esa-sceva/satcom-chunk-collection huggingface dataset:
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## Appendix A Reference LoS  
\[H_{\text{LoS}}=\mathcal{CG}\frac{(m+1)A_{R}}{2\pi d^{2}}\Omega_{\text{LoS}}\Psi_{ \text{LoS}}\,,\]  
\[\mathcal{C}=\begin{cases}\frac{\eta}{\sin^{2}(\theta_{0})}&0\leq\theta_{0}\leq \Theta_{\text{FoV}}\\ \\ 0&\theta_{0}>\Theta_{\text{FoV}},\end{cases}\]  
Reference NLoS  
\[H_{\text{NLoS},(1)}=\frac{(m+1)A_{R}}{2\pi}\sum_{w=1}^{W}\mathcal{C}_{w} \mathcal{G}_{w}\frac{A_{\text{refl},w}\varrho_{w}}{d_{1,w}^{2}d_{2,w}^{2}} \Omega_{\text{NLoS}}^{(w)}\Psi_{\text{NLoS}}^{(w)}\,,\]  
Reference Scattering Model  
\[H_{\text{scatter}}=\lim_{N\to\infty}\frac{(m+1)A_{R}\,\varrho_{s}}{2N\pi}\sum _{n=1}^{N}\frac{p_{n}}{d_{n}^{2}}\,\Omega_{\text{scatter}}^{(n)}\,\Pi(\theta_{n })\,,\]  
\[\Pi(\theta_{n})=\begin{cases}1&0\leq\theta_{n}\leq\Theta_{\text{FoV}}\\ \\ 0&\theta_{n}>\Theta_{\text{FoV}}.\end{cases}\]  
\[p_{n}=\frac{k_{s}^{\text{mie}}}{k_{s}}p_{\text{mie}}(\phi_{n})+\frac{k_{s}^{ \text{ray}}}{k_{s}}p_{\text{ray}}(\phi_{n})\]  
where: \(m\) is the Lambertian mode number, \(A_{R}\) is the PD surface area, \(d\) is the distance between LS and PD, \(\mathcal{G}\in[0,1]\) is the optical filter gain, \(\mathcal{C}\in[1,+\infty)\) is the optical concentrator gain, \(\Omega_{\text{LoS}}\in[0,1]\) is a trigonometric expression that depends on the relative position, rotation and tilt of the LS and PD, the half-power angle of the light beam, \(\phi_{1/2}\), the Lambertian mode number, \(m\), and the field of vision angle of the PD,\(\Theta_{\text{FoV}}\); and \(\Psi_{\text{LoS}}\in[0,1]\) is a scaling factor which accounts for the degree of shadowing in the LoS link  
\(W\) is the number of reflectors, modelled as the generalized Lambertian virtual LSs, such that for the \(w\)-th reflector,  
\(A_{\text{refl},w}\) is its surface area,  
\(\rho_{w}\in[0,1]\) is its reflectance,  
\(d_{1,w}\) is the distance from the LS to the reflector  
\(d_{2,w}\) is the distance from the reflector to the PD  
\(\mathcal{G}_{w}\) and \(\mathcal{C}_{w}\) are the optical filter and the concentrator gain at the receiver, respectively, accounting for the incident angle from the \(w\)-th reflector to the PD,  
\(\Omega_{\text{NLoS}}^{(w)}\in[0,1]\) is a trigonometric expression which depends on the relative position, rotation and tilt of the LS, the \(w\)-th reflector and the PD  
\(\Psi_{\text{NLoS}}^{(w)}\in[0,1]\) is a scaling factor that accounts for the degree of shadowing in the \(w\)-th NLoS link. \(k_{s}^{\text{mie}}\) is the scattering coefficient of the Mie scattering dust particles,  
\(k_{s}^{\text{ray}}\) is the scattering coefficient of the Rayleigh scattering air molecules,  
\(k_{s}=k_{s}^{\text{mie}}+k_{s}^{\text{ray}}\) is the total scattering coefficient,  
\(p_{\text{mie}}\left(\cdot\right)\) is the Mie scattering phase function,  
\(p_{\text{ray}}(\cdot)\) is the Rayleigh scattering phase function.
