esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Workflow Technology for Big Data Analysis Service
## 2 Big Data Analysis Oriented Cloud Workflow System
### Workflow Scheduler  
Workflow scheduler includes time scheduling and workflow dependency. Time scheduling is important in big data analysis, which mainly needs offline analysis periodically and online real-time analysis, such as constructing daily business reports. Workflow dependency means that another process would be triggered after a process is executed. \"Workflow scheduler module\" refers to the scheduling framework Quartz [17] (as shown in Figure 12), which employs Job, Trigger, and Scheduler as three core concepts. Job is an interface. A developer implements an interface definition to execute tasks. JobDetail describes the implemented interface and related static information, such as job name, description, and association with the listener. Trigger is a class that describes the time trigger rules of a triggered job, and it includes SimpleTrigger and CronTrigger. SimpleTrigger applies to trigger execution periodically, and CronTrigger is developed for workflow dependency. Scheduler is a container of workflow scheduling. Trigger and JobDetail can register in the scheduler. Scheduler binds Trigger for JobDetail so that when Trigger is triggered, the corresponding job is executed.  
Figure 11: Lambda architecture.
# Fault-Tolerant Control of Multi-Joint Robot Based on Fractional-Order Sliding Mode
## 3 Fractional-Order Sliding Mode Controller Design
### Proof of Stability  
In order to verify whether the multi-joint robot controlled by the fractional-order sliding mode control law is stable, it is necessary to analyze the stability theoretically. Compared with classical calculus, fractional calculus faces two major problems, namely, non-locality and weak singularity, which makes the research of fractional calculus very difficult. It is also the root cause of the difficulty in the stability analysis of practical problems using fractional calculus indirectly. Therefore, in this paper, the stability of the fractional-order sliding mode controller is verified by the Lyapunov function from the perspective of energy.  
Set the following Lyapunov function as  
\[V_{2}=\frac{1}{2}{S_{2}}^{T}I_{2\times 2}S_{2} \tag{14}\]  
where \(I_{2\times 2}\) is a positive definite constant matrix. The derivative of the above formula is  
\[\begin{array}{ll}\dot{V}_{2}&=\frac{1}{2}{S_{2}}^{T}\dot{I}_{2\times 2}{S_{2}}+{S_ {2}}^{T}I_{2\times 2}\dot{S}_{2}\\ &={S_{2}}^{T}I_{2\times 2}\dot{S}_{2}\\ &={S_{2}}^{T}I_{2\times 2}\big{[}{D_{t}}^{\alpha-1}e+C\dot{e}+\ddot{e} \big{]}\\ &={S_{2}}^{T}I_{2\times 2}\big{[}{D_{t}}^{\alpha-1}(q_{d}-q)+C\big{(}\dot{q}_{d}- \dot{q}\big{)}+\big{(}\ddot{q}_{d}-\ddot{q}\big{)}\big{]}\end{array} \tag{15}\]  
The output torque vector is contained in \(\ddot{q}\) item, and the torque should keep \(\dot{V}_{2}\) negative at non-origin state.  
As \(I_{2\times 2}\) is the identity matrix and combines Formula (13), the following inequality is established.  
\[\begin{array}{ll}\dot{V}_{2}&={S_{2}}\dot{S}_{2}\\ &={S_{2}}\big{(}{D_{t}}^{\alpha-1}\ddot{e}+C\dot{e}+\ddot{e}\big{)}\\ &={S_{2}}\big{(}{D_{t}}^{\alpha-1}\big{(}-CD_{t}^{1-\alpha}\dot{e}-\varepsilon D _{t}^{1-\alpha}\text{tanh}(S_{2})-{D_{t}}^{1-\alpha}kS_{2}\big{)}+C\dot{e}+ \big{(}-CD_{t}^{1-\alpha}\dot{e}-\varepsilon D_{t}^{1-\alpha}\text{tanh}(S_{2} )-{D_{t}}^{1-\alpha}kS_{2}\big{)}\big{)}\\ &\leq{-kS_{2}}^{2}-\varepsilon|S_{2}|\\ &\leq 0\end{array} \tag{16}\]  
According to Lemma 1, the origin is the equilibrium point of the system and is asymptotically stable.
