-Score 66\% & \\  & YOLOv4, and YOLOv4 Tiny & & (YOLOv4) & \\  & Modified YOLO-Tomato models & 2 classes & F1-Score 97.90\% & \\  & & & (YOLO-Tomato-C) & \\  & Faster-RCNN, PPN & \multirow{3}{*}{3 classes} & Mean Average & \multirow{3}{*}{[PERSON] et al. [57]} \\  & SSD MobileNet v2, RetinaNet, & & & (RetinaNet) \\  & SSD Inception v2, YOLOv3 & & Average Accuracy & \\ Classification & CCN and YOLO model & 3 classes & 94.67\% (YOLO) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of different papers regarding tomato detection and classification through Deep Learning (DL) one-stage detection models. (N/D = Not Described). show that improving and specifying the state-of-the-art DL models for a particular task can be a great advantage and lead to substantially better results.
## 4 Simulation Results and Discussion
The initial positions of the second group of UAVs are (1000, 1000), (1500, 2000), and (3000, 2000), namely Position 2. The initial positions of the third group of UAVs are (1500, 1500), (1500, 3000), and (3000, 1500), namely Position 3. It can be seen that the UAVs can avoid the no-fly zone even if they take off at the edge of the no-fly zone. The performance advantage benefits what we set as the position penalty in the reward function, which can guide the agent to control the UAVs flying in the target area. Figure 7 shows the comparison of the multi-UAV trajectories based on the SU-MAPPO algorithm with different SNR thresholds of data collection. It can be seen that the UAV trajectories were closer to the buoys with the growth of the SNR threshold. This is because the designed reward function is mainly related to the transmission rate and the time reward \(K^{*}\). With the SNR thresholds increasing, the UAV needs to be close enough to the buoy to  
Figure 5: UAV trajectories with different schemes. Figure 6: UAV trajectories with different initial positions of taking off. achieve the minimum transmission rate requirement. The agent chooses to let the UAV be closer to the buoy to enhance the data collection transmission rate, which contributes to shortening the data collection time and obtaining a larger value of \(K^{*}\). Figure 8 shows the total mission completion time of the SU-MAHPO algorithm and the SU-MAPPO algorithm with different SNR thresholds of data collection. Figures 9 and 10 show the total mission completion time of the SU-MAHPO algorithm and the SU-MAPPO algorithm with different channel conditions. It can be seen that the performance of our proposed algorithm is significantly better than the SU-MAPPO algorithm. The reason is that the discretized action space makes the SU-MAPPO algorithm unable to explore and select the optimal action in the considered environment, which further verifies the advantage of the proposed algorithm in the continuous action space.
# Performance Evaluation of GPS Auto-Surveying Techniques
## 4 Discussion

Table 4: Mean error for the DGPS receiver with base station position averaged after a set number of hours.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 4 Experimental Validation
### Model Testing
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Training Datasets** \\ **(Number of Training** \\ **Samples**)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Testing Datasets** \\ **(Number of Test** \\ **Samples**)** \\ \end{tabular} } & \multicolumn{2}{c}{**Classification Accuracy (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **Average** \\ **Result** \\ **(BF)** \\ \end{tabular} } \\  & & **Inner Race** & & & & \\
**Full** & **Full** & **Full** & **Full** & **Normal** & & \\
**Full** & **Full** & **Full** & **(IRF)** & **(ORF)** & & \\ \hline Dataset 1 (15) & Dataset 2, 3, 4 (527) & 90.13 & 92.28 & 92.83 & 99.48 & 93.33 \\ Dataset 2 (15) & Dataset 1, 3, 4 (490) & 90.89 & 92.44 & 92.06 & 99.61 & 93.75 \\ Dataset 3 (15) & Dataset 1, 2, 4 (492) & 92.80 & 88.91 & 94.20 & 98.53 & 91.84 \\ Dataset 4 (15) & Dataset 1, 2, 3 (492) & 93.34 & 86.50 & 96.20 & 99.28 & 93.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Initial diagnostic performance of the proposed method in different scenarios. Figure 9: Final accuracy vs. initial accuracy at different rotational speeds. distinguish between different types of faults under variable operating conditions. The MTC-GAN model shows strong robustness and adaptability under these conditions.
## 4 A Distributed Mapping Service  
The distributed construction of accurate and efficient visualisations of sense data is an open problem. This section presents a solution in which groups of network nodes cooperate to produce local maps that are cached and merged at the sink node, producing a low-resolution map of the global network. The sink node receives periodic map updates from each cluster head used to refine an up-to-date global map. The global map gives a low cost interface that can be used to target queries for generating detailed maps from a subset of sensors in the network. In an energy constrained network, it is preferable to allow the application to control the level of detail and topology coverage of a generated map.  
Solving global network data mapping problems through localised and distributed computation is achieved by abstracting and simplifying the routing and the Map Generation services and turning them into services of the network rather than being the result of coordinating the services of individual nodes. This scheme changes the mechanism of use of certain capabilities in WSNs; the Map Generation service is used by sensor nodes to deal with continuous representation of sense data and to localise map computation; while cluster heads are selected as the caches for local maps.  