# Ten Years of TerraSAR-X Operations
## 4 Dedicated Mission Campaigns  
### DRA Campaigns  
The parallel activation of the primary and redundant receiver chain to obtain experimental quad polarization products and complex ATI products is performed within dedicated campaigns. Since the DRA operation imposes specific further constraints w.r.t. the on-board data acquisition, recording, and downlink when compared to the SRA operation, ordering and planning of DRA acquisitions is done centrally inside the ground segment for pre-selected test sites. The resulting data are then made available for user product ordering from the catalog.  
The first DRA campaign lasted for three cycles (33 days) from 11 April until 13 May 2010. Since no SRA data can be acquired while downlinking data in DRA mode, the by then newly built-up station ISSF with the DLR antenna and equipment was used for data reception. Thus, the impact on data acquisition (both SRA and DRA) over Europe and the downlink of SRA data to NSG was kept at a minimum. In total, 356 SM quad pol and 163 SM single pol (ATI) acquisitions are available for user catalog ordering from this time period.  
The second DRA campaign was conducted as part of the TanDEM-X Science Phase, starting at 18 November 2015 and ending at 11 February 2016.
## 3 Path Loss Models
### UMi Path Loss Models
#### 3.1.4 mmMAGIC Model  
This model uses large-scale ABG model to estimate the PL in UMi-LOS and UMi-NLOS scenarios.
## 1 Introduction
Since thousands of service pipes are present in each water district, these leaks can result in high overall losses. Implementing an effective active control strategy for this kind of leak is therefore as crucial as managing larger bursts in the water main pipelines. The long-term objective of the research is the development of a system for automatic early detection of unreported burst leaks occurring in the service connections running from the water main to the users' metering points [21]. To the authors' best knowledge, no studies have specifically investigated the use of AE methods for leak detection purposes in plastic small-diameter service pipes. The desired system is meant to be installed next to the customer water meter and to operate with the normal functioning conditions of the network. In order to keep costs as low as possible, the system should rely on relatively simple hardware components and, consequently, computationally demanding signal processing is not suitable for this application. Hence, the investigation took into account only basic signal processing techniques. An experimental campaign was performed to assess the suitability of AE methods for the application of interest. The experiments were conducted on a test rig featuring a small-diameter polyethylene pipe, which simulated a typical customer connection branch of the network. The test bed replicated the main characteristics of buried service pipes of the network of interest. AE events were monitored by means of a wideband AE sensor. Water leaks were artificially induced at different distances from the transducer measuring location. Measurements in both leaking and non-leaking conditions were carried out. Two different boundary conditions of the pipeline were taken into account. Firstly, measurements were performed on the unburied pipe. Then the pipe was covered with backfill soil and further tests were conducted. This paper is organized as follows. Section 2 describes the experimental setup and procedures, and reports the most relevant results of the analysis for the unburied pipeline. The tests on the buried pipe are presented and discussed in Section 3. The last section draws the conclusions of the investigation.
