esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 MPPT Techniques
### Intelligent MPPT Techniques
#### 3.3.2 Artificial Neural Network  
The term \"artificial neural network\" refers to a technique derived from the behavior of neurons. Because it is an artificial intelligence algorithm, ANN can think for itself. However, extensive knowledge is required to train the neurons [35; 36; 37]. According to the results of the survey, in the implementation of MPPT, ANN incorporates three layers. Figure 11 depicts a typical three-layer ANN model. Because of its broader optimization scope, the ANN is preferred for use in conjunction with other traditional MPPT methods to extract the most power from the PV system.  
[PERSON] and [PERSON] [112] introduced a neural network to extract the MPP of PV modules and the design of a proportional integral-type controller for real-time optimum power tracking. The proposed neural network and the open circuit voltage applied by the regulating cells can be used to identify MPP. The suggested neural network created a PV module-based real-time MPP calculation. In comparison to straightforward mathematical calculation techniques, neural networks generally present a clearer image of functions. In contrast, a neural network works with random data without human intervention. As a result, it is highly dependent on the amount of data available to analyze the network, which makes it a laborious and complicated process. So, as a last resort to deal with issues relating to a lack of competence, neural network analysis is used.  
The third group includes artificial intelligence methods, such as planning and neural networks, as well as fuzzy logic [37; 112]. Studies have suggested using neural networks and fuzzy logic MPPT to address the shortcomings of traditional approaches, which has led to their increased popularity. Since the PV module's features must be provided in order to create MPPT control rules, these solutions, however, are limited in their flexibility. FLCs are commonly employed in MPPT because of their many benefits, including their ability to work with wrong inputs, resolve nonlinearity, and not need a proper mathematical model.  
Figure 11: Three-layer ANN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \(\Delta\)E & & \\ \cline{2-7}  & & **BN** & **SN** & **ZE** & **SP** & **BP** \\ \hline \multirow{4}{*}{E} & **BN** & ZE & ZE & ZE & BN & SP \\  & **SN** & ZE & ZE & SN & SN & SN \\  & **ZE** & SN & ZE & ZE & ZE & SP \\  & **SP** & SP & SP & SP & ZE & ZE \\  & **BP** & BP & BP & BP & ZE & ZE \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fuzzy rule base.  
Additionally, the FLCs are unaffected by changes in environmental conditions. However, the user must choose an acceptable method for calculating the error and formulate the table of base rules to ensure that they are accurate, and FLCs require a significant amount of memory to handle the two extremes.
# An Efficient Multi-Lane SpaceFibre Core for Spacecraft Data-Handling Networks
## 4 FPGA Implementation and Analysis  
The proposed E-ML-SpFi IP core has been synthesized and implemented on the XC7Z100 FPGA. As shown in Figure 8, board-level verification platforms aim at evaluating the traffic between two SpaceFibre end nodes connected by optical fiber cables. Each verification platform is equipped with a data generator, a broadcast generator, a parameter configurator, a status recorder, an integrated logic analyzer (ILA) and an E-ML-SpFi IP core. The data generator and BC generator are responsible for continuously feeding two IP cores with new data. A parameter configurator is exploited to configurate parameters. A status recorder is responsible for monitoring status information in the E-ML-SpFi core. The core depicted in Figure 8 contains four VCs and four lanes. Each VC buffer is configured to store 256 36-bit data words. Each lane has one of the seven data signaling rates specified by the SpaceFibre protocol [11].  
Figure 7: Architecture of the fast alignment subsystem.
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 3 Methodology
### The Desired Vehicle States  
Based on the kinematic model, the desired vehicle states, i.e., the references of the inverse system \(\mathbf{U}_{ref}=\left(a_{x},a_{y},\ddot{\varphi}\right)\), are calculated from the target motion trajectory.  
Supposing that the references from the planning module are the sequences of resultant speed \(V(t)\) and trajectory \(Y(X)\), \(t\) is the time, \(X\) and \(Y\) are the vehicle longitudinal and lateral position in the inertial coordinate system.  
The spatial-temporal relationship is unified by using the travel distance of vehicles [34].  
\[\int Vdt=\sum_{i}^{n}\sqrt{\Delta X(i)^{2}+\Delta Y(i)^{2}}, \tag{33}\]  
where \(\Delta X(i)\) and \(\Delta Y(i)\) are the longitudinal and lateral spacing between the \(i+1\) th point and the \(i\) th point in the inertial coordinate system.  
