esa-sceva/satcom-chunk-collection huggingface dataset:
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 6 Conclusions  
This section summarizes the contributions of this paper as follows. We first interpret why it is difficult to achieve high-sensitivity and high-dynamic GNSS signal tracking at the same time. Then a conventional design of a third-order PLL is put forward, and taken as the baseline, i.e., SSV RRAM. Taking both the low C/N\({}_{0}\) and high LOS jerk into account, an optimized strategy of CL form is recommended, and this strategy can be implemented by an adaptive four-state KF-based algorithm, which can adjust its LNBW according to the received signal power and LOS dynamics. The paper also describes the relationship between the filter gain matrix and equivalent LNBW. In order to prevent the PLL from losing lock, an INS-assisted strategy is adopted, and the decoupling of the Doppler frequency estimate and carrier phase estimate should be attributed to the non-coherent processing. Finally, a mission of a lunar upper stage in its Earth phasing orbit is used to test the validity of these strategies. The simulation results substantially prove the superiority of an adaptive four-state KF-based strategy under non-maneuvering conditions and the advantage of an OL tracking strategy aided by high-quality IMUs under maneuvering conditions.  
It must be noted that the analyses in the paper are all based on the steady state performance of the tracking structure, but the transient response is another key point that requires a lot of in-depth studies. Therefore, future work will focus on the improvement of transient capability of the signal tracking strategy in the SSV.
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 2 Analysis on the Megatrend of Mobile Services  
In this section, various changes to our daily life that are brought by 5G services are described by investigating five megatrends of mobile services as follows:  
* Explosion of mobile data traffic
* Rapid increase in connected devices
* Everything on the cloud
* Hyper-realistic media for convergence services
* Knowledge as a service enabled by big data analysis  
As mobile multimedia contents streaming and social networks have become more popular, the demand for mobile traffic has continuously increased. In addition to this, new types of multimedia services such as augmented reality, virtual reality and holograms, which all require huge traffic volumes, have emerged. According to [2], the amount of mobile data traffic in 2020 (30.6 exabytes per month, 1 EB = 1,000,000 TB) will be 8.3-times higher than in 2015 (3.7 exabytes per month).  
In addition to mobile traffic volume, the number of mobile devices has increased exponentially by introducing new types of devices such as wearable devices, sensors, vehicles, drones and robots. In particular, this trend has accelerated because many machines with sensors and/or actuators are connected to communication networks for IoT services [3]. Globally, it is expected that the market of mobile handsets and other IoT devices, which are connected to the Internet, may increase from USD7.9 billion in 2015 to USD11.6 billion in 2020 [2; 4]. The increase of active connections requires another direction of network evolution.  
To access mobile networks with high computing power through simple portable devices, mobile cloud computing has emerged as one of the most essential technologies [5]. It is expected that the portion of mobile cloud traffic with respect to total mobile traffic will increase from 35% in 2013 to 70% by 2020 [6]. Furthermore, most mobile services in the 5G era are expected to be integrated with the mobile cloud computing system.  
Unlike current 2D multimedia, hyper-realistic media services like AR, VR and multi-point view are enabled by the development of mobile networks and media technologies. Interactive services based on 3D holograms are also expected to become more popular. These hyper-realistic media services are tightly coupled with other sectors of the industry such as robots, drones, vehicles, health-care, personal assistance and security [7].  
It is expected that big data analytics will create new opportunities in various areas because the value of data can be extracted through data gathering, processing, predictive analysis, inference, etc. The potential of big data analysis, especially in the area of knowledge services and artificial intelligence, is maximized by employing cloud computing technology, e.g., machine learning, deep learning, inference and recognition of IBM Watson [8; 9].
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## 1 Introduction
[32] presented a local optimization navigation algorithm based on fuzzy logic, using the \"recognition-memory\" strategy to process the sensor information. When the current planning path forms a dead zone and runs repeatedly, \"identification\" is formed, and the path and navigation decision are re-planned to avoid obstacle colliding. In our study, the advantages of GPS, Dijkstra algorithm, ant algorithm, CNN, and fuzzy control are utilized in the control system design. Contributions of this research are mainly in three parts. The first is to develop a robot that can conduct outdoor mobile patrols using a simple physical mechanical structure and inexpensive and easily available electronic components and motors. Secondly, a convolutional neural network in the system integration is used to improve the robot's face recognition ability in outdoor patrol. Through the combination of Gaussian function a new type of ideal fuzzy control model is established. The DGPS method plus Dijkstra algorithm and ant algorithm for robot patrol in real-time path planning has been approved to be sufficiently accurate. Finally, through software integration and data transmission and analysis, a control center for remote robot manipulation and monitoring of patrol conditions can be set up anywhere, which proves the flexibility and feasibility of this conceptual system.