## 5 WSN Application Design
### Duty Cycle
In an extreme situation, it could even cause the speed at which events are propagated to be lower than the speed of vehicles. This may occur if sleep periods are longer than the time required for a vehicle to move from one node to the next one (assuming one packet forwarding per cycle), which would be a major issue for many applications. The active period, in turn, must be designed to assure the proper transmission of information. This does not only include the transmission of the information itself by merely reserving a transmission time according to the maximum number of messages that can be transmitted per period and their length. It also includes time deviations, which must be taken into account, too. On the one hand, this implies reserving additional time to handle the synchronization variations that the system may be subject to. On the other hand, if the system allows packet retransmissions (due to packet collisions in the channel access or to transmission errors), the active period must be extended to accommodate them. The extent of this non-ideal operation depends on the selected MAC protocol (Section 6.1). In addition, the use of routing protocols may also increase the active period duration and consequently the power consumption, because it may become necessary for nodes to receive and propagate additional messages from their neighbors, thus having to reserve additional fully operational time for the reception of messages. Given the differing frequency of events of vehicle traffic, the use of a fixed schedule for the duty cycle is not always the best option. In this respect, there are some works which vary the duty cycle according to parameters related to the traffic state and safety; in particular, they focus on adjusting the duty cycle of the sensors but not the one followed by the radio. For example, in [56] the speed of vehicles is used as the parameter governing the duty cycle. In [36], in turn, several operational schemes are proposed in which the duty cycle of the nodes is controlled by a master node. The objective is the detection of vehicles in order to report potential vehicle collisions on the road. Upon the detection of a vehicle from the master node, all subsequent nodes along the road are woken up. The risk of taking this approach is that if the master node does not detect a vehicle, none of the subsequent nodes will do it. As a result, master nodes are supposed to be more powerful nodes (in terms of energy) with less restrictive duty cycles to ensure vehicle detection. In a similar way, the loss of the wake-up messages also jeopardizes the detection of vehicles by the nodes. Therefore the authors of that work suggest the use of schemes based on the combination of wake-up messages with random wake-up for the nodes. In spite of this, it is still possible that some nodes remain asleep. Some works instead, focus on assuring communication in order to avoid this situation. For example, in [12] a similar scheme based on the wake up of nodes is implemented, but packets are sent by using guaranteed access to the radio channel and packet acknowledgements and retransmissions.
## 3 Results
### Decision-Making Process  
It was noted above that parameters (indicators) take rather arbitrary values. More precisely, each parameter varies in a certain inherent interval. Further, we assume that the parameters are unified, i.e., the corresponding intervals are mapped to the interval [0, 1].  
Each \(i\)-th indicator can be associated with one \"universal\" predicate \(\ P^{i}(x)\) or three single-place predicates \(P^{i}_{L}(x),\ P^{i}_{M}(x),\ P^{i}_{H}(x),\) which naturally arise through composition with the functions below (Figure 1a-c).  
For simplicity, formula-definable predicates are also usually introduced:  
\[P^{i}_{LM}(x)=P^{i}_{L}(x)\cup P^{i}_{M}(x),\ \ P^{i}_{HM}(x)=P^{i}_{M}\cup P^{i}_{H}\]  
Similarly, with each financial indicator, i.e., characteristic of the loan, we associate the predicate \(Q^{i}_{D},\ D\in\{L,\ M,\ H,\ LM,\ HM\}.\) The rules based on which decisions are made have the form \(\ \varphi(x_{1},\ldots,\ x_{n})\rightarrow\psi(y_{1},\ \ldots,\ y_{m})\,.\)  
Figure 1: Membership functions.  
Usually, \(\varphi(x_{1},\ldots,\ x_{n})=\Lambda_{j=1}^{n}Q_{j}(x_{j})\), i.e., we have a conjunction of unary predicates of the above signature. Moreover, each \(Q_{j}\) has the form \(P_{D}^{i}\), \(D\in\{L,\ M,\ H,\ LM,\ HM\}\). As a result, we obtain a rule of the following form [24]:  
\[\begin{split} P_{LM}^{1}(x_{1})\cap P_{LM}^{2}(x_{2})\cap P_{M}^{ 3}(x_{3})\cap P_{M}^{4}(x_{4})\cap P_{HM}^{5}(x_{5})\cap P_{M}^{6}(x_{6})\cap P _{LM}^{7}(x_{7})\cap P_{M}^{8}(x_{8})\\ \cap P_{L}^{9}(x_{9})\cap P_{L}^{10}(x_{10})\cap P_{H}^{11}(x_{1 1})\cap P_{M}^{12}(x_{12})\cap P_{L}^{13}(x_{13})\\ \cap P_{LM}^{14}(x_{14})\cap P_{L}^{15}(x_{15})\cap P_{LM}^{16}(x _{16})\cap P_{M}^{17}(x_{17})\cap P_{HM}^{18}(x_{18})\\ \cap P_{M}^{19}(x_{19})\cap P_{LM}^{20}(x_{20})\cap P_{LM}^{21}(x _{21})\cap P_{L}^{22}(x_{22})\to Q_{M}^{1}(y_{1}).\end{split} \tag{12}\]  
Several rules may be used \(R_{i}:\varphi_{i}(x_{1},\ldots,x_{n})\to\psi_{i}(y_{1},\ldots,y_{n}),\ i=1,..N\).  