## 3 Results and Discussion
### Flexural Behaviors of Specimens with Carbon Grids
#### 3.2.4 Idealized Load and Mid-Span Deflection of Specimens with Carbon Grids  
Based on the experimental results, Figure 14 illustrates the idealized relationship between the load and mid-span deflection of the carbon grid-reinforced specimens.  
The experimental results indicate that after cracking, the stiffness decreased to approximately 8% or lower than the stiffness before cracking, except for the three specimens reinforced with relatively stiffer carbon grids. Additionally, the maximum load was reached when the stiffness was only marginally recovered, even at the crack stabilization stage. In other words, the load gradually increased owing to the increase in the mid-span deflection with a similar stiffness at the crack formation and stabilization stages even though the stiffness decreased sharply because of the crack occurrence, unlike the behavior observed in flexural members that used textile reinforcement without resin (Figure 2). Therefore, the load and mid-span deflection relationship might be modeled as a bilinear curve in which the stiffness changes around the crack initiation point (Figure 14).  
In accordance with the experimental results, when the strain of the longitudinal strands was less than approximately 80% of the tensile strain under the maximum load, the load was found to decrease in stages as the load resisted by the mortar was transferred to the carbon grid when the bond between the carbon grids and mortar was secured (ii in Figure 14). Conversely, it was found that the load resisted by the mortar could not be transferred to the carbon grid after reaching the maximum load, while the load decreased rapidly when the bond between the mortar and carbon grid was not secured, or strain corresponding to approximately 80% of the tensile strain or higher occurred on the longitudinal strands under the maximum load (i in Figure 14).
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 2 Control Design
### PD Sliding Mode Control (PDSMC)  
In this section, the details of the proposed scheme are discussed, and in the next section, the proposed scheme is integrated with SPO to achieve robust control with faster convergence and better characteristics as compared with SMCSPO.  
As the main idea was to generate a sliding surface which was the sum of the derivative and proportional gain of PID control or to introduce the derivative gain in the structure of the sliding surface as shown below.  
\[s=k_{d}\cdot\dot{e}+k_{p}\cdot e \tag{28}\]  
\(k_{p}\delta k_{d}\) are the proportional and derivative gain where \(e\) is the error as defined in the above section.  
The value of \(k_{p}>0\), \(0<k_{d}<1\). In PDSMC, during the reaching phase, the \(\dot{s}\) dynamics is shown below.  
\[\dot{s}=-K^{\prime}\cdot sat(s)+k_{d}\cdot\psi \tag{29}\]  
In order to fulfill the sliding condition \(s\cdot\dot{s}\leq 0\) the gain \(K^{\prime}\) should be greater than the \(k_{d}\cdot\psi\) as shown below.  
\[K^{\prime}>k_{d}\cdot\psi \tag{30}\]  
As it can be seen in Equation (30) that during the reaching phase, the gain \(k_{d}\) multiplied with perturbation and reduced the effect of perturbation because the value of this gain was \(k_{d}<1\), but greater than 0. Because of this small value of switching gain, Small \(K^{\prime}\) was required as compared with conventional SMC switching gain K. This reduced chattering from the system output and its sliding surface. When the system reached the sliding surface, or within the boundary layer, the \(\dot{s}\) dynamics become as shown below.  
\[\dot{s}=-\frac{K^{\prime}}{\varepsilon_{c}}s+k_{d}\cdot\psi \tag{31}\]  
After taking the Laplace transform, the transfer function of the sliding surface over perturbation is obtained as shown below.  
\[\frac{s}{\psi}=\frac{k_{d}}{p+\frac{K^{\prime}}{\varepsilon_{c}}} \tag{32}\]  
The above equation is a low pass filter between the sliding surface and perturbation, with breaking frequency \(\frac{K^{\prime}}{\varepsilon_{c}}\).  