# New Orthogonal Transforms for Signal and Image Processing
## 1 Introduction  
The literature on orthogonal transforms for signal and image processing is extensive, including the often-cited books by [PERSON] and [PERSON] [1] and [PERSON] [2]. In this paper, a new orthogonal transform is proposed that can be treated as a generalized Walsh-Hadamard transform [3, 4, 5, 6, 7, 8, 9]. A modification of the proposed transform is also investigated. This transform has a simple structure leading to a fast calculation of both forward and inverse transforms and hence very fast computational algorithms with a significant reduction in the number of calculations required. Indeed, the forward and inverse transforms have the same structure, only differing in the constant coefficient. The proposed transforms could be effectively applied in such areas of signal and image processing as watermarking technology [10, 11, 12], steganography, intelligent monitoring and others [13, 14, 15, 16]. The general form of this transform enables the possibility of selecting optimal parameters for specific tasks, implying there are potentially other applications. Further investigations are required to determine in which applications it has maximum effectiveness.
## 3 Results and Discussion
### Essential Oil and Hydrosol Extract Chemical Profile Implementing GS-MS Analysis
#### 3.1.2. S. _officialis_ Essential Oil and Hydrosol Extract Chemical Profile  
In total, ten major compounds were detected in _S. officialis_ EO, accounting for 84.4% of the total essential oil. The most abundant compounds, representing 50% of essential oil, were \(\mathbf{\alpha}\)-thujone (25.1%), _1,8_-cineole (15.8%) and \(\mathbf{\beta}\)-pinene (10.0%). Moreover, _E_-caryophyllene and \(\mathbf{\alpha}\)-humulene were found at equal and lower percentage (7.8%), as well as \(\mathbf{\beta}\)-thujone and borneol (5.0%). The lowest percentages corresponded to \(\mathbf{\alpha}\)-pinene and camphor (2.9%), as well as camphene (2.0%). These compounds were commonly found in the essential oil of _S. officialis_, though with different percentages depending on the season, geographic origin, environmental factors, extraction methods, plant organs, sampling techniques, and genetic differences. In agreement with this study, [PERSON] et al. [48], reported that the EO of _S. officialis_ inflorescence cultivated in Germany (Table 1) was mainly composed of \(\mathbf{\alpha}\)-thujone, _1,8_-cineole, \(\mathbf{\beta}\)-pinene and _E_-caryophyllene. In addition, [PERSON] et al. [49] attributed the difference regarding the chemical composition of _S. officialis_ EO to the part of the plant. The compounds \(\mathbf{\beta}\)-thujone, _1,8_-cineole and camphor were most abundant in the EO of _S. officialis_ leaves, while \(\mathbf{\alpha}\)-thujone, \(\mathbf{\beta}\)-pinene and _1,8_-cineole were most abundant in the EO of _S. officialis_ flowers. Furthermore, in another study of whole aerial parts of _S. officialis_ from India, the percentages of \(\mathbf{\alpha}\)-thujone, _1,8_-cineole, _E_-caryophyllene, viridiflorol, \(\mathbf{\beta}\)-pinene and borneol were found to be higher in the essential oil. Similarly, it was reported that the total content of thujone was lower in inflorescence in comparison to stems, contrary to the observed trend of \(\mathbf{\beta}\)-pinene accumulation [50]. On the other hand, data presented in previous studies on _S. officialis_ EO indicated that the most abundant constituents were \(\mathbf{\alpha}\)-thujone and camphor [51; 52]. Moreover, recent studies of _S. officialis_ cultivated in South Brazil [27] and Tunisia [29; 53], reported that the EOs mainly contained \(\mathbf{\alpha}\)-thujone, _1,8_-cineole and camphor. [PERSON] et al. [22] reported that the
# Commonsense-Guided Inductive Relation Prediction with Dual Attention Mechanism
## 3 Methodology
### Our Model
#### 3.4.4 Commonsense Re-Ranking  
To ensure that the generated triples conform to commonsense, they are compared to commonsense knowledge. The generation of commonsense knowledge is specifically divided into four steps:  
**Fine tuning BERT to acquire contextual representations.** The pre-trained language model BERT is suitable for acquiring the contextual representations of triples. Specifically, given a tuple \((u,r,v)\), u, r, and v are iteratively masked such that the encoder predicts the masked elements using the other two unmasked elements, which enables the BERT model to better understand the relationships between the tuple elements.  