For brevity, we write \(R_{i}=R_{i}(x_{1},\ldots,x_{n},\ y_{1},\ldots,y_{m})\).  
In most cases, it is natural to consider their distinction \(R=\bigvee_{i}R_{i}\).  
Parameters \(x_{1},\ldots,x_{n}\) are obtained because of the analysis of the enterprise. These are the indicators mentioned in Section 1. Parameters \(y_{1},\ldots,y_{m}\) are predicted. These are indicators such as loan amount, interest rate, and loan duration. In its most general form, one parameter is predicted called the credit index, varying from 0 to 1 and having a natural interpretation. Preference is given to enterprises with a higher credit rating.
# Data and Knowledge-Driven Bridge Digital Twin Modeling for Smart Operation and Maintenance
## 7 Conclusions  
This study proposed a novel digital twin (DT) framework that integrates spatial, temporal, and expert knowledge models to enhance real-time bridge monitoring and maintenance. By addressing critical challenges in bridge maintenance, this framework improves the accuracy of structural assessments and increases the efficiency of maintenance operations, demonstrating the proactive role of DT technology in bridge O&M.  
The findings highlight the potential of DT technology to extend the lifespan of critical infrastructure through real-time data integration and predictive maintenance. The framework provides a practical solution for smart structural health monitoring and contributes to the development of advanced strategies for infrastructure management.  
In summary, this study underscores the significance of DT technology in improving structural safety and operational efficiency. The proposed framework serves as a foundational step toward implementing smart, data-driven solutions in civil engineering, paving the way for further advancements in intelligent infrastructure management.  
Future studies could explore several key directions to further enhance DT applications in bridge monitoring. First, incorporating advanced machine learning algorithms may improve model efficiency and accuracy. Second, improving the interaction between spatial and temporal models could increase model responsiveness, strengthening real-time decision-making. Refining expert knowledge model classifications and enabling automatic selection of appropriate knowledge bases for varying bridge types could enhance operational efficiency. Such advancements will continue to evolve DT technology, enhancing infrastructure monitoring and maintenance in intelligence, efficiency, and precision.  
Conceptualization, Z.S. and B.L.; methodology, Z.S.; software, B.L.; validation, Z.S., B.L., S.L. and Z.L.; formal analysis, B.L.; investigation, B.L. and S.L.; resources, Z.S.; data curation, B.L. and S.L.; writing--original draft preparation, B.L.; writing--review and editing, Z.S.; visualization, B.L.; supervision, Z.S.; project administration, Z.S.; funding acquisition, Z.S. and Z.L. 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 No. 52408145] and the Natural Science Foundation of Chongqing [Grant No. CSTR2023 NCQMSX1013]. The support is gratefully acknowledged.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Selection of Cationic Surfactant Concentration  
The effect of CTAB concentration was investigated over a wide concentration range, from 0.5 mM (i.e., below the cmc of \(\approx\) 0.9 mM) to 140 mM. The signal gradually increases up to 10 mM and decreases at higher concentrations (Figure 3b). This observation agrees with the solubility of the Au-CTAB complex, which reaches its maximum value at 1/60 [20]. At CTAB concentrations lower than 10 mM, the Au/CTAB ratio is higher than 1/60; therefore, the complex has limited solubility and forms precipitates that scavene the incident light and produce intense signals (darker images) as less light is transmitted through the sample. At CTAB concentrations higher than 20 mM, the Au/CTAB ratio is lower than 1/60, hence the Au-CTAB complexes are solubilized, and more light is transmitted through the solution producing brighter images. When cysteine is added into the solutions, it complexes Au, releasing it from the Au-CTAB complex. In this manner, the Au/CTAB ratio decreases (as the Au concentration on the Au-CTAB complex also decreases), thus increasing the solubility of the Au-CTAB complex. As a result, the signal of the blank is lower than that of the sample due to the increased solubility of the Au-CTAB complex. Therefore, the net analytical signal response, which is calculated as the difference between the signals of the blank and the sample, reaches its maximum value. The above discussion also explains why at CTAB concentrations lower than 10 mM, the (net) analytical signals are higher than those observed at CTAB > 20 mM (except for the cases where the CTAB concentration is lower than its cmc) due to the solubilizing action of Cys.
