esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Geometric-Translation Based Intersection Coordination Algorithm (TICA)
```
//id is the serial number of vehicles, \(num\) is the total number of vehicles. //index is the serial number of conflict points. //\(t_{id}^{\text{arri}}\), \(v_{id}^{\text{init}}\), \(lane_{id}\) are arrival time, initial speed, and location of the \(id\)th vehicle. //vded is the default design speed, \(v^{\text{mod}}\) is the modified design speed. Begin
1. Begin
2. For\(id=1\) to \(num\) Do
3. flag = 1, \(v^{\text{def}}\) = 30 km/h. 4. While\(flag=1\) Do
5. crashCount = 0;
6. Obtain motion information, including \(t_{id}^{\text{arri}}\), \(v_{id}^{\text{init}}\), \(lane_{id}\). 7. Calculate the location of the Time Blocks on Conflict-Duration-Graph. 8. For\(index=1\) to \(16\) Do
9. Record the time occupied by the \(id\)th vehicle for the _index_th conflict point. 10. If the \(id\)th vehicle conflicts with other vehicles at the _index_th conflict point
11. crashCount = crashCount+1. 12. End If
13. End for
14. If crashCount \(>0\)//there are collisions existing on CDG
15. Calculate \(v^{\text{mod}}\) using TICA. 16. \(v^{\text{def}}=v^{\text{mod}}\). 17. Else
18. flag = 0. 19. Instruct the \(id\)th vehicle to pass the intersection area with \(v^{\text{def}}\). 20. End if
21. End While
22. End For
23. End
```  
**Algorithm 1** Conflict detection and resolution strategy  
\[TB^{\prime}_{7}=TB_{7}^{2}+\Delta t \tag{28}\]0 Speed control solution for vehicle B. (**a**) Velocity difference before and after speed control; (**b**) speed control strategy.
## 2 System and Channel Model
### RF Sublink  
Assuming that the channel of the RF link adopts the \(\alpha-\mu\) distribution model, the PDF of instantaneous SNR \(\gamma^{RF}\) at the output of the RF sublink can be written as follows [33]:  
\[f_{\gamma^{RF}}(\gamma^{RF})=A_{m}e^{-B_{m}}{\left(\gamma^{RF}\right)}^{C_{m} }\left(\gamma^{RF}\right)^{C_{m}\mu_{m}-1}, \tag{3}\]  
where \(A_{m}=\frac{C_{m}B_{m}^{km}}{\Gamma(\mu_{m})}\), \(B_{m}=\mu_{m}\left(\overline{\gamma}^{RF}\right)^{-C_{m}}\), \(C_{m}=\frac{\alpha_{m}}{2}\), and \(\overline{\gamma}^{RF}\) is the output average SNR of the RF sublink. \(\alpha_{m}>0\) and \(\mu_{m}>0\) represent the nonlinearity parameter of the propagation environment and the number of multipath clusters, respectively. The \(\alpha-\mu\) distribution is the unified model which includes the small-scale fading channel and the large-scale fading channel. Some classical RF channel models can be obtained by setting different values of \(\alpha_{m}\) and \(\mu_{m}\)[33; 41].  
The CDF of \(\gamma^{RF}\) can be obtained by the integral [33]:  
\[F_{\gamma^{RF}}(\gamma^{RF})=1-D_{m}e^{-B_{m}}\gamma^{C_{m}}\gamma^{C_{m}}, \tag{4}\]  
where \(D_{m}=\sum_{j=0}^{\mu_{m}-1}\frac{1}{j!}B_{m}^{j}\).
