esa-sceva/satcom-chunk-collection huggingface dataset:
# Computer Vision in Autonomous Unmanned Aerial Vehicles--A Systematic Mapping Study
## 3 Results
### RQ4: What Are the Characteristics of the Vision System?
#### 3.4.1 Distribution and Annual Trend per Vision System  
The distribution of these types of vision systems as regards the database is shown in Figure 14, in which the predominance of monocular systems is clear, with a total of 116 papers representing 80% of the papers. Secondly, stereo systems are present in 20 papers, representing 14%. The remaining 6% is divided between multi-camera (2) and multi-camera (4) systems with 7 and 1 papers, respectively.  
\begin{table}
\begin{tabular}{l c c l} \hline
**UAV Class** & **N** & **\%** & **Papers** \\ \hline
**Rotatory-Wing**: Helicopter & **29** & 20.14\% &
\begin{tabular}{l} [43,54,73,83,84,86,87,89,91,104-106,112,115-118,120,121, \\ 127,141,142,144,148,152,163,170,175,176] \\ \end{tabular} \\
**Rotatory-Wing**: Multirotor & **7** & 4.86\% &
\begin{tabular}{l} [42,63,68,131,138,169,172] \\ [17,22,24,25,28,29,32,34,35,44,58-62,69,71,72,74,80-82,85, \\ 88,92-95,97,99,102,107,109-111,114,119,122,126,128-130, \\ 165,168,173,177] \\ \end{tabular} \\
**Fixed-Wing** & **34** & 23.61\% &
\begin{tabular}{l} [42,63,68,131,138,169,172] \\ [17,22,24,25,28,29,32,34,35,44,58-62,69,71,72,74,80-82,85, \\ 88,92-95,97,99,102,107,109-111,114,119,122,126,128-130, \\ 132,135,136,139,143,145,146,149-151,153,159,160,162,164, \\ 165,168,173,177] \\ \end{tabular} \\
**Rotatory-Wing**: Sit-On-Tail VIOL & **1** & 0.69\% &
\begin{tabular}{l} [23,27,30,31,33,38,41,53,57,64-66,75,90,96,98,103,108,113, \\ 123,124,133,134,137,140,147,154-157,161,166,167,174] \\ \end{tabular} \\
**Fixed-Wing**: Blended Wing-Body & **2** & 1.39\% &
\begin{tabular}{l} [40,125] \\ [39] \\ \end{tabular} \\
**Airship**: Blimp & **1** & 0.69\% &
\begin{tabular}{l} [51] \\ [100,101,158,171] \\ \end{tabular} \\ \hline
**Not Specified** & **4** & **2.78\%** &
\begin{tabular}{l} [100,101,158,171] \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 5: List of papers per UAV class.  
Figure 15 shows the annual trend of papers considering the vision system employed. A clear predominance of monocular vision systems can be observed again. Moreover, the graph shows a remarkable growth trend for these monocular systems. In the case of stereo, the growth is not clear. However, some increase can be observed in the last few years' research (since 2014). This data may suggest that the lower use of stereo systems can be due to their later development. Regarding the rest of multi-camera configurations, it is not possible to draw relevant conclusions as a result of the reduced number of papers focused on these systems.
## 3 Results and Analysis  
The modeling for the DG sizing and siting problem and the multi-objective solution methodology (MOHGA) proposed in this work are applied in two case-studies and main results are analyzed in graphs and tables. Case-studies are carried out in medium voltage systems: 69-bus test system known from literature (Case Study I) and a 918-bus Brazilian distribution system (Case Study II). Simulations were performed on a 2.21 GHz Intel Core i7-8750H CPU using MATLAB 2018b (9.5.0.94444). The elapsed time for Case Study I was 500 s and 4490 s for Case Study II, both with \(Popsize=100\) and 500 iterations.