**Filtering abstract concepts.** Replacing ternary entities with entity concepts may result in concepts with a high level of abstraction. We use entity and concept representations to compute the probability of a concept appearing in an entity to measure the abstraction level of the concept. A threshold is set to filter out concepts with a higher abstraction level whose occurrence expectation is lower than the threshold.  
**Entity-to-concept mapping.** After filtering out the higher degree of abstraction, concepts were used instead of entities in the triad to obtain conceptual triples. Commonsense knowledge in the individual form C1 was obtained by eliminating duplicate concept-level triples. Commonsense knowledge in the set form C2 was then obtained by merging concept-level triples that contained the same relations.  
**Filtering relationship-independent concepts.** The commonsense knowledge obtained by substituting concepts for entities would also suffer from the problem that concept c is not related to relation \(\mathbf{e}_{r}\). To measure the degree of relevance of concept c and relation \(\mathbf{e}_{r}\), the following cosine similarity was calculated to obtain the similarity score.  
\[R(r,c)=\cos(\mathbf{e}_{r},\mathbf{c}), \tag{15}\] \[\mathbf{e}_{r}=\underset{\mathbf{e}_{i}\in N_{r}}{\text{mean}}( \mathbf{e}_{i}), \tag{16}\]  
where \(N_{r}\) denotes the set of relations \(r\) corresponding to the head entity or the tail entity, and \(\mathbf{c}\) and \(\mathbf{e}_{r}\) are the contextual representations of concepts and entities, respectively. A threshold was set to filter the target of relationship-independent concepts.  
Based on the constructed commonsense knowledge database, we propose a simple commonsense-based re-ranking strategy, i.e., one that is based on the KGs of the train set or test set. Moreover, if the triple \((u,r_{t},v)\) satisfies the commonsense \((c_{t},r_{t},c_{t})\), the score of the ternary is increased by \(\omega>0\).  
\[Score(u,r_{t},v)=\mathbf{W}_{s}\Big{[}\mathbf{h}_{u}^{L}\oplus\mathbf{h}_{v}^ {L}\oplus\mathbf{e}_{r_{t}}^{L}\oplus\mathbf{s}_{G}\Big{]}+\omega(I((u,r_{t}, v)\in(
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 4 Architectures
### Integrated Operations
All operations follow traditional satellite interfaces while incorporating network operational requirements. Figure 6: Persistent connectivity example scenario featuring a multi-layered constellation with separated operations (in purple) and data planes (in green). tracking to establish and maintain a continuous service data plane with the provided QoS requirements (both for the relay and control satellites). Same as the previous architecture, the CMS receives feedback from the satellites through telemetry and from the ground control to adjust operations accordingly.
## 3 Framework
### Principles of SERENA Coloring Algorithm
* _Rule R2:_ node \(N\) takes: * the smallest color unused in \(\mathcal{N}(N)\) in case of general communications; * the smallest color unused in \(\mathcal{N}(N)\) and higher than the color of its parent in case of tree-based communications. These rules tend to minimize the number of colors needed to color all network nodes. Notice that in the case of tree communication, the rule obliging a node to take a color higher than the color of its parent has been introduced to minimize the delay needed to collect data by the sink. Scheduling medium access according to the decreasing order of colors enables each node to aggregate the information received from its children before transmitting it to its parent. Consequently, information from all nodes can reach the sink in a single cycle. Thus, delays are minimized, as we will see in Section 4.4.
## 2 Example NPP Structure and Earthquake Loads
### Artificial Earthquake Lads  
To construct a realistic seismic loading scenario for the example NPP structure, artificial ground motions were generated using the stochastic simulation model proposed by [PERSON] and [PERSON] (2018). This technique is notable for its ability to incorporate a variety of earthquake scenarios using probabilistic models to manage seismic wave uncertainty, thereby enabling the generation of more realistic ground motions.  
[PERSON] and [PERSON]'s method determines seismic wave characteristics through frequency domain analysis and generates artificial earthquakes based on these parameters. It adjusts the amplitude using actual earthquake records to ensure that the artificial seismic wave closely resembles the actual vibration intensity of a real earthquake. For this study, the ground characteristics of the Ulsan area, where many NPP structures are located in Korea, were used as the input parameters.  
To ensure that the input parameters reflected actual geological and geotechnical conditions, the values were selected based on site-specific investigation documents rather than arbitrary assumptions. The fault type was set to strike-slip, with a moment magnitude of 6.0 and a rupture distance of 40 km, based on a geological report on regional fault structures. The shear wave velocity (V\({}_{30}\)) was set to 715 m/s, as reported in a site-specific geotechnical  
Figure 3: Configuration of inputs and outputs in the ML database.  