Consider a mass damper system as Equation (3). After taking the derivative of Equation (28) and substituting Equation (3) into it, the following relation was obtained.  
\[\dot{s}=k_{d}\bigg{\{}\frac{1}{J}\big{[}u-B\cdot\dot{x}-\psi\big{]}-\ddot{x}_ {d}\bigg{\}}+k_{p}\cdot\dot{e} \tag{33}\]  
As we know that to fulfill the sliding condition \(s\cdot\dot{s}\leq 0\). \(\dot{s}\) should be equal to \(\dot{s}=-K\cdot sat(s)\)  
\[k_{d}\bigg{\{}\frac{1}{J}\big{[}u-B\cdot\dot{x}-\psi\big{]}-\ddot{x}_{d} \bigg{\}}+k_{p}\cdot\dot{e}=-K\cdot sat(s) \tag{34}\]  
After rearranging the above equation, the following controller output is obtained for PDSMC.  
\[u=-\frac{J\cdot K\cdot sat(s)}{k_{d}}+J\cdot\ddot{x}_{d}-\frac{J\cdot k_{p} \cdot\dot{e}}{k_{d}}+B\cdot\dot{x} \tag{35}\]
## 2 Related Work  
The importance of routing in WSNs and its impact on the implementations is discussed in several works in the literature [15], even considering energy concerns [16]. Likewise, the use of SDN in WSN is explored in some contributions, for instance, in [17]. Several researchers discuss routing control for SDWSN using computational methods. In [18], the authors analyze the advantages of using a centralized SDN control to plan network routes in which the OpenFlow protocol is used to monitor the routing status and link the routing information of network load in real-time. The routes are obtained using the Elman neural network. In [19], SDN-WISE improves routing processes in WSN operating with TSCH. Instead, reinforcement learning techniques to select the best path in an SDWSN are used in [20], considering all the necessary metrics to have energy efficiency and the QoS of the WSN.  
In [21], the Kruskal and clustering algorithms select each group's nodes to increase the network's lifetime. Similarly, in [22], the algorithm of Kruskal is used to determine the route within a group of nodes and thus optimize energy consumption. The Dijkstra algorithm is used in SDN in [23; 24] for routing control and uses complex metrics to select the best route, including hop count and energy efficiency. In [25], the algorithm for reliable networks is applied by selecting nodes with the lowest transmission power to route data between networks and comparing it with the LEACH algorithm.  
Table 1 shows related work focused on the routing problem in SDWSN by implementation or through simulation. A notable observation is that most of the proposals use simulation tools, but in contrast to the works implemented, nodes use high processing capacity due to the complexity of the algorithms.
## 3 Methodology
### Construction of the Spatio-Temporal Network
#### 3.3.1 Construction of Train Spatio-Temporal Network
\[A_{i}^{dwell}=\left\{\big{(}s,t,i;s,t^{\prime},i\big{)}\big{|}(s,t,i)=\theta_{i }^{s}\big{(}s,t^{\prime},i\big{)}=\rho_{i}^{s},\ s\in S_{i}\right\} \tag{4}\]  
(4) Train End Arcs \(A_{i}^{end}\)  
Train end arcs connect the departure node at the terminating station to the virtual end point. For any train \(i\in I\), at its terminating station \(s=d_{i}\in S_{i}\), there is a train end arc \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Set** & **Type** & **Node Expression** & **Time Setting Basis** \\ \hline \(\sigma_{i}\) & Virtual origin & \((o_{i},0,i)\) & Daily operating time window start time (0:00 h) \\ \(\sigma_{i}\) & Virtual destination & \((d_{i},1440,i)\) & Daily operating time window end time (24:00 h) \\ \(\theta_{i}^{s}\) & Arrival node & \(\left(s,t_{i,s^{\prime}}^{a},i\right)\) & Train arrival time \(t_{i,s}^{a}\) \\ \(\rho_{i}^{s}\) & Departure node & \(\left(s,t_{i,s^{\prime}}^{d},i\right)\) & Train departure time \(t_{i,s}^{d}\) \\ \hline \end{tabular}  
2. Network Arc Construction  
For any train \(i\in I\), the train sub-spatio-temporal network \(A_{i}\) includes four types of arcs: train start arcs \(A_{i}^{start}\), train travel arcs \(A_{i}^{travel}\), train dwell arcs \(A_{i}^{dwell}\), and train end arcs \(A_{i}^{end}\), \(A_{i}=A_{i}^{start}\cup A_{i}^{travel}\cup A_{i}^{dwell}\cup A_{i}^{end}\), as shown in Table 5. Representation form of arcs in train spatio-temporal network.  