The desired yaw can be calculated as [35]:  
\[\varphi(i)=\tan^{-1}\frac{\Delta Y(i)}{\Delta X(i)}, \tag{34}\]  
The vehicle velocity along the \(X\) and \(Y\) directions in the inertial coordinate system are  
\[V_{X}(i)=\frac{\Delta X(i)}{\Delta t(i)},\ V_{Y}(i)=\frac{\Delta Y(i)}{\Delta t (i)}, \tag{35}\]  
Then, the vehicle longitudinal and lateral velocities are [28]  
\[\dot{x}(i)=V_{X}(i)\cos\varphi(i)+\Delta Y(i)\sin\varphi(i), \tag{36}\]  
\[\dot{y}(i)=-V_{X}(i)\sin\varphi(i)+\Delta Y(i)\cos\varphi(i), \tag{37}\]  
The longitudinal, lateral and yaw accelerations are yields:  
\[a_{x}(i)=\frac{\dot{x}(i)}{\Delta t(i)}-\dot{y}(k)\frac{\Delta\varphi(i)}{ \Delta t(i)}, \tag{38}\]  
\[a_{y}(i)=\frac{\dot{y}(i)}{\Delta t(i)}+\dot{x}(k)\frac{\Delta\varphi(i)}{ \Delta t(i)}, \tag{39}\]  
\[\ddot{\varphi}(i)=\frac{\Delta\frac{\Delta\varphi(i)}{\Delta t(i)}}{\Delta t( i)}, \tag{40}\]  
Figure 4: Scheme of the designed trajectory tracking controller with decoupling.  
So far, the input of the inverse system, the target motion states of the vehicle \(\mathbf{U}_{ref}=\left[a_{x},a_{y},\tilde{\varphi}\right]^{T}\), has been obtained.
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 2 System and Channel Modeling
### Channel Modeling  
During the optical signal propagation from the UAV to FSO receiver over the atmospheric channel, the amplitudes and phases are distorted by different interferences such as absorption, scattering and refraction. In this study, we consider the combined effects of three primary parameters characterizing an UAV/FSO channel on the received signal intensity including (i) the atmospheric attenuation which causes the link loss \(X_{l}\); (ii) the atmospheric turbulence causes random intensity fluctuation \(X_{a}\); and (iii) the geometric and pointing error due to the UAV's fluctuation \(X_{pa}\). Hence, the combined channel coefficient \(X\) can be given by the following equation [7]  
\[X=X_{l}X_{a}X_{pa}. \tag{10}\]
## 5 Investigation and Results
### Memory Demand  
Given the degree distribution from [3], an information bottleneck decoder can be constructed as proposed in Section 3. We designed the decoder to perform at most \(i=50\) iterations. That is, \(i=50\) different tables for each node degree and node type have to be generated. For the considered code, Table 2 summarizes the memory demand. Clearly, the direct implementation with only one look-up table per iteration and node degree is not implementable due to the huge memory requirement of \(104.6\cdot 10^{24}\) kByte. The sequential approach proposed in [10] already yields a manageable complexity and allows the construction of an information bottleneck decoder that only needs \(384\) kByte of memory in total. Please note that due to very simple look-up operations leveraged for decoding, other than in conventional LDPC decoders, not the computational complexity but the memory demand and the memory access times are the only parameters affecting the space complexity and decoding throughput. Thus, it is crucial that the number of look-ups is reduced and parallelized to simultaneously tackle decoding speed and memory demand. Table 2 shows that the proposed tree-like node structure alsoachieved notable improvements for practically relevant codes. In total, only 134.4 kByte memory were required to realize the entire information bottleneck decoder. This is a reduction of 65%. Furthermore, the number of distinct look-up tables for the node with highest degree (i.e., the check node with \(d_{c}=23\)) could be reduced from 21 to 6, which is a reduction by 71%. Table 2 also includes the total number of look-ups needed to compute all outgoing messages per node and iteration. Clearly, the tree-like structure needs significantly less operations compared to the sequential structure, which enhances the decoding speed correspondingly.
## 3 Methodology
### FFSAR L1A to FFSAR L1B Processing
In this case, echo returns can come from reflecting surfaces away from nadir. In other words, at every time bin, there is a radius of a certain distance from the satellite from which the return could originate. This angle can be determined by operating with a second antenna to the satellite and using interferometry, as the Surface Water and Ocean Topography (SWOT) and the CryoSAT-2 satellites do [34]. Without the additional information provided by the second antenna, it is impossible to know from which angle the echo returns, unless further assumptions are made. Hence, the use of water masks is essential. They allow us to identify at which satellite locations we can expect water to be at nadir. For example, Figure 3 includes two vertical dashed red lines, which delimit the latitudes at which we assume the satellite was flying over the water of the Itoiz Reservoir on 17 July 2023. Figure 3: Radargram acquired through FFSAR processing over Itoiz Reservoir on 17 July 2023. The dashed red lines delineate the latitudinal limits within the associated water mask.
