esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Use Cases
#### 5.2.2 Underwater Search and Recovery  
This experiment consists of running autonomous trajectory tracking while, at the same time, an object detection algorithm (ArUco markers) is running on the computer inside of the robot. When the robot detects an object, it navigates to a nearby place and performs a semi-autonomous manipulation routine conducted by the operator. A camera on the gripper was used to perform a visual-servoing task. Once the detection was done, the processed image was transferred through the VLC modems. This experiment was designed in such a way that there were no lost connection points and the manipulation area was aligned to the modem. In a real intervention cooperation and collaboration among robots can be used. For example, using a surface vehicle as a support vehicle can be a great solution to overcome the problem of misalignments. Figure 15 shows two images from the intervention experiments. In the left image, the VLC modem at the ground station is visible in the control station, located behind the glass. In the right image, a rear view of the underwater robot shows the VLC modem pointing directly at the ground station.  
Figure 14: Network congestion control of the pipe inspection use case.  
Similar to the pipe inspection task, the HRI was adapted to perform this intervention task. Principally because the communication was performed using a VLC modem and the need for supervision and decision making during the intervention, network congestion control was used for image transfer. Figure 16 shows three pictures of the intervention task with different bandwidths and different requirements of the operator. At the top of the figure, a medium-quality grayscale image is shown in the HRI. It the center, a low-quality RGB image is displayed. Lastly, a high-quality RGB image is shown on at the bottom. This figure exhibits the cases of the experiment where, depending on the bandwidth and the operator requests, the image compression was adjusted to prevent the congestion of the VLC network.  
Results presented in Figure 17 show that most of the time, the desired signal was either 0.3 s or 0.2 s. These values represent 3.3 and 5 frames per second, respectively. The reason for having a low desired TBF signal is that in a manipulation experiment where the operator needs to select the grasp points, a higher TBF and reduced movement of the underwater robot increase the accuracy in selecting those points. It can be observed that  
Figure 16: HRI on the left and different compressed images depending on the bandwidth on the right.  
Figure 15: Search and recovery use-case experiment.  
between 255 s and 280 s, the operator activated the manual mode. During this period, the robot's movement was planned to approach the manipulation distance.
## 2 Results and Discussion
### Metabolism of VOCs of ARR
#### 2.3.1 Metabolites of the Oxidation Reaction  
The double bond of M8 (limonene) was oxidized to form the epoxy bond and converted to M15 (_cis_-limonene oxide) [15]. M33 (_l_-menthol) was oxidized by cytochrome P450s, and M24 (isopulegol) was produced. Hydroxylation was found in the carbon atom adjacent to the double bond of M3 (\(\beta\)-pinene), and M36 (_l_-pinocarveol) was formed [16].  
M64 (safrole) was oxidized to form M141 (3,4-methylenedioxypheny-1-propanal) and M149 (1-hydroxy-2-(prop-2-enyl)-4,5-methylenedioxypbenzene), and M96 (_m_-eugenol) was formed after the breaks and methylation of methylenedioxybenzene [17; 18; 19]. M11 (terpinolene) was transformed into M42 (\(\alpha\)-terpineol acetate) after oxidation and esterification.  
Metabolites M132 (piperol) and M137 (methoxyeugenol) were detected only in the decoction-treated group, and their original constituents were presumed to be M104 (piperol) [20] and M93 (eugenol).
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Quantum Deep Learning Techniques with Copyright Detection
#### 2.2.4 Quantum Techniques for Copyright Detection  
The incorporation of quantum computing into deep learning methods presents notable advancements in copyright detection for digital content. Quantum algorithms can improve the efficiency and accuracy of identifying copyrighted material by utilizing principles such as superposition and entanglement. These techniques enable the swift analysis of large datasets, facilitating the detection of potential infringements in real time. One effective method involves quantum-enhanced machine learning algorithms that examine the visual features of images to evaluate their similarity to existing copyrighted works. By applying quantum adaptations of traditional algorithms, such as convolutional neural networks (CNNs), the detection process can be accelerated, which is particularly beneficial in environments with extensive digital content where timely identification is essential.  