Figure 1 shows the distributed mapping service architecture and interactions between its component. It is made up of four layers:  
1. The _Network Services Layer_: This layer hosts the services of the network. In this work, the mapping service exploits the Map Generation and the routing services. However, this layer may contain other services such as, power management and self-healing.
2. The _In-network Processing_ layer: The role of this layer is to process raw data received from various cluster heads. It applies filtering on all the received data to reduce redundancy resulting from overlapping cluster coverage. Moreover, the _In-network Processing_ module manages incremental update messages and merges them into a single transaction. Also, it defines interfaces for the _Network Services Layer_ and the _API for Standardised High-level Services_ layers. It also provides access to the cached data in a suitable format.
3. The _API for Standardised High-level Services_: This layer provides the essential components and interfaces that facilitate the development and deployment of end-user mapping applications, e.g. isopleth or terrain navigation applications. This is a simple layer that does not include the more complex features and capabilities required for building a middleware.
4. The _User Applications_ layer: This layer hosts the user mapping applications.  
Figure 1: The architecture of the distributed mapping service.
## 3 Energy and Information Beamforming
### Strongest-Path Beamforming Scheme
#### 3.2.1. Average Useful Signal Power Maximization
Note that for the given altitude of AP, the uplink achievable rate region of the sensor can be predicted based on (29) or the analytical SINR expression presented in Section 4. Moreover, the performance metric of the network, e.g., system spectral efficiency (SE), can be estimated using the sensor density information. With these in the hand, the altitude optimization can be formulated as a network performance maximization problem subject to the sensor quality of service (QoS) constraint, e.g., a threshold on the uplink achievable rate.
## 4 Results and Discussion
### DFT Calculations  
In Table 3, the values of the Gibbs free energy, as calculated from DFT, of the nine possible reactions of citric acid esterification or anhydride formation are presented. As can be seen, seven out of the nine reactions have \(\Delta\)G values in the order of magnitude of 10-100 kJ/mol, one reaction has a \(\Delta\)G more than 100 kJ/mol, and one reaction has a very low (as an absolute value) \(\Delta\)G value close to 1 kJ/mol. This reaction is the reaction that  
Figure 4: (**a**) FTIR spectra of the raw and the recrystallized samples and their subtracted spectra (recrystallized-raw) and (**b**) photograph of the recrystallized sample after solvent evaporation showing a film poor formation.  
Figure 5: (**a**) FTIR spectra of the raw and the freeze-dried samples and their subtracted spectra (freeze dried-raw) and (**b**) FTIR of the raw, recrystallized and freeze-dried samples.  
leads to the formation of a dimer through esterification between the OH group at position 3 of one molecule and the COOH group at position 3 of another citric acid molecule. Thus, the most probable product is the E33 dim. This is in agreement with the interpretations of the experimental results for the formation of oligomers and the increase in molecular weight. Also, despite the fact that the \(\Delta\)G values, as calculated from DFT, refer to gas-phase reactions, the very low value of \(\Delta\)G implies and may explain the easiness of the occurrence of this reaction. This is also in agreement with the conclusions of the previous sections, where it was claimed that the esterification reaction takes place even at room temperature. In addition to the E33 dim product, other probable products are mainly cyclic anhydrides, e.g., A13 and A15. These products may be formed during the decomposition at 150 \({}^{\circ}\)C or to a lower extent at lower temperatures.
# The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration
## 3 Experiments and Results
### Ablation Experiment of Improved YOLOv8 Detection Model  
To further validate the effectiveness of the improved models, ablation experiments were conducted. The experiments compared the performance of models a, b, c, and d. Model a represents the YOLOv8 baseline algorithm. Model b incorporates TripletAttention into the backbone network of the baseline algorithm. Model c replaces the Concat structure in the neck network of the baseline algorithm with the AFM structure. Model d combines the additions of TripletAttention in the backbone network and the replacement of Concat with AFM in the neck network of the baseline algorithm. Performance evaluation metrics include Precision (P), Recall (R), mean Average Precision at IoU = 0.5 (mAP50), and mean Average Precision from IoU = 0.5 to 0.95 (mAP50-95). Detailed results of the ablation experiments are shown in the Table 5 below:  
The results indicate that incorporating attention mechanisms into the backbone network and replacing the Concat structure in the neck network with AFM structure improved the detection model compared to the baseline model by 2.4% in Precision (P), 2.1% in Recall (R), 1% in mAP50, and 1.3% in mAP50-95. Additionally, the number of parameters increased from 3,005,843 to 3,110,319. The specific training process is shown in Figure 7.  
The comparison shows that the improved model can reduce instances of missed detections and decrease the frequency of false positives, especially in scenarios with more occlusions and complex environments. Detailed experimental results validating this outcome are provided in Figures 8-10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Method** & **P** & **R** & **mAP50** & **mAP50-95** & **Parameters** \\ \hline a & YOLOv8 (baseline) & 0.945 & 0.878 & 0.947 & 0.789 & 3,005,843 \\ b & YOLOv8+TripletAttention & 0.966 & 0.883 & 0.956 & 0.804 & 3,006,143 \\ c & YOLOv8+AFM & 0.957 & 0.884 & 0.956 & 0.800 & 3,110,019 \\ d & YOLOv8+AFM+TripletAttention & 0.969 & 0.899 & 0.957 & 0.802 & 3,110,319 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiments.  