## 6 Conclusions  
In this study, weather radar was considered as a special wireless communication device. Via the derivation of the radar range equation, the transmission loss of radar clear-air echoes was calculated in order to establish a relationship with the troposcatter. The results indicate that the radar transmission loss is in accordance with ITU-R recommendations, with ITU-R P.2001 demonstrating greater consistency with radar loss than ITU-R P.617. Additionally, a linear regression relationship exists between the transmission loss (in dB) and the altitude. The result of the study shows that most clear-air echoes are caused by tropospheric scattering due to turbulence. The scattering of clear-air echoes is consistent with troposcatter theory. The theory of tropospheric scattering is complementary to the theory of Bragg scattering. Tropospheric scattering can be thought of as general Bragg scattering.  
We also found that some echoes do not comply with the recommendations of the ITU-R and are more likely to be caused by biological activity. This provides a new approach by which to recognize biological echoes.  
The connection between wireless communication and atmospheric science has been established using the theory of troposcatter. However, there are still many issues that need to be addressed. The geometrical structure of the troposcatter, especially incoherent reflection by irregular layers, remains obscure. The scattering characteristics of microscale air mass (centimeter level) under the influence of meteorological background effects poses great challenges. This may be an opportunity for exploring the microscale atmospheric structure. A more objective and comprehensive study of troposcatter echoes can effectively promote the progress of atmospheric physics.  
Conceptualization, Y.T. and S.M.; methodology, Y.T.; software, Y.T.; validation, Y.T.; formal analysis, Y.T.; investigation, Y.T.; resources, S.M. and H.C.; data curation, L.W., Y.X. and S.L.; writing--original draft preparation, Y.T. and T.L.; writing--review and editing, Y.T. and T.L.; visualization, Y.T. and T.L.; supervision, S.M., H.C. and L.W.; project administration, Y.T., S.M. and H.C.; funding acquisition, Y.T., S.M. and H.C. 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, grant number 42205145.  
Data Availability Statement: Data available on request due to legal.  
The authors declare no conflicts of interest. The funders had no role in the design of this study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Design AGV Scheduling Strategies  
### AGV Selection Strategy  
When a production task arrives, the scheduling system selects an AGV for logistics transport based on the task's specific requirements. The system first checks the current status of all AGVs to determine if there are any idle vehicles available for the task. If no idle AGVs are available at the time, the system will update the system time to reflect the earliest idle AGV across all queues. For the selection of an idle AGV, different strategies can be followed to optimize the scheduling process and ensure timely task completion. These strategies can include Random Choice (RC), Sequential Choice (SC), and Nearby Choice (NC), each of which offers distinct advantages depending on the production environment and task characteristics.  
(1) In the RC strategy, the system queries the idle AGV queue and randomly selects an AGV to perform the task. This strategy helps balance the workload across AGVs and avoid overburdening a single AGV. If no idle AGVs are available, the system updates the time to the earliest idle AGV in all queues, ensuring that the task begins as soon as an AGV is available.  
(2) In the SC strategy, the system selects the first idle AGV from the queue to perform the task. This strategy follows a first-come-first-served approach, ensuring that AGVs are assigned tasks in the order they become available. If no idle AGVs are found in the queue, the system updates the time to the earliest standby time point across all AGV queues, ensuring timely task initiation.  
(3) In the NC strategy, the system selects the idle AGV closest to the task's starting point. This strategy minimizes travel time, especially for tasks involving long distances. If no idle AGVs are available, the system updates the time to the earliest standby point across all AGV queues, ensuring that the scheduling is based on the earliest availability of any AGV.  
Each of these strategies offers different benefits depending on the nature of the tasks and the distances between workstations. Choosing the right strategy can optimize AGV utilization, reduce task completion time, and maintain a smooth production flow.