Additionally, integrating perceptual image hashing techniques within a quantum framework can enhance copyright detection capabilities. These methods create unique identifiers for images, allowing for efficient comparisons against databases of copyrighted materials. The computational power of quantum systems enables more thorough similarity assessments, even for images that may have been altered. As copyright detection technologies progress, the combination of quantum computing and deep learning not only bolsters the protection of intellectual property rights but also encourages the responsible use of digital content. This integration is vital for ensuring compliance with legal standards in an increasingly intricate digital landscape.
## 3 Results and Discussion
### pH and FOS/TAC  
The pH and FOS/TAC were found to correlate with the OLR. The pH showed a consistent negative correlation with organic matter levels and a positive one with HRT. Both stages showed a near-neutral pH (7.23 \(\pm\) 0.04-6.96 \(\pm\) 0.03) as long as OLR was maintained within 2.0-4.0 gVS/dm\({}^{3}\cdot\)d (Figure 6a). An increase in OLR to 5.0 gVS/dm\({}^{3}\cdot\)d triggered significant decreases in pH--6.75 \(\pm\) 0.05 in S1 and 6.84 \(\pm\) 0.03 in S2 (Figure 6a). Similarly, the highest tested OLR (V4) led to elevated FOS/TAC at 0.44 \(\pm\) 0.02 (S1) and 0.48 \(\pm\) 0.02 (S2) (Figure 6b). Such values are indicative of digester overload. The FOS/TAC ratio in variants V1-V3 ranged from 0.37 \(\pm\) 0.02 to 0.43 \(\pm\) 0.02 in S1 and from 0.33 \(\pm\) 0.03 to 0.42 \(\pm\) 0.01 in S2 (Figure 6b). In both stages, very strong positive correlations were found between the pH and biogas and methane production yields. The coefficients of determination reached R\({}^{2}\) = 0.9765 and R\({}^{2}\) = 0.9722 in S1 (Figure 7a,b) and R\({}^{2}\) = 0.9601 and R\({}^{2}\) = 0.9188 in S2 (Figure 7a,b). Similarly, very strong but negative correlations were found between FOS/TAC and biogas (R2 = 0.9287) and methane (R2 = 0.9365) yields in S1 (Figure 7c,d) and R2 = 0.9675 for biogas (Figure 7c) and R2 = 0.9316 for methane in S2 (Figure 7d).  
The pH value is a key determinant of stability in an anaerobic reactor [50]. An almost neutral pH in an anaerobic medium means a balance between the hydrolysis, acidic, and methanogenic phases of anaerobic conversion [51]. The pH decreases are primarily driven by the build-up of VFAs, which are produced during the acidogenesis stage [52], which may occur with too high OLRs [53]. A decreased pH results in the inhibition of the activity of methanogenic bacteria, effectively reducing pollutant removal rates and biogas production [54]. [PERSON] et al. [55] have also demonstrated that increased OLR--though a possible driver of methane yields--may lead to a decreased pH and system failure due to the accumulation of VFAs and free ammonia [55]. By the same token, the FOS/TAC ratio is used as a metric of AD stability. The indicator shows the ratio of volatile organic acids to the alkaline buffer capacity [56]. In a stable AD process, the FOS/TAC falls within 0.3-0.4 [57]. On the other hand, an FOS/TAC above 0.5 indicates a reduced production of biogas associated with the lack of optimal running parameters for anaerobic microbes [56].
## 4 Finite Element Analysis of Temperature Distribution Characteristics in HSLLRBs
### Temperature Distribution Analysis  
#### 4.3.1 Steady-State Thermal Analysis  
The steady-state thermal distribution is obtained by solving the temperature matrix equation:  
\[[\mathrm{K}]\{\mathrm{T}\}=\{\mathrm{Q}(\mathrm{T})\} \tag{31}\]  
This equation is based on [PERSON]'s law, in which [K] and [Q] are constants. It is a linear equation, and the transient effect is not considered in the steady-state analysis.  