## 3 Results
### Validation of the NPP and NEP Calculations
#### 3.1.2 Reliability Analysis of the NEP Values  
There are two main ways to verify the accuracy of regional NEP model simulations: one is to compare the simulated and measured data, and the other is to compare the simulation among different models [48]. Because the measured data in the HKH region are limited, we compared the average NEP values of the grassland ecosystems with prior studies. We mainly compared NEP values from the Qinghai-Tibet Plateau because prior studies are mostly concentrated there (Table 3). NEP and net ecosystem carbon exchange (NEE) are numerically equal [49; 50; 51]. There were differences between NEP values in this study and prior studies, but the NEP values of our study were basically within the range of values given in the published literature. This difference might have been mainly caused by the uncertainty of the Rh model, which was established by [PERSON] et al. [37] through statistical analysis of measured data around the world. The following problems occurred: (1) The database used to establish the model had no measured data throughout the HKH region, which led to deviations in Rh when the model was applied in the HKH region. (2)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Site Name** & **Period** & **Latitude** & **Longitude** & **Vegetation Types** \\ \hline CN-HaM & Haibei Alpine & & & & \\  & Tibet site & 2002–2004 & 37.37 & 101.18 & Grassland \\ CN-Ha2 & Haibei Shrubland & 2003–2005 & 37.6086 & 101.3269 & Wetland \\ CN-Dan & Dangxiong & 2004–2005 & 30.4978 & 91.0664 & Grassland \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of flux tower sites in the Hindu Kush Himalayan (HKH) region.  
The annual soil respiration (Rh) of the model was mainly derived from the expansion of the soil respiration (Rh) during the growing season, which would cause an overestimation of Rh. The soil microbial activity in the growing season and the decomposition ability were both strong, which would produce a large amount of carbon. In the nongrowing season, due to the decrease of temperature, the activity of microorganisms would weaken, and the carbon production would decrease. (3) A model based on a global scale might not be suitable for research on a regional scale. When the model was constructed, the strong spatial heterogeneity of the ground surface was not considered, so it might be biased when applied in the HKH region with strong heterogeneity.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
###### Abstract  
The design of passive photonic devices based on geometry optimization can lead to energy-efficient, small-footprint, and fabrication-ready geometries. In this work, we propose an angle-based parametrization method to optimize Y-junction splitters based on multimode interferometers. The selected figure of merit was the transmittance in the SCL and OESCL optical fiber communication bands. The performances of three optimization methods were compared: (i) particle swarm optimization (PSO), (ii) genetic algorithm (GA), and (iii) the covariance matrix adaptation-evolution strategy (CMA-ES). The results show that CMA-ES parametrization produces similar transmittance results (\(\leq\)1.5% of difference) to PSO in the first 40 generations. The CMA-ES results are identical in the SCL (1460-1625 nm) and OESCL (1260-1625 nm) bands, whereas the GA and PSO executions are slightly different in terms of the rate and similarity of the figure of merit.  
optimization methods; genetic algorithms; optical beam splitting; integrated optics; silicon on insulator Article
## 3 Experimental Program
### Test Results and Discussion
#### 3.5.2 Strain-Time Curves  
Taking the A114 \(\times\) 2.5-d0.6-H1.0 and A114 \(\times\) 2.5-d0.6-H2.0 samples as examples, the strain-time (\(\varepsilon\)-\(t\)) curves are shown in Figure 11. It can be known from Figure 11a,b that \(\varepsilon\) on the longitude and transverse sections at the mid-span of the specimen increased rapidly and approximately descended to zero after the impact load. Furthermore, compared with \(\varepsilon\)T, \(\varepsilon\)L was much larger and \(\varepsilon\) on the upper and lower surfaces of the pipelines showed a symmetrical state. When \(H\) was 2 m, \(\varepsilon\)L was about 816 \(\mu\varepsilon\) and \(\varepsilon\)T was about 603 \(\mu\varepsilon\). The maximum deviation of \(\varepsilon\)L and \(\varepsilon\)T was approximately 26%, which indicates that the deformation of the pipeline at the mid-span under the impact load was in the elastic range, and a large elastic deformation was produced in the longitudinal section of the pipeline at mid-span.  