## 2 Problem Description and Model Formulation
### Model Formulation
#### 2.2.6 TDGVRPSTW Model Formulation  
The TDGVRPSTW model is built as follows:  
\[minC=C_{1}+C_{2}+C_{3} \tag{5}\]  
Among them  
\[C_{1}=\sum_{i\in N}\sum_{j\in N}\sum_{k\in K}X_{ijk}D_{ij}\varphi+\sum_{k\in K }z_{k}\delta+\sum_{i\in N^{\prime}}P_{c}(T_{ik})\]  
Figure 2: Penalty cost curve under the broken line soft time window.  
\[C_{2}=\sum_{h\in H}\sum_{i\in N}\sum_{j\in N}\sum_{k\in K}x_{ijk}^{h}E_{ijk}^{h}u\]  
\[C_{3}=\sum_{k\in K}\sum_{j\in N^{\prime}}\xi_{0j}pq_{j}y_{jk}\]  
Subject to:  
\[\sum_{i\in N^{\prime}}q_{i}y_{ik}\leq Q,\forall k\in K \tag{6}\]  
\[\sum_{j\in N^{\prime}}X_{0 jk}\leq 1,\forall k\in K \tag{7}\]  
\[\sum_{k\in K}y_{ik}=1,\forall i\in N^{\prime} \tag{8}\]  
\[\sum_{i\in N}X_{ijk}=\sum_{j\in N}X_{ijk},\forall k\in K \tag{9}\]  
\[t_{ijk}=\sum_{h\in H}X_{ijk}t_{ijk}^{h},\forall i\in N,j\in N,k\in K \tag{10}\]  
\[D_{ij}=\sum_{h\in H}d_{ijk}^{h}X_{ijk},\forall i\in N,j\in N,k\in K \tag{11}\]  
\[ET_{0}\leq X_{i0k}(T_{is}+s_{i}+t_{i0k})\leq LT_{0},\forall i\in N^{\prime},k\in K \tag{12}\]  
\[T_{is}\geq T_{ik},\forall i\in N^{\prime},k\in K \tag{13}\]  
\[L_{ik}=T_{is}+s_{i},\forall i\in N,j\in N,k\in K \tag{14}\]  
\[t_{0i}=T_{is},\forall i\in N^{\prime},k\in K \tag{15}\]  
\[\sum_{j\in N^{\prime}}X_{0 jk}\leq 1,\forall k\in K \tag{16}\]  
\[x_{ijk}^{h}\in\{0,1\},X_{ijk}\in\{0,1\},y_{ik}\in\{0,1\},z_{k}\in\{0,1\} \tag{17}\]  
Equation (5) of the objective function represents cost minimization, where \(C_{1}\) represents distribution cost, including vehicle transportation cost, vehicle fixed use cost, and soft time window penalty cost. The time windows penalty cost is defined in Equation (4). \(C_{2}\) represents the cost of carbon emission and \(C_{3}\) represents the cost of freshness loss. Carbon emission cost is defined in Equation (3), and freshness loss cost is defined in Section 2.2.2. Equation (6) represents the load limit of each vehicle. Equation (7) indicates that each car can only be used once. Equation (8) indicates that each customer must be served only once. Equation (9) indicates that the number of departing and arriving vehicles at each node should be balanced. Equation (10) represents the relationship between the road section driving time and the vehicle driving time within the time period. Equation (11) represents the relationship between the road section distance and the distance traveled by the vehicle in each period. Equation (12) indicates that vehicles returning to the distribution center should meet the constraints of the distribution center's working time window. Equation (13) represents the relationship between the start time of service and the arrival time. Equation (14) represents the relationship between the service start time, duration, and departure time. Equation (15) indicates the time when the customer receives the fresh products. Equation (16) indicates that the customer's demand is less than the vehicle capacity, and there is only one vehicle to serve them. Equation (17) represents the variable value constraint.