Figure 7: Training plot of improved YOLOv8 detection model.
# Matching of Water Breakthroughs in a Low-Resistivity Oil Reservoir Using Permeability Anisotropy
## 4 Results and Discussion
### Petrophysical Property Modelling  
Reservoir modelling begins with the classification and zonation of the reservoir, structural construction and petrophysical models, followed by a 3D static model and mapping of porosity, permeability, and water saturation [34].  
The initial high oil saturation and fast water breakthrough imply that some oil was bypassed and remained unrecovered in LHS. Property modelling using reservoir simulation was employed to build models of porosity and water saturation at the start and the end of production using Kv/Kh = 0.1 and Kv/Kh = 1 for comparison (Figures 13-15).  
The highest porosity is related to the dome in the vicinity of Well E. Wells G and H also fall into the area with a high porosity of above 16% (Figure 13). Other wells with lower porosity in LHS surrounded this dome.  
Figure 12: Actual (historical data) and simulated water production using different pairs of relative permeability curves that are plotted for different cases as shown in Figure 11: (**a**) Well-A, (**b**) Well-B.  
Figure 14: Petrophysical models of water saturation of porous LHS-LD reservoir: before the start of production (**a**) and at the end of production with (**b**) \(\mathrm{Kv}/\mathrm{Kh}=0.1\) and (**c**) \(\mathrm{Kv}/\mathrm{Kh}=1\). Red, orange, and yellow colors indicate the areas of remaining oil.  
Figure 13: Petrophysical model of porosity of LHS reservoir.  
In LHS-UD, the highest initial oil saturation was to the NW of Wells C and F and between Wells A and B (Figure 14). In these areas, the oil saturation decreased after production but still had significant oil saturation remaining. Although the difference was insignificant, Kv/Kh = 1 indicates a larger area of the remaining oil saturation.  
Another area with high initial oil saturation was to the SE of Wells E and G. With Kv/Kh of both 0.1 and 1, these areas remained unrecovered. The area around Well D with Kv/Kh = 1 was more depleted compared to Kv/Kh = 0.1.  
In LHS-LD, the highest initial oil saturation was also in the dome close to Wells E and G, with even higher oil saturation to the south where wells were yet to be drilled (Figure 15). Another undrilled area of high oil saturation is located to the NW of Wells F, C, and A. After years of production, this reservoir is almost fully depleted. Oil was recovered in Wells A, D, C, and F. The drainage areas around Wells B, E, G, and H were significantly reduced. However, oil remains at the top of the dome to the SE of Wells E and G in the undrilled part. When the ratio of Kv/Kh was equal to 1, there was an increase in oil saturation not only in the dome area but also towards the northwest of Well C, as compared to when the Kv/Kh ratio was 0.1. Therefore, two areas remained unswept in the LHS reservoir due to the absence of wells: to the NW of Wells C and F and to the SE of Wells E and G.
# A Novel Carrier Tracking Approach for GPS Signals Based on Gauss-Hermite Kalman Filter
## 6 Conclusions  
A GHKF-based tracking system approach was presented in this paper. It was designed to work under conditions of dynamic and weak GPS signal reception. In the proposed approach, a method for forming joint estimates of the RNP in a coherent tracking mode was considered. The performance of the proposed algorithm was evaluated by simulations, using a software-defined simulator and an SDR device. Two nonlinear tracking algorithms were also tested for comparison: EKF and UKF.  
The simulation results demonstrated that the proposed algorithm could improve the tracking sensitivity and capability compared with the EKF and UKF methods while simultaneously preventing bit synchronization loss under static weak signal conditions. In addition, under dynamic weak signal conditions, the proposed method was more robust than the other two methods.  
The obtained results showed that the GHKF algorithm could form RNP estimates with appropriate accuracy in the proposed static and dynamic scenarios, which are typical for indoor pedestrian and urban vehicle navigation. It is worth mentioning that the tracking performance highly relies on the knowledge of noise statistics. However, the noise covariance is fixed during the GHKF iteration, which may not reflect real variable-noise statistics. Therefore, the adaptive GHKF can be employed in the tracking loop to further improve the tracking performance under more challenging environments.  
[PERSON] and [PERSON] conceptualized and wrote this paper; [PERSON] and [PERSON] designed the simulations and experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] plotted the figures. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Tomsk State University Development Programme, grant number (NU 2.4.4.22 LMU).  
The authors extend their appreciation to _[PERSON]_ (candidate of physical and mathematical sciences, associate professor, NR TSU), _[PERSON]_ (postgraduate student, NR TSU), and _[PERSON]_ (candidate of physical and mathematical sciences, associate professor, NR TSU).  
The authors declare no conflict of interest.