In this study, the steady temperature distribution of the roller bearing is analyzed, and the parameters of the bearing are given in Tables 1 and 2. The roller is defined as the rigid target surface and the two ring raceways are flexible contact surfaces. The rolling friction coefficient between the roller and the inner and outer raceways is 0.0025. The cylindrical roller bearing mesh is divided into 258,381 nodes and 150,100 units.  
Figure 9 is the steady temperature distribution diagram of the bearing. The results show that the highest temperature of the bearing is 114.78 \({}^{\circ}\)C at the center of the inner ring raceway, and the lowest temperature is 81.74 \({}^{\circ}\)C on the outer surface of the cage.  
Figure 10 shows the temperature distribution in HSLLRBs with different slip ratios. The highest temperature shows an increasing trend from 108 \({}^{\circ}\)C to 186 \({}^{\circ}\)C, but the lowest temperature shows a reverse trend. The main reason for this is that the increment in friction heat of the inner (outer) rings raceway is greater than the reduction in friction heat of the oil stirring of the roller and the guide surface of the cage. In addition, the results also show that the inner ring raceway has the highest temperature, while the cage has the lowest.  
Figure 9: Steady-state temperature distribution of the bearing. (**a**) Front view; (**b**) section view.
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 5 Discussion
The global approach can be easily applied to instruments that fly in near-polar, sun-synchronous orbits, however, adjustments to background definition are needed for instruments operating on the International Space Station (e.g., OCO-3) or on geostationary orbit (e.g., GeoCarb) due to the possible influence of diurnal variations and spatial coverage. Further applications of this method could combine anomalies from similar platforms, e.g. belonging to the same constellation.
## 4 Implementation Details
### Design Principles  
emgr is realized using the high-level, interpreted Matlab programming language, which is chosen due to its widespread use, long-term compatibility and mathematical expressiveness. This enables first, a wide circulation due to compatibility with Mathworks Matlab(r)[67], and second, the usage of the open-source variant Gnu Octave[68]. Generally, the implementation of emgr follows the procedural programming paradigm, includes various functional programming techniques and avoids object-oriented programming.  
Since empirical Gramians are computable by mere basic linear algebra operations, Matlab code can be evaluated efficiently by vectorization, which transfers computationally intensive tasks as bulk operations to the Basic Linear Algebra Subroutines (BLAS) back-end.  
Overall, emgr is a reusable open-source toolbox, and encompasses less than 500 LoC (Lines of Code) in a single file and a cyclomatic complexity of <100 of the main function. Apart from a Matlab interpreter, emgr has **no** further dependencies, such as on other toolboxes. The source code is engineered with regard to the best practice guides [69] (coding style) and [70] (performance).  
Furthermore, two variants of emgr are maintained: First, emgr_oct (See [[http://gramian.de/emgr_oct.m](http://gramian.de/emgr_oct.m)]([http://gramian.de/emgr_oct.m](http://gramian.de/emgr_oct.m))), uses Octave-specific language extension: _default arguments_ and _assignment operations_, second, emgr_lgc (See [[http://gramian.de/emgr_lgc.m](http://gramian.de/emgr_lgc.m)]([http://gramian.de/emgr_lgc.m](http://gramian.de/emgr_lgc.m))), enables compatibility to Matlab versions before 2016b not supporting _implicit expansion_, also known as _automatic broadcasting_.