## 2 Materials and Methods
### Numerical Simulations  
The numerical calculations were made using software complexes ANSYS and FlowVision to get the operating parameters of the air-abrasive mixture both inside and off the nozzle. The following nozzles were tested: a Venturi nozzle with a critical diameter of 4 mm and a length of 143 mm, a Venturi nozzle with a critical diameter of 8 mm and a length of 194 mm, a cylindrical nozzle from ceramics with a length of 22 mm, a cylindrical nozzle from steel C40 with a length of 11 mm, a cylindrical nozzle from steel C40 with a length of 44 mm, UDC32-450 with a critical diameter of 6 mm with hydraulic smooth walls and a rough inner surface, cylindrical nozzle with an inner diameter of 20 mm, length of 22 mm. The flowchart of the numerical simulations is presented in Figure 2.  
Numerical modeling with the ANSYS application algorithm is presented in Figure 3. The first stage included building a 3D model of the nozzle UDC32-45 (Figure 3a). Next,  
Figure 2: The flowchart of numerical simulations.  
the computational grid was constructed (Figure 3b). Afterwards, a calculation model was selected, and boundary conditions were set.  
Next, calculations with convergence control application (Figure 4) were carried out.  
The turbulent flow simulation was carried out using the Reynolds equations application of various turbulence models. The flow was calculated for stationary conditions. The carrier medium was air; the abrasive material was sand with grain sizes of 0.1-0.5 mm and the turbulent flow mode. Numerical calculations simulated the movement of the air-abrasive mixture in the flowing part of the nozzle under study. The geometry of the nozzle is limited by the three-dimensional model we created. The software package user sets the boundary conditions related to gas dynamics manually and remain unchanged until the calculation is completed [1; 17; 18].  
Figure 3: Three-dimensional model (**a**) and mesh grid (**b**) of the flow part for the industrial nozzle UDC32-450 with a critical diameter of 6 mm. Dimensions of the simulation model (**c**).  
The working process was modeled using the well-known Reynolds equations, which include turbulence models. The flow was considered to be stationary. Compressed air was selected as the carrier medium, and sand with a grain size of 0.1-0.5 mm was selected as the working abrasive material. The flow was turbulent. Similar problems have used k-\(\epsilon\), Spalart-Allmaras, and SST models [18; 19; 20].  
Airflow velocities in the flow part of the industrial nozzle UDC32-450 with a critical diameter of 6 mm are presented in Figure 5.  
Figure 4: The convergence of momentum and mass.  
Figure 5: Airflow velocities in the flow part of the industrial nozzle UDC32-450 with a critical diameter of 6 mm (**a**) and a surface roughness of 3.2 \(\upmu\)m (**b**).
# Research and Development of Inland Green and Smart Ship Technologies in China
## 3 Current Inland Green and Smart Ship Technology in China
### Inland Ship Technology
#### 3.1.4 Optimal Engine-Propeller Matching  
Because the flow velocities of inland rivers are often high [29], the loads when the ship moves against and along the current are quite different. Using the characteristic curves of the main engines and the complex load characteristics of inland ships, multiple-operating-point propeller design was conducted to allow the propeller to work efficiently when the ship moved against and along the current. Highly efficient, wake-adapted propellers were developed based on circulation theory considering the stern circumferential and axial wake field conditions and a combination of related technologies, such as a large skew, blade tip unloading and an anti-cavitation profile. By thoroughly considering the comprehensive performance of propellers, such as the efficiency, cavitation, and vibration, and through model self-propelled tests, the propulsion performance of real ships with adapted propellers was predicted.