## 4 Ground Directional Pre-Grouting Reinforcement Technology
### Key Technical Processes
#### 4.3.3 Key Technology of Grouting  
1. Slurry density selection: first, the slurry is used for trial injection to understand the size of the hole section grouting volume and the change in orifice pressure; then, the slurry concentration is adjusted.
2. Grouting pressure: Grouting pressure is the power source of grout diffusion in cracks, which should be adapted to the formation conditions, grout performance, and the required diffusion distance. When the grouting pressure is too small, the designed  
Figure 10: Grouting pressure evolution law of various grouting hole arrangement forms.  
growing diffusion distance cannot be achieved, and the grouting effect is poor. When the grouting pressure is too large, the diffusion distance is too large, resulting in unnecessary waste. Grouting adopts the grouting method of a 'constant pressure variable'; that is, the fixed grouting pump volume is selected, and the slurry density is adjusted to make the actual grouting pressure \(\geq\) the design grouting pressure, and the grouting pump volume is adjusted from large to small.
3. The criterion for the end of grouting: when the grouting pressure reaches 1.5 to 2.5 times the hydrostatic pressure, the pump volume is less than 60 L/min, and the stabilization time reaches 30 min, so the grouting process is completed.
4. Hole sealing: after the grouting of all branch holes, a single liquid cement slurry is used for grouting and sealing.  
The ground directional pre-grouting reinforcement technology has a good effect on the shield roadway driving through the fault fracture zone, but this technology requires accurate geological exploration reports and directional drilling-related equipment.
# Contingent Task and Motion Planning under Uncertainty for Human-Robot Interactions
## Related Work
### Task and Motion Planning under Uncertainty  
There are some situations in which a robot has incomplete information about its manipulation environment; therefore, it needs to plan under uncertainty. Task planning under uncertainty is a well-established field in Artificial Intelligence. Conditional-based task planners can provide conditional plan to cope with uncertain information when either the initial state is not completely known, or the result of actions are nondeterministic. There are various classes of planning in this field like conformant, contingent, or probabilistic planning.  
Conformant planning looks for plans under given uncertainty concerning the start state and the effects of symbolic actions, assuming no sensing capabilities during the execution of the plan. The plan should be successful regardless of which is the start state. Contingent planning also considers uncertainty regarding the start state and the effects of actions. However, it can provide some sort of observation over a conditional plan in execution. Probabilistic planning does planning under probabilistic uncertainty regarding the start state and the effects of actions.  
More details on some approaches following conditional-based task planning are commented next as we are interested in this type of planner due to its feature of providing observation over a conditional plan. Some conditional task planners are _Contingent-FF_[1], _POND_[20], and _PKS_[21]. They plan in the belief space and compute conditional plans in the offline mode, which are guided by the result of sensing actions. On the other hand, there are some conditional task planners like _K-Planner_[22], _SDR_[23], and _HCP_[24] solving conditional plans online. Although these planners can prune some branches by considering online sensing actions, satisfying the goal of task may not be possible and the planners may face with dead-end even if there is a solution.  
The concept of contingent-based task and motion planning has also emerged. For instance, the _Planning with Knowledge and Sensing (PKS)_ planner considers incomplete information and performs contingent planning [25] in two main scenarios, using _force sensing_ and _visual sensing_. In a similar direction, offline-based hybrid conditional task and motion planning has been proposed [26], i.e., task planning is foremost performed, and then geometric evaluation is considered by incorporating low-level feasibility checks inside conditional planning (assuming that actuation actions are deterministic). On the contrary, the approach proposed here interweaves simultaneously efficient geometric reasoning inside the task planning process to provide geometrically feasible plans. The approach also copes with collaboration between the mobile robot and a human operator to perform a manipulation task.