## 2 Materials and Methods
### System Setup  
The experiment schematic diagram of PAS is depicted in Figure 1. A near-infrared DFB (Distribute Feedback, NEL, Tokyo, Japan) laser with a center wavelength of 1683 nm and a light power of 24.5 mW was selected as the light source. A laser driver (ITC102, Thorlabs, Newton, USA) was employed to precisely control the current and temperature of the laser to cover the absorption line of CHCl\({}_{3}\) at 1683 nm. An analog modulation signal combined with a high-frequency sine wave and a low-frequency sawtooth wave was generated by a DAQ card (USB-6211, National instrument, Austin, TX, USA) and imposed on the laser driver. The space light emitted from the pigtail of the laser was collimated by a collimator (F280 APC-1550, Thorlabs, Newton, USA) and the beam was converged in the center of the PA cell through a quartz lens (_f_ = 100 mm) coated with near infrared anti-reflective coating. To reduce the intensity loss of the laser, the windows of the PA cell were made of CaF\({}_{2}\) with a strong transmittance (>90%) in the near-infrared band. The resonant cavity of the PA cell made by stainless steel has a resonate frequency around 6390 Hz, as shown in Figure 2. Different from the quartz tuning fork, the resonant cavity has a wide frequency response range. The resonant cavity we used had a slight response difference around 6390 Hz.  
Around the middle part of the resonant cavity, eight microphones (FG-23329-P07, Knowles, Itasca, USA) were evenly distributed to receive the sound signals produced by the chloroform gas during the measurement. A pair of Mass Flow Controllers (MFCs) with a maximum flow rate of 1 L/min and an error of 1 mL/min were used to mix the chloroform gas with the pure nitrogen. The buffer zones on the edge of two sides were used to reduce the sound interference caused by the gas flow. The PA signal was processed through a home-made preamplifier which possesses a high pass filter and a function of signal amplification at the same time. A dual-phase lock-in amplifier (LIA-BVD-150-L, Femto, Berlin, Germany) with a sinusoidal reference signal given by the DAQ card was used for further demodulation. Finally, the signal was transmitted to the laptop for display through a program written using the LabVIEW software.
# A Roadmap for the Integration of Active Solar Systems into Buildings
## 2 Literature Review
### Prototype Systems  
There are also examples of research teams that have resolved the construction issues arising from the integration of prototype active solar systems into buildings. In this context, work was done during COST Action TU1205--BISTS [58] by five research teams that developed five different solar systems, the Modular Intergraded Solar/Thermal Flat Plate Collector, the Hybrid Photovoltaic/Solar Thermal (HyPVT), the Concentrating Photovoltaic/Thermal Glazing (CoPVTG), the Solar Plenum and the Trapeze Flat-Plate Solar Thermal Collectors (FPSTC), and proposed viable solutions for building integration (Figure 1). For each system, a specialised design and fabrication solution was adopted that focused on the sustainable operation of both the system and the building.
###### Abstract  
A Mueller-Muller Phase Detector (MM PD) technology based on duo-binary four-level pulse amplitude modulation (DB PAM-4) with low complexity and high phase-detection density is presented. The proposed low complexity includes low phase-detection complexity and low space complexity of data processing. The waveform sifting technology simplifies 175 specific waveform changes into five fuzzy waveform change trends, reducing the complexity of subsequent phase detection. By making the data sample before the waveform sifting, the data bit width is reduced from 8 bit to 3 bit, which realizes data dimensionality reduction, greatly reduces the scale of subsequent auxiliary data, reduces the number of basic devices by 13.7%, and reduces the spatial complexity of data processing. The coherent coding of DB PAM-4 combined with waveform sifting increases the phase-detection density from 50% to 65% and improves both phase-detection density and phase-detection gain by 30%, and improves the jitter tolerance. Through the simulation of the clock and data recovery (CDR) model built by Cadence, the fast locking capability of CDR is verified.  
clock and data recovery; Mueller-Muller phase detector; low space complexity; low phase-detection complexity; duo-binary four-level pulse amplitude modulation; high phase-detection density; waveform sifting 2022
# Pushover Analysis on Infill Effects on the Failure Pattern of Reinforced Concrete Frames
## 3 Results and Discussions
### Required Ratio of Column to Beam Strength  
The required ratio of column to beam strength is defined in Equation (18). The calculated results of each joint are shown in Table 5.  
\[\eta=\frac{\sum M_{c}}{\sum M_{b}} \tag{18}\]  
where \(\sum M_{c}\) = sum of moment of columns calculated at the 2% story drift; and \(\sum M_{b}\) = sum of moment of beams calculated at the 2% story drift. Where the slab is in tension under moments at the face of the joint, slab reinforcement within an effective slab width can be calculated with the proposed equations above.  