Figure 2: Finite element model of the OPR1000 auxiliary building: (**a**) perspective; (**b**) plan.  
investigation conducted in the same region. By grounding the input parameters in real investigation data, the artificial ground motions were tailored to reflect both the tectonic setting and site-specific seismic response characteristics of the target location. The probabilistic nature of the [PERSON] and Kiuregbian's method further enabled the generation of a broad set of motions with controlled variability, making the dataset suitable for machine learning model training.  
In this study, 500 artificial earthquake loads were generated using this method. Spectral acceleration graphs for 100 randomly selected earthquakes are displayed in Figure 4. This figure illustrates the diverse characteristics of the generated artificial ground motions, which help prevent overfitting during the machine learning process. Moreover, this diversity allows for objective verification by using a test dataset with different characteristics from the training dataset when evaluating the performance of the machine learning models. The acceleration time history of a randomly selected sample earthquake from the generated ground motions is presented in Figure 5. As depicted in the figure, the waveform of the artificial seismic wave is irregular, resembling that of actual seismic events.
# A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode
## 5 Discussion
### Discussion of Influencing Factors and Countermeasures
* _Shooting Angle_ As for the shooting angle, it mainly affects vehicle counting, and it is due to the distortion of the image caused by perspective. The imaging principle of the camera is pinhole imaging. As a result, the view of the camera on the road and the vehicle usually produce a certain distortion. In general, the more oblique the shooting angle, the more severe the distortion. As shown in Figure 16, the projection of vehicle driving on \(lane^{1}\) was tilted to the left, while the projection of vehicle driving on \(lane^{4}\) was tilted to the right. This phenomenon is most obvious when the vehicles were shot from the side. As shown in Figures 19 and 20, the projection of vehicle was tilted to the right by very much. If only the primary loops were set, there may be some errors in counting. However, with the help of the secondary loops, the counting was not greatly affected by the shooting angle. The experimental results also prove that the multi-loop setup works well against this adverse effect, as shown in Table 15.
## Appendix A A Basic Notion of DBSCAN  
DBSCAN algorithm characterizes the closeness of the samples based on two neighborhood parameters, \(\varepsilon\) and \(Min\)Pts. For a given data set \(D{=}\left\{x_{1},x_{2},\ldots,x_{n}\right\}\), there defines some notions as following:  
* \(\varepsilon\)-neighborhood  
To \(x_{j}\in D\), the \(\varepsilon\)-neighborhood of \(x_{j}\) is defined as the samples in \(D\) and the Euclidean distance from \(x_{j}\) is not greater than \(\varepsilon\), can be expressed as:  
\[N_{\varepsilon}(x_{j})=\left\{x_{i}\in D\big{|}dist(x_{i},x_{j})\leq\varepsilon\right\} \tag{1}\]
* Core point  
If the \(\varepsilon\)-neighborhood of \(x_{j}\) contains at least \(Min\)Pts samples, which is \(\big{|}N_{\varepsilon}(x_{j})\big{|}\geq Min\)Pts (\(\|\) means the number of elements in the data set), then \(x_{j}\) is a core point.  
* Directly density-reachable If \(x_{j}\) is in the \(\varepsilon\)-neighborhood of \(x_{i}\), and \(x_{i}\) is a core point, then \(x_{j}\) is directly density-reachable from \(x_{j}\).  
* Density-reachable Assuming a sample sequence \(\left[p_{1},p_{2},\ldots,p_{n}\right]\), \(p_{1}=x_{i}.p_{n}=x_{j}\) and \(p_{k+1}\) is directly density-reachable from \(p_{k}\), then \(x_{j}\) is density-reachable from \(x_{i}\).  
* Density-connected If \(x_{i}\) and \(x_{j}\) are density-reachable with \(x_{k}\) respectively, then \(x_{i}\) and \(x_{j}\) are density-connected.  
Figure 11 visually shows the above notions.  
As shown in Figure 11, when \(Min\)Pts \(=3\) and dotted circle represent \(\varepsilon\)-neighborhood of each points; \(\left\{x_{1},x_{2},x_{3},x_{5}\right\}\) are core points; \(x_{4}\) and \(x_{6}\) are both density-reachable from \(x_{1}\); \(x_{4}\) and \(x_{6}\) are density-connected.  