## 2 Materials and Methods
### Bioink Formulation  
An alginate-based biomaterial (IK1020000303, Cellink Bioink, Cellink, Sweden) was mixed with human chondrocytes according to the manufacturer's instructions to create the bioink. Briefly, human articular chondrocytes isolated from hyaline cartilage in the knee and expanded to the second passage were purchased from Lonza (NHAC-kn, Lonza Bioscience, Breda, The Netherlands). The cells were expanded until the third passage in chondrocyte growth medium (CC-3216, Lonza Bioscience, Breda, The Netherlands) according to the manufacturer's instructions, in an incubator at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\), and 90% humidity. The cells were trypsinized when confluency was reached, counted, and re-suspended in a growth medium. The hydrogel was taken up from the stock cartridge by a syringe and was mixed gently 10:1 with the cell suspension using a sterile female-female luer lock, and a second syringe was used to homogenize the cells in the bioink. The mixture was transferred to a new cartridge for bioprinting. According to the study by [PERSON] et al., the total cell density within the human articular cartilage of a medial femoral condyle is 10 \(\times\) 10\({}^{3}\) cell/mm\({}^{3}\)[21]. Therefore, the homogeneous scaffold was given a biomimetic total cell density of 10 \(\times\) 10\({}^{6}\) cell/mL. [PERSON] et al. also described the different cell densities in the different zones of articular cartilage in which it was established that the cell densities corresponding to the superficial, middle, and deeps zones are 24 \(\pm\) 3 \(\times\) 10\({}^{3}\) cells/mm\({}^{3}\), 10 \(\pm\) 0.5 \(\times\) 10\({}^{3}\) cells/mm\({}^{3}\), and 7 \(\pm\) 0.5 \(\times\) 10\({}^{3}\) cells/mm\({}^{3}\), respectively [21]. The gradient scaffold was given a similar biomimetic cell density corresponding to each zone of the articular cartilage: superficial (top), middle (middle) and deep (bottom) zones. The top, middle, and bottom zones were designed to respectively have 20 \(\times\) 10\({}^{6}\) cells/mL, 10 \(\times\) 10\({}^{6}\) cells/mL, and 5 \(\times\) 10\({}^{6}\) cells/mL, respectively (Figure 1).
## 4 Experiments
### Description of Evaluation Metrics and Model Parameters  
In this experiment, we used the following model evaluation metrics:  
* mAP: mean average precision. The average precision is calculated as the weighted mean of precisions at each threshold; the weight is the increase in recall from the prior threshold. The mAP is calculated by finding the average precision (AP) for each class and then averaging over several classes. \[mAP=\frac{1}{N}(\sum_{i=1}^{N}AP_{i})\] (7)
* CMC: cumulative matching characteristics. This is a classic evaluation index in the re-identification problem. The abscissa of the curve is the rank, and the ordinate is the recognition accuracy; \(rank_{n}\) indicates the top n results in descending order of the similarity of the recognition results containing the target. The recognition accuracy is the ratio of the number of \(rank_{n}\) to the total number of query samples.  
Figure 6: A real scenario of roadside perception on Donghai Bridge, Shanghai, China: (**a**) roadside MMW radar and AI cameras; (**b**) Donghai Bridge video data.  
* ADE: average displacement error. This is defined as the Euclidean distance (\(L_{2}\) distance) between the ground truth and prediction of the vehicle locations over all predicted time steps: \[L_{2}=\sqrt{\left(x1-x2\right)^{2}+\left(y1-y2\right)^{2}}\] (8) In Equation (8), \(\left(x1,y1\right)\) and \(\left(x2,y2\right)\) are the locations of the ground truth and prediction of the vehicle locations, respectively.  
Table 1 shows some of our model parameter settings.
## 3 Results and Discussion
### EO Chemical Composition
The oxygen-containing monoterepenes fraction was also higher in strobili (FC and MC) EOs, as compared to the EOs from foliage/leaves, particularly in MC EOs due to their highest content of terpinen-4-ol (12% vs. 3-4%). Contrariwise to strobili EOs, those from leaves and foliage were particularly rich in oxygen-containing sesquiterpenes (mainly elemol) and in diterpene hydrocarbons (mainly phyllocladene). In fact, as already mentioned, the elemol and phyllocladene contents seem to vary in an inverse way to \(\alpha\)-pinene. Oxygen-containing diterpenes were present in very low amounts in FC, MC, and leaf EOs (\(\leq\)1%), whereas the presence of nezukol (3%) in foliage EO has yet to be evidenced. Sesquiiterpene hydrocarbons were also less abundant (0.2-3%), particularly in MC EO. Indeed, contrariwise to the monoterepenes class, with the oxygen-containing monoterepenes accounting for only 4-15% of the total EOs, the sesquiterpenes class mainly consists of oxygen-containing components (6-31% of the total EOs). Our results for EO chemical compositions from different Azorean _C. japonica_ parts were in good agreement with those reported by [PERSON] et al. [30] for different _C. japonica_ parts collected on Corsica (France), except regarding the diterpene hydrocarbons class, which is mainly phyllocladene in Azorean samples and kaurene in Corsican ones [30]. This difference is probably due to genetic differences and/or different environmental conditions.