It can be seen that the required ratio of column to beam strength is smaller than 1.0 with full-filled infill, which demonstrated that the infilled wall has changed the behavior of the frame. The required ratio considered slab is 16-70% larger those ignored slab. In other words, the required ratios of Joint 2 of axis A and Joint 4 of axis B, when the slab is ignored, are 1.48 and 1.28, respectively. When the slab is taken into consideration, the values change to 1.13 and 0.76, respectively. Thus, if the effective width  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Axis A Beam** & **Axis B Beam** & **Axis C Beam** \\ \hline \multirow{3}{*}{Effective slab width (\(b+b_{\theta}\)) /mm} & FEM & 250 & 756 & 205 \\  & ACI 318-14 [27] & 300 & 700 & 300 \\  & [PERSON] et al. [28] & 300 & 500 & 300 \\  & [PERSON] et al. [29] & 300 & 500 & 300 \\ \hline \end{tabular}
\end{table}
Table 4: Effective slab width (mm).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Axis A** & **Axis B** & **Axis C** \\ \cline{2-5}  & **Joint 1** & **Joint 2** & **Joint 3** & **Joint 4** & **Joint 5** & **Joint 6** \\ \hline Sum of column moment/kNm & 4.5 & 8.9 & 7.2 & 9.3 & 2.0 & 5.9 \\ Sum of beam moment (consider slab)/kNm & 10.2 & 10.4 & 12.7 & 13.4 & 9.0 & 9.5 \\ Sum of beam moment (ignore slab)/kNm & 7.9 & 8.0 & 7.6 & 8.0 & 7.7 & 7.9 \\ Moment of column-to-beam (consider slab) & 0.61 & 1.13 & 0.10 & 0.76 & 0.68 & 0.81 \\ Moment of column-to-beam (ignore slab) & 0.80 & 1.48 & 0.17 & 1.28 & 0.79 & 0.97 \\ \hline \end{tabular}
\end{table}
Table 5: Required ratio of column to beam strength.  
Figure 13: Stress of the slab reinforcement.  
is ignored, the amplified factor in the column may still not be enough to achieve a \"strong column weak beam\". The effective slab width should take infills into consideration and the effective width should take the infill type into account.
## 3 Methodology
### Physics-Informed DeepONet
The new network requires additional training of the weights and biases of the two transformer networks compared to the FNN. Figure 4 illustrates the architecture of the new network. Figure 3: Architecture of physics-informed DeepONet regularizing geometric differential equations. The training dataset of the DeepONet is a triplet \(\left[e,x,\mathbf{w}(x)\right]\) which takes the following form:  
\[\left[ \begin{bmatrix}\vdots\\ \varepsilon_{i}\left(x^{(1)}\right),\varepsilon_{i}\left(x^{(2)}\right),\cdots, \varepsilon_{i}\left(x^{(m)}\right)\\ \varepsilon_{i}\left(x^{(1)}\right),\varepsilon_{i}\left(x^{(2)}\right), \cdots,\varepsilon_{i}\left(x^{(m)}\right)\\ \vdots\end{bmatrix} , \begin{bmatrix}\vdots\\ x^{(1)}\\ x^{(2)}\\ x^{(P)}\\ \vdots\end{bmatrix} , \begin{bmatrix}\vdots\\ \mathbf{w}_{i}\left(x_{1}\right)\\ \mathbf{w}_{i}\left(x_{2}\right)\\ \vdots\\ \end{bmatrix} \right]\right. \tag{17}\]  
in which \(\left\{\varepsilon_{i}\right\}_{i=1}^{N}\) denote \(N\) separate strain functions, \(\left\{x_{j}\right\}_{j=1}^{p}\) denote \(P\) coordinates in the domain of \(G_{\theta}(\mathbf{\varepsilon}_{i})\), and \(\mathbf{w}_{i}(x_{j})\) is the corresponding true displacement observed at \(x_{j}\). The loss of the DeepONet during training in the form of mean square error can be expressed as follows:  
\[L_{d}(\theta)=\frac{1}{NP}\sum_{i=1}^{N}\sum_{j=1}^{p}\left|G_{\theta}(\mathbf{ \varepsilon}_{i})\left(x_{j}\right)-\mathbf{w}_{i}(x_{j})\right|^{2} \tag{18}\]  
where \(L_{d}(\theta)\) is the loss determined by paired strain-displacement observations, called data-driven loss. As a purely data-driven network, the prediction error and generalization error of the DeepONet heavily depend on the quantity and quality of the training data. To eliminate the necessity for training data, GDEs can be formulated as the loss terms of the DeepONet used to construct the PI_DeepONet, as shown in Figure 3.  