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## Appendix A Beamforming Output of a Circular Region  
The distance \(R_{c}\) in Equation (2) can be approximated as:  
\[\begin{array}{l}R_{c}=\sqrt{{r_{s}}^{2}+{r}^{2}-2{r}_{s}r\cos(\phi_{c}-\phi) }\\ \cong{r}_{s}-r\cos(\phi_{s}-\phi),\quad\text{if }r_{s}\gg a.\end{array} \tag{1}\]  
The desired field in free field \(\widetilde{P}_{f}(r,\phi)\) can also be approximated as:  
\[\begin{array}{l}\widetilde{P}_{f}(r,\phi)=A\frac{\exp(ikR_{c})}{R_{c}}\\ \cong A\frac{\exp(ikr_{s})}{r_{s}}\exp(-ikr\cos(\phi_{0}-\phi)).\end{array} \tag{2}\]  
Inserting Equations (1) and (2) into Equation (7):  
\[\begin{array}{l}\widetilde{B}(\phi_{c})=\frac{A}{r_{s}^{2}}\int_{0}^{2\pi} \int_{0}^{a}\exp(ikr[\cos(\phi_{c}-\phi)-\cos(\phi_{0}-\phi)])2\pi rdrd\phi\\ =\frac{A}{r_{s}^{2}}\int_{0}^{2\pi}\int_{0}^{a}\exp(ikr\Big{[}2\sin\Big{(} \frac{\phi_{c}+\phi_{0}}{2}-\phi\Big{)}\sin\Big{(}\frac{\phi_{0}-\phi_{c}}{ 2}\Big{)}\Big{]}\Big{)}2\pi rdrd\phi.\end{array} \tag{3}\]  
\(\phi\) can be substituted by \(\phi^{\prime}=\frac{\pi}{2}-\frac{\phi_{s}+\phi_{0}}{2}+\phi\):  
\[\begin{array}{l}\widetilde{B}(\phi_{c})=\frac{A}{r_{s}^{2}}\int_{0}^{a}\int _{0}^{2\pi}\exp\Big{(}i2 kr\sin\Big{(}\frac{\phi_{0}-\phi_{c}}{2}\Big{)}\cos \phi^{\prime}\Big{)}d\phi^{\prime}2\pi rdr\\ =\frac{8\pi A}{r_{s}^{2}}\int_{0}^{a}\int_{0}^{\pi/2}\cos\Big{(}2 kr\sin\Big{(} \frac{\phi_{0}-\phi_{c}}{2}\Big{)}\cos\phi^{\prime}\Big{)}d\phi^{\prime}rdr. \end{array} \tag{4}\]Using the properties of the Bessel function \(J_{n}\), this equation is reduced as:  
\[\begin{array}{l}\widetilde{B}(\phi_{c})=\frac{8\pi A}{r_{s}^{2}}\int_{0}^{a}J_{0 }\left(2 kr\sin\left(\frac{\phi_{0}-\phi_{k}}{2}\right)\right)rdr\\ =\frac{8\pi A}{r_{s}^{2}}\Bigg{[}\frac{r^{2}h\left(2 kr\sin\left(\frac{\phi_{0}- \phi_{k}}{2}\right)\right)}{2 kr\sin\left(\frac{\phi_{0}-\phi_{k}}{2}\right)} \Bigg{]}_{0}\\ =\frac{8\pi a^{2}}{r_{s}^{2}}\frac{h\left(2 ka\sin\left(\frac{\phi_{0}-\phi_{k}} {2}\right)\right)}{2 ka\sin\left(\frac{\phi_{0}-\phi_{k}}{2}\right)}\end{array} \tag{10}\]
## 2 Design and Methods
### Design Principle of the Wireless Vibration Sensor  
According to the requirements that the tractor should be tested quickly and accurately before leaving the factory, a wireless vibration sensor should be characterized by wireless transmission, magnetic suction, and self-powered operation. Furthermore, the vibration sensor designed for collecting vibration data should have a data conversion function and be able to convert the vibration acceleration generated by the tractor in an idle state into velocity and displacement through calculus processing. This conversion capability is crucial for a better understanding of the tractor's vibration behavior, which can significantly contribute to the assessment of its assembly quality.  
Based on the above requirements and references [26], the present research team collaborated with Shanghai Zhendi Testing Technology Co., Ltd. (Shanghai, China) to jointly design and develop a wireless vibration sensor. The wireless vibration sensor is shown in Figure 2. The designed vibration sensor consists of an STM32F103 microcontroller-based control unit, MEMS chip-based acquisition unit, Bluetooth communication module-based wireless transmission unit, and other components. Signal processing is primarily handled by the STM32 and its peripheral circuits, which serve as the processing unit for the three-axis sensor and also act as the primary control unit. The processed data are transmitted wirelessly via the Bluetooth module, which is the central component of the wireless data  
Figure 1: Design principle of the wireless measuring system for vibration test.  
transmission unit. The sensor operates on an internal 3.7 V lithium battery and features a Micro-USB interface for convenient charging. Additionally, it includes essential circuits for voltage regulation, buck-boost conversion, protection, and voltage detection to ensure reliable and safe operation.  