Based on the notions defined above, DBSCAN defines clusters as: a set of largest density-connected samples. For given parameters \(\varepsilon\) and \(Min\)Pts, cluster \(C\subseteq D\) is a non-empty sample subset which satisfies the following properties:  
connectivity: \(x_{i}\in C,x_{j}\in C\Rightarrow x_{i}\) and \(x_{j}\) are density-connected.  
maximality: \(x_{i}\in C,x_{i}\) and \(x_{j}\) are density-connected \(\Rightarrow x_{j}\in C\).  
_B. DBSCAN Algorithm and Framework_  
The DBSCAN algorithm is shown as Figure 12. The input to the algorithm are data set \(D\) and the parameters (\(\varepsilon\) and \(Min\)Pts). From line 1 to line 7, the algorithm finds all core points based on the given parameters (\(\varepsilon\) and \(Min\)Pts). After that, starting from any core point, the algorithm finds all the density-connected samples to generate clusters until all core points are visited. Then the clustering result \(C{=}\left\{C_{1},C_{2},\ldots,C_{k}\right\}\) and number of core points \(\left\{\Omega\right\}\) are output from the DBSCAN algorithm.  
Figure 13 shows the basic framework of DBSCAN algorithm. It shows the DBSCAN clustering algorithm intuitively and concisely.  
_Appl. Sci._**2020**, _10_, 1095 11 of 14  
**Figure A2. DBSCAN algorithm.**
## Appendix B  
The convergence proof of position errors \(x_{e}\) and \(y_{e}\) is as follows:  
Define Lyapunov function  
\[V_{1}(x_{e},y_{e})=\frac{1}{2}x_{e}^{2}+\frac{1}{2}y_{e}^{2}\]  
Take time derivation for \(V_{1}\) and substitute it into Formulas (22) and (23), which yields  
\[\dot{V}_{1}(x_{e},y_{e})=x_{e}\big{(}-k_{1}x_{e}-k_{2}|x_{e}|^{p} sign(x_{e})\big{)}-\frac{u}{\sqrt{\Delta^{2}+A^{2}}}y_{e}\big{(}y_{e}+k_{y}|y_{e}|^{ p}sign(y_{e})\big{)} \tag{12}\]  
where \(A=y_{e}+k_{y}|y_{e}|^{p}sign(y_{e})+\Delta\tan\hat{\beta}\), then  
\[\frac{u}{\sqrt{\Delta^{2}+A^{2}}}\leq\frac{u}{\sqrt{\Delta^{2}+(\Delta\tan\beta )^{2}}}=\frac{u}{\sqrt{\Delta^{2}+\Delta^{2}\frac{v^{2}}{u^{2}}}}=\frac{u^{2 }}{\Delta U}\leq k_{4}\]where \(U=\sqrt{v^{2}+u^{2}}\), \(k_{4}\geq\max\bigl{(}u^{2}/(\Delta U)\bigr{)}\), so that Formula (A3) satisfies  
\[\dot{V}_{1}(x_{e},y_{e}) \leq x_{e}\bigl{(}-k_{1}x_{e}-k_{2}|x_{e}|^{p}sign(x_{e})\bigr{)}-k _{4}y_{e}\bigl{(}y_{e}+k_{y}|y_{e}|^{p}sign(y_{e})\bigr{)}\] \[=-\ k_{1}x_{e}^{2}-k_{4}y_{e}^{2}-k_{2}x_{e}|x_{e}|^{p}-k_{4}k_{y} y_{e}|y_{e}|^{p}\] \[=-k_{1}x_{e}^{2}-k_{4}y_{e}^{2}-k_{2}x_{e}^{p}+1-\ k_{4}k_{y}y_{e}^ {p+1}\] \[=-k_{1}x_{e}^{2}-k_{4}y_{e}^{2}-k_{2}\bigl{(}x_{e}^{2}\bigr{)}^{ \frac{p}{2}+1\over 2}-\ k_{4}k_{y}\bigl{(}y_{e}^{2}\bigr{)}^{\frac{p}{2}+1\over 2}\] \[=-\xi_{1}V_{1}-\xi_{2}V_{1}^{\sigma}\]  
in which \(\xi_{1}=k_{1}=k_{4}\), \(\xi_{2}=k_{2}=k_{4}k_{y}\), \(\sigma=\frac{p+1}{2},\sigma\in(0,1)\).  
According to Lemma 3, \(x_{e},y_{e}\) converge in finite time \(+T\leq\frac{1}{\xi_{1}(1-\sigma)}\log\frac{\xi_{1}V_{1}(x_{0},y_{0})^{1-\sigma }+\xi_{2}}{\xi_{2}}\) to the equilibrium point \((0,0)\).