## 8 Conclusions  
In this paper, the secrecy outage performance of the multi-antenna three secondary users CR-NOMA network is studied, and the exact closed-form SOP expressions of secondary users with a single-antenna scheme, MCC scheme, and ST scheme are derived, respectively. Then, the asymptotic performance of SOP is analyzed, and the relationship between system parameters and secondary user secrecy outage performance is obtained. Finally, an SA optimization algorithm is proposed, and the optimal power allocation coefficient and secrecy outage probability of each TAS scheme are obtained. The simulation results verify the correctness of the theoretical analysis and obtain some valuable conclusions. The results of our work prove that the security advantages of using the MCC scheme and ST scheme to prevent eavesdropping will be beneficial to the design of practical CR-NOMA systems with TAS. The secondary users with multiple-antenna are considered in our future work, and the secrecy outage performance of multiple-user is further studied.  
Conceptualization, J.J.; methodology, S.L.; software, S.L.; validation, S.L.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, S.L. and J.J.; supervision, S.L. and J.J.; project administration, S.L. and J.J.; funding acquisition, S.L. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (no. 61650103), the Natural Science Foundation of Guangxi (no. 2018 GXNSFAA281161).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: Iterative convergence rate of SA algorithm.
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing for Controller
#### 3.2.2 Learning and Optimization for Controller Real-Time Response  
In addition to ensuring the control precision of path tracking control, the controller should also improve the computational complexity. The method for generating a neural network controller with MPC training samples solves the problem of poor real-time response of MPC controllers while ensuring control precision [113]. However, the training samples used in the aforementioned method lose the feedforward information. This problem can be solved by reducing the number of control steps of the controller and using a point-to-line NMPC neural network (PLNMPC-NN) in articulated vehicle path tracking control [114]. The PLNMPC method uses the position and state errors between the predictive horizon and on the reference path as penalty terms and generates the training samples in the non-global coordinate system. The training process and training parameters of PLNMPC-NN are presented in Figure 8.  
The online update of the recurrent neural network (RNN) models captures the nonlinear dynamics when the model is uncertain. In [115], the RNN method is applied to a  
Figure 8: The PLNMPC-NN training process and its training parameters [114].  
Figure 7: The architecture of the proposed strategy for nonlinear system path under uncertain disturbances [110].  
chemical process with time-varying disturbances under LMPC and LEMPC. A real-time control Lyapunov barrier function-based model predictive control (CLBF-MPC) system is developed in the aforementioned research [116]. The CLBF-MPC system considers time-varying disturbances to ensure the closed-loop stability and operation safety, which proves its effectiveness in dealing with the problem of ML model online updating in real-time control.  
The reinforcement learning (RL) learns the optimal control command on the basis of the predefined reward function, simulates the real environment in advance, and improves the real-time control process response in the real environment. The RL network is trained offline, and a large volume of offline training data is used for the RL agent, including initial positions, headings, and velocities of the vehicle [97]. The evaluation of the real vehicle shows that the trained agent steadily controls the vehicle and adaptively reduces the speed to adapt the sample path. Such a control process has better real-time performance.  
It is noteworthy that the learning and optimization of controller parameters significantly influence the control precision. The offline training of samples is a very effective way to improve the real-time response of the controller. The controller design based on learning methods improves the performance of the path tracking controller itself.