The designed vibration sensor is capable of not only collecting vibration data, but also realizing spectrum analysis and statistical functions on vibration data. Moreover, the data collected by the sensor could be wirelessly transmitted, enhancing the efficiency and convenience of the testing process. This innovative approach streamlined the data collection and analysis  
The sensor could collect vibration acceleration signals, including vibration speed signals, and vibration displacement signals. It was essential for the test system to be able to measure vibration velocity, which was used to determine the vibration severity. The measurement range of vibration acceleration of the sensor was 0.1-200 mm/s. The accuracy of the sensor was \(\pm\)5%. The response range of the vibration acceleration frequency of the sensor was 10 Hz-5 kHz. The calibration of the sensor was carried out by the Wuxi Institute of Metrology and Testing, Wuxi, China.
## 3 Methods
### Integration of Global Algorithm D* with KDDPG  
In large-scale dynamic environments, the DDPG model is limited by its perception range and lacks the utilization of global information, which can lead to redundant actions and getting trapped in local optima. This paper integrates a global path-planning algorithm named D* algorithm with KDDPG to enhance navigation efficiency and safety. The D*-KDDPG algorithm represents a typical two-layer path planning structure that integrates global and local path planning. The global path planner uses the D* algorithm to make high-level path decisions based on a known map, providing long-term goals and a route framework for the local path planner. Guided by the global path, the local path planner KDDPG refines and adjusts the path in real-time, responding quickly to environmental changes and avoiding unexpected obstacles. This two-layer structure combines the efficiency of global planning with the flexibility of local planning, enabling the path planning process to adapt to complex environmental changes while maintaining the pursuit of the overall goal. The pseudo-code for this integration is presented in Algorithm 1.  
```
0: Map: 2D Grid Map, S: Start position, G: Goal position, S*(x, y): Current robot coordinates, \(d_{goal}\): The distance of robot to goal, \(\epsilon\): Reference-Goal threshold, \(\delta\): Goal-Reach threshold, KDDPG_Move: Pre-trained KDDPG model, MAXTIME: Timeout threshold.
0: Path to the Goal position
1: function D*-KDDPG (Map, S, G, S*(x, y), \(d_{goal}\), \(\epsilon\), \(\delta\), KDDPG_Move, MAXTIME)
2: Global Path \(\leftarrow\) D_star(Map, S, G)
3: StartTime \(\leftarrow\) GetCurrentTime()
4: for G' in Global Path do
5: while\(d_{goal}>\delta\) do
6: Compare \(\sqrt{\left(x-G^{\prime}.x\right)^{2}+\left(y-G^{\prime}.y\right)^{2}}\) with \(\epsilon\)
7: ReferenceGoal \(\leftarrow\) Select the point closest to threshold \(\epsilon\)
8: KDDPG_Move (S', ReferenceGoal)
9: TimeElapsed \(\leftarrow\) CurrentTime-StartTime
10: if TimeElapsed > MAXTIME or Collision then
11: return Fail to reach Goal
12: end if
13: end while
14: if\(d_{goal}<=\delta\) then
15: return Success to reach Goal
16: end if
17: end for
18: end function
```  
**Algorithm 1** D*-KDDPG path-planning algorithm  
Algorithm 1 presents three stages of the D*-KDDPG algorithm. Firstly, the D* algorithm generates a global path based on a known 2D map. The generated path composes a sequence of path nodes connecting the robot's initial position and the global goal. Next, for each stage of local path planning, D*-KDDPG selects a path node from the global path as a local reference goal. The path nodes are selected as candidates if their distances to the robot's current locations are smaller than a given threshold \(\epsilon\), and the nearest one is selected as the local reference goal, accordingly. The global goal becomes the current reference goal if no candidate arises. Finally, KDDPG drives the robot toward the selected reference goal and completes the local path planning. The process continues until the robot achieves the global by following the global path generated by D*.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 3 Preliminaries and Methods  
### Definitions  
#### 3.1.1 Event  
For a business process, let \(A\) denote the set of all activities; let \(T\) denote the set of occurrence times corresponding to all activities; and let \(M\) denote the set of attributes corresponding to all activities. An event can be denoted as \(e=\left(a,t,m^{(1)},\cdots,m^{(k)}\right)\), where \(a\in A\) denotes the activity executed in the event; \(t\in T\) denotes the time at which the event occurred; and \(m^{(j)}\in M\) (\(1\leq j\leq k\)) denotes the \(j\)-th attribute of the event, and usually there are multiple attributes of an event such as executor, execution cost, etc. Event attributes can be further classified into discrete and continuous attributes according to the range of values they take; for example, the executor attribute of an event is discrete, and the execution cost attribute is continuous.