To construct the PI_DeepONet, let \(\mathbf{\varepsilon},x\) be the inputs of the branch net and trunk net of the DeepONet, respectively, and \(G_{\theta}(\mathbf{\varepsilon})(x)\) be the output of the DeepONet; then, the physical constraint residuals generated by the lack of satisfaction of the GDEs can be expressed as follows:  
\[R_{p}(\theta)(x,\mathbf{\varepsilon})=\left|(x,\mathbf{\varepsilon}(x),G_{\theta}( \mathbf{\varepsilon})(x))\right| \tag{19}\]  
\[R_{N_{i}}(\theta)(x,\mathbf{\varepsilon})=\left|\mathcal{N}_{i}(x,\mathbf{\varepsilon }(x),G_{\theta}(\mathbf{\varepsilon})(x))\right|,\;i=1,\ldots,H \tag{20}\]  
\[R_{D}(\theta)(x,\mathbf{\varepsilon})=\left|\mathcal{D}(x,\mathbf{\varepsilon}(x),G_ {\theta}(\mathbf{\varepsilon})(x))\right| \tag{21}\]  
in which \(R_{p}\), \(R_{N_{i}}\), and \(R_{D}\) are the residuals generated by the unsatisfaction of the strain-displacement relation, Neumann boundary conditions, and Dirichlet boundary conditions defined in GDEs, respectively.
## 4 Energy Consumption
### Average Energy Consumed by RN1 during Awake Cycles
#### 4.4.1 Average Energy Consumed by RN1 in Type 1A Cycles  
Let \(E_{tx,s}^{aw}[1A]\), \(E_{tx,f}^{aw}[1A]\) and \(E_{oh}^{aw}[1A]\) be the average energy consumed by RN1 in type 2A cycles when RN1 completes a successful PF transmission, a PF from RN1 collides, and RN1 overhears PF transmissions from other C1 SNs, respectively.  
These RN1 energy consumption terms can be determined as,  
\[\begin{split} E_{tx,s}^{aw}[1A]&=\sum_{i=1}^{Q_{1}} \sum_{k=1}^{M_{1}}\pi_{1}(i,k)P_{s,k,1}\Big{[}E_{1}^{aw,1}-(BT_{s,k,1}+\pi_{1} \,t_{DATA})P_{rx,1}\Big{]}\,,\\ E_{1}^{aw,1}&=\big{[}T-T_{sc,1}-\big{(}t_{RTS}+t_{CTS }+t_{ACK}+4D_{p}\big{)}\big{]}P_{rx,1}\,.\end{split} \tag{8}\]  
\[\begin{split} E_{tx,f}^{aw}[1A]&=\sum_{i=1}^{Q_{1}} \sum_{k=1}^{M_{1}}\pi_{1}(i,k)P_{f,k,1}\Big{[}E_{2}^{aw,1}-BT_{f,k,1}P_{rx,1} \Big{]}\,,\\ E_{2}^{aw,1}&=\big{[}T-T_{sc,1}-\big{(}t_{RTS}+2D_{p }\big{)}\big{]}P_{rx,1}\,.\end{split} \tag{9}\]  
\[\begin{split} E_{oh}^{aw}[1A]&=\sum_{i=1}^{Q_{1}} \sum_{k=1}^{M_{1}}\pi_{1}(i,k)kP_{s,k,1}\Big{[}E_{3}^{aw,1}-\widehat{f}_{k,1} \,t_{DATA}(P_{rx,1}-P_{sl})\Big{]}\\ &+\sum_{i=1}^{Q_{1}}\sum_{k=2}^{M_{1}}\pi_{1}(i,k)\widehat{P}_{ f,k,1}(T-T_{sc,1})P_{rx,1}\,,\\ E_{3}^{aw,1}&=(T-T_{sc,1})P_{rx,1}-\big{(}t_{CTS}+t_ {ACK}+3D_{p}\big{)}(P_{rx,1}-P_{sl})\,,\\ \widehat{P}_{f,k,1}&=\frac{\
u_{1}}{i=1}\Bigg{[} \sum_{n=2}^{k}\binom{k}{n}\bigg{(}\frac{1}{W_{1}}\bigg{)}^{n}\bigg{(}\frac{W_{ 1}-i}{W_{1}}\bigg{)}^{k-n+1}\Bigg{]}\,,\\ \widehat{f}_{k,1}&=\frac{1}{G_{k,1}}\sum_{i=1}^{Q_{1 }}\pi_{1}(i,k)\,,\ \ G_{k,1}=\sum_{i=1}^{Q_{1}}\pi_{1}(i,k)\,.\end{split} \tag{10}\]  
where \(T\) is the cycle duration, \(\widehat{P}_{f,k,1}\) is the probability that PFs transmitted by other SNs, different from RN1, collide in cycles where RN1 is active but loses the contention, \(\widehat{f}_{k,1}\) is the average PF size transmitted by C1 SNs, conditioned on RN1 being contending with other \(k\) C1 SNs in the same cycle, and \(P_{sl}\) is the power level in _sleep_ mode [23]. Please note that \(\widehat{f}_{k,1}\) is obtained to determine the average time that RN1 will remain in the _sleep_ mode awaiting the end of the PF transmission. Note also that \(\widehat{f}_{k,1}=1\) when the SPT scheme is deployed.  