From Figure 11c-f, it can be known that, when \(H\) was 2 m, \(\varepsilon_{\mathrm{L}}\) at positions (A-A)2 and (C-C)2 was 310 \(\upmu\varepsilon\) and 300 \(\upmu\varepsilon\), respectively, and \(\varepsilon_{\mathrm{T}}\) at positions (A-A)1 and (C-C)1 was 200 \(\upmu\varepsilon\) and 185 \(\upmu\varepsilon\), respectively. It shows that \(\varepsilon\) at positions (A-A) and (C-C) displayed a symmetrical state. In addition, \(\varepsilon_{\mathrm{L}}\) at positions (C-C)2 and (C-C)4 was 300 \(\upmu\varepsilon\) and 295 \(\upmu\varepsilon\), respectively. It can be seen that \(\varepsilon\) on the upper and lower surfaces at positions (A-A) and (C-C) also presented a symmetrical state.  
It also can be seen from Figure 11 that \(\varepsilon\) increased with the increase of \(H\), and the maximums of \(\varepsilon\) on the longitudinal and transversal sections appeared at the mid-span of the pipeline. This indicates that, with the increase of the distance of the specimen away from the impact area, the strain of the pipeline became small, and the deformation of the pipeline was still in the elastic range even when \(H\) was 2 m.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation  
### Simulation Setup  
We implement CBCAIS on the Opportunistic Networking Environment simulator [25], and evaluate its performance in the data set of Infocom 06 [26]. Infocom 06 is a dataset collected during the Infocom 2006 student workshop. It contains 78 devices and covers approximately four days. The iMotes are programmed to log contacts of all visible mobile devices and a number of external devices that are not used in this paper. Every node has initial credits of 15, and pays one credit for each delivered message. The period \(T\) of calculating the _messages changing number_ is 2500 s, and the \(a\) in Equation (6) is 0.7. By default, the message generation interval is 5-10 s; the message size is 1 MB; the buffer size is 50 MB; the data transmission speed is 200 kBps; and the warm-up phase is 35,000 s. All nodes have the same initial energy, 10,000 J. The scan energy every time for scanning/discovering neighbors is 0.1 J. The transmission energy for a message is 1 J.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
###### Abstract  
With the emergence of numerous low Earth orbit (LEO) satellite constellations such as Iridium-Next, Globalstar, Orbcomm, Starlink, and OneWeb, the idea of considering their downlink signals as a source of pseudorange and pseudorange rate measurements has become incredibly attractive to the community. LEO satellites could be a reliable alternative for environments or situations in which the global navigation satellite system (GNSS) is blocked or inaccessible. In this article, we present a novel in-flight alignment method for a strapdown inertial navigation system (SINS) using Doppler shift measurements obtained from single or multi-constellation LEO satellites and a rotation technique applied on the inertial measurement unit (IMU). Firstly, a regular Doppler positioning algorithm based on the extended Kalman filter (EKF) calculates states of the receiver. This system is considered as a slave block. In parallel, a master INS estimates the position, velocity, and attitude of the system. Secondly, the linearized state space model of the INS errors is formulated. The alignment model accounts for obtaining the errors of the INS by a Kalman filter. The measurements of this system are the difference in the outputs from the master and slave systems. Thirdly, as the observability rank of the system is not sufficient for estimating all the parameters, a discrete dual-axis IMU rotation sequence was simulated. By increasing the observability rank of the system, all the states were estimated. Two experiments were performed with different overhead satellites and numbers of constellations: one for a ground vehicle and another for a small flight vehicle. Finally, the results showed a significant improvement compared to stand-alone INS and the regular Doppler positioning method. The error of the ground test reached around 26 m. This error for the flight test was demonstrated in different time intervals from the starting point of the trajectory. The proposed method showed a 180% accuracy improvement compared to the Doppler positioning method for up to 4.5 min after blocking the GNSS.  
Keywords:INS; inertial navigation system