\end{table} 
Table 4. Representation form of train spatio-temporal network nodes. from the departure node at the terminating station \(s\), \((s,t,i)=\rho_{i}^{d_{i}}\), to the virtual end point \((s^{\prime},t^{\prime},i)=\varphi_{i}\), represented as \((s,t,i)\rightarrow(s^{\prime},t^{\prime},i)\), as shown in Equation (4). \[A_{i}^{end}=\Big{\{}\big{(}s,t,i;s^{\prime},t^{\prime},i\big{)}\Big{|}(s,t,i)= \rho_{i}^{d_{i}},\big{(}s^{\prime},t^{\prime},i\big{)}=\varphi_{i}\Big{\}} \tag{4}\]
## 3 Discussion of Results  
The use of TLS and SfM in the monitoring of the restoration works of the Refectory of the Santo Domingo College has made it possible to record its geometry accurately, as well as to document graphically all the walls and construction elements that have been intervened during the works.  
The point cloud obtained in the intermediate stage, once the pre-existing floors have been demolished, provides data on hidden construction elements: the vault is 11 and 12 cm thick (Figure 16). In the same sense, the recording of all the data collected in the intervention process makes possible the archaeological analysis and the study of possible findings. An important debate is opening up that relates the compatibility of traditional construction models and construction systems used in retrofitting that respect the heritage received [45; 46] and the standards of quality and structural safety [47]. Comparison of the initial loads (34.54 KN/m in the starts of arches 1, 2, 3, and 4; and 34.84 KN/m in the start of arch 5) with the final loads (31.90 KN/m at the start of arches 1, 2, 3, and 4; and 34.90 KN/m at the start of the arch 5) confirm a final load state that is more favourable or equal to the initial one. The loads on the keys offer a similar conclusion (initial loads of 26.45 KN/m in arches 1, 2, 3, and 4; and 28.63 KN/m in arch 5) and (final loads of 26.45 KN/m in arches 1, 2, 3, and 4; and 28.21 KN/m in arch 5). The data obtained in (Table 4) show a maximum deflection of 11 mm, so that the deflections are negligible and less than the deflections allowed in conventional slabs. The deflections in the arch kidneys  
Figure 15: (**a**): Downturned voussoirs of diagonal arches. (**b**): Deformations and cracks in vaults.  
in the West zone of the Refectory (approximate point 3 of the arch), with a maximum of 2 mm, are smaller than in the East zone (approximate point 11) with a maximum of 11 mm, which indicates a tendency to greater movements in the parts of the arches closest to the facade (exterior).  
As a working methodology to be followed in restorations similar to the one shown in this work, the use of digital tools is proposed for the documentation and monitoring of the restoration process in the 3 phases of work: in the preliminary studies, in the execution of the works and in the final state of the restoration.  
The point cloud obtained at the end of the works is checked against the initial one to study possible differential movements [3].  
The decreases recorded are negligible, so that we are in the order of f < 1/1000 of light, less than the deflections required for slabs in buildings, and probably due to the commissioning of the new state of loads on the previously dismantled structure.  