Then, the average energy consumed by RN1 in type 1A _awake_ cycles is  
\[E_{aw}[1A]=E_{tx,s}^{aw}[1A]+E_{tx,f}^{aw}[1A]+E_{oh}^{aw}[1A]\,. \tag{11}\]
## 4 Experiments
### Comparisons with Variable Taining Sample Sizes
Significant improvement on classification accuracy with extremely few training samples  
As shown in Figure 12 and Table 5, under the sceneries of extremely few training samples (size of 4 per fault mode), the proposed TfEn improve the average classification accuracy by 33.27% compared with three mainstream DNN-based methods, and the average classification accuracy is improved by 12.95% compared with four mainstream entropy-based methods. Among all the methods, the DNN-based methods obtain the lowest average accuracy. This is because there are many parameters that need to be learned and updated in their complicated model architectures, and the scarce training samples are not enough to support this, and thus limit their performance. Compared to these DNN-based methods, the existing entropy-based methods present the relatively better adaptiveness to the scarce failure samples, while there is still room for improved performance due to the information loss caused by the ignored domain of time or frequency. To sum up, through jointly taking into account of the nonlinear behaviors from both time and frequency domains, the proposed TfEn-RFE method can extract more intrinsic fault representation and thus achieve the best adaptiveness for the extremely few failure samples. With the help of the proposed method, the classification accuracy is significantly improved compared with the other comparative methods.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Actuation Mechanism
#### 2.1.1 Linear Actuation  
In railway S&C systems comprising linear actuators, such as electromechanical, electropneumatic, and electrohydraulic actuators, the switch rails slide from one side to the other depending on the command. A traditionally used linear electromechanical switch actuation system, as mentioned in [16; 38], is shown in Figure 5a. The motor shaft is connected to a belt drive, which runs the crank through a gearbox. The crank drives the drive or actuator rod, which is connected to the switch rails. The drive rod actuates the switch rails at the toe position to slide from one position to another. A different configuration is seen in an HPSS system: the motor gearbox unit is connected directly to a lead screw, which is connected to the mechanical linkages at the toe through a nut [40]. The rotation of the motor causes linear movement of the nut, which drives the switch rails.  
Figure 5b represents a hydraulic switch actuation system called a clamplock, which consists of two actuating rods between two switch rails that are pushed against adjacent stock rails. The hydraulic pump pushes the actuating rods by providing the force.