The recoveries in the deformations of the vaults not addressed in the present investigation may be the subject of future studies. In some cases they may be the result of cleaning and adjustment of some of the voussoirs.
# A Cloud-Based Ambulance Detection System Using YOLOv8 for Minimizing Ambulance Response Time
## 1 Introduction
In this paper, we propose a layered architecture system to develop an ambulance vehicle detection system that consists of four layers including (i) the data acquisition layer (DAL), which is the camera on the traffic light. (ii) The ambulance detection layer (ADL), which has the YOLOv8 model, will inherit the data from the DAL and process each frame. It will then export the data for the application programming interface (API), and from there, the API sends the data to (iii) the monitoring layer (ML). Both the ADL and ML are connected through (iv) the cloud layer (CL) for process and storage purposes. The main contributions of this work are summarized as follows:  
* _We collected and labeled 3000 images of ambulances from 10 different countries that cover various domains and languages. The dataset can be used to train and evaluate computer vision processing models for cross-lingual tasks._
* _We proposed a novel layered architecture system that can handle multiple types of signal detection reports from different sources and formats. The system can efficiently process, store, and query reports using a unified interface._
* _To the best of our knowledge, we are the first to use YOLOv8 to detect the ambulance to allow the traffic signals to turn green to speed up the ambulance's estimated arrival time to the hospital._
* _We conducted a comprehensive evaluation of the pre-trained model and the non-pre-trained model on our dataset and compared its performance among 10 different countries. We showed that the pre-trained model outperforms the non-pre-trained model on most metrics and achieves state-of-the-art results._The remainder of the paper is structured as follows: Section 2 discusses the related work. We describe the architecture of the ambulance detection system in Section 3. The YOLOv8 model we used is presented in Section 4, and the experiment's results are described in Section 5. Section 6 discusses the conclusion and future work.
# Reference Mapping Considering Swaps of Adjacent Bases
## 1 Introduction
In this paper, we propose the SOAP3-swap, which is based on SOAP3-dp and performs reference mapping with additional consideration for swaps. SOAP3-swap can produce alignments in the presence of mismatches, small gaps, and swaps.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 3 Solution Algorithm  
Although GUROBI can be applied directly to the arc-based formulation, after some preliminary experiments, we find that the instance size handled by the solver is quite limited. To obtain the optimal solution for larger instances, we reformulate the MB scheduling problem as a path-based formulation. By applying Dantzig-Wolfe decomposition, the original problem effectively yields a master problem allocating MB platoons and a subproblem that addresses the trip sequences under range and charging constraints.
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 6 Appendix  
Figure 8: Corona discharges in a needle-plate geometry (needle diameter = 0.8 mm, needle tip-to-ground distance = 70 mm, 60 kPa, 3.6 kV, 50 Hz). (**a**) Photograph of the corona discharge in the tip of the needle. (**b**) Voltage (yellow) and current (blue) waveforms during the corona discharges. (**c**) Detail of a corona discharge as seen in the electrical current pattern.  
Figure 9: State-of-the-art EH systems for transmission lines.  
Table 1 summarizes the main characteristics of the EH methods for power lines reviewed in this section.  
The results presented in Table 1 clearly show that solar is the EH system with the highest power density, and it can be applied to both AC and DC transmission lines, although this method presents two important drawbacks related to periodic maintenance requirements and the impossibility of a constant supply. Other EH methods compatible with AC and DC lines are thermoelectric, RF and corona. RF is probably the EH system with the lowest power density. Solar, thermoelectric, electric field, and RF EH allow for generating energy even when the line is not carrying any current, i.e., under no load conditions. The results show that there is not a clear winner or universal solution, so the most suitable EH system depends on the characteristics of each particular application. These characteristics include the geographical location (it defines dust, sand, dirt, ice. or snow conditions, solar resource, or ambient RF energy), the intensity of the current flowing in the line, or the line voltage, among others.  
It is worth noting that WSNs in transmission lines are often enclosed inside a metallic box that acts as a Faraday cage to block the intense electric field. In such cases, the common ground of the electronic board is connected to the metallic box, which, in turn, is in contact with the line conductor.
# Two-Dimensional Scanning of Circularly Polarized Beams via Array-Fed Fabry-Perot Cavity Antennas
## 2 Unit-Cell Design and Analysis Strategy
The standard formulation based on the reciprocity theorem gives  
\[\mathbf{J}_{\mathrm{test}}\cdot\mathbf{E}_{\mathrm{HED}}=\mathbf{J}_{\mathrm{ HED}}\cdot\mathbf{E}_{\mathrm{test}} \tag{1}\]  
where \(\mathbf{J}\) and \(\mathbf{E}\) represent the electric current density and the electric field, respectively, in the presence of the source (subscript 'HED') or of a test dipole in the far-field region producing an impinging plane wave (subscript 'test'). By reciprocity, the electric fields sampled by an ideal probe (oriented as the original HED), produced by plane waves impinging on the structure from different azimuthal and elevation angles, allow for recovering the overall 3-D far-field pattern for the \(E_{\phi}\) and \(E_{\theta}\) components from the incident TE and TM Floquet waves in the unit cell [47]. The CP components are finally obtained by the following transformation:  
\[ \begin{bmatrix}E_{\mathrm{RHCP}}\\ E_{\mathrm{LHCP}}\end{bmatrix} =\frac{1}{\sqrt{2}} \begin{bmatrix}1&j\\ 1&-j\end{bmatrix}\begin{bmatrix}E_{\theta}\\ E_{\phi}\end{bmatrix}  \tag{2}\]  
with \(E_{\mathrm{RHCP}}\) and \(E_{\mathrm{LHCP}}\) being the right-hand and left-hand circular-polarization components, respectively. The design parameters of the PCM are here optimized to have an RHCP broadside pencil beam at 27 GHz with low cross-polarization levels. Thanks to the aforementioned decoupling between the top and bottom layers of the PCM, the modifications introduced in the optimization of the top layer have very little impact on the reflection coefficient and hence on the cavity height. This concept is corroborated in Figure 2, where the reflection-coefficient phase is reported for different optimization steps showing a quasi-perfect superposition. The complete list of optimized unit-cell parameters is reported in Table 1, (see Figure 1 for the definition of the symbols).
## 3 Results and Discussion
### Experiment Verification  
#### 3.2.1 Linearization Verification of Static Weighing Results  
According to the linear simplification range of the mathematical model in Section 3.1, the parameters of the connected piping of the device shown in Figure 4 are determined as follows: the moment of inertia is 3.29 \(\times\) 10\({}^{2}\) mm\({}^{4}\), the length is 0.2 m, the elastic modulus of the materials is 127 GPa, and the total mass of the materials is in the range of 0-1000 g. The static weighing experiment was carried out and the results are shown in Table 2.  
The linear regression equations between the true value and the measured value were fitted. The calibration equation obtained with the average value is \(m_{1}=1.0181m-1.6190\), with the coefficient of determination (R\({}^{2}\)) 0.99997. The absolute maximum error, repeatability coefficient, hysteresis, and linearity of the measurement data were calculated respectively to comprehensively evaluate the absolute error, random error, and systematic error of the weighing measurement system. The results are shown in Table 3.  
The maximum absolute error mainly assesses the accuracy of the measurement results, which considers the deviation in the measurement process. Repeatability is the degree of consistency of the curves obtained during multiple measurements, which is used to evaluate random errors. Hysteresis indicates the degree of noncoincidence between positive and negative characteristics during measurement. Linearity represents the degree of consistency between the input-output curve and the calibration curve. The hysteresis and linearity values reflect the magnitude of the system error [37]. The results show that the values of the four indicators are all very small, so it can be inferred that the system has high measurement accuracy and low random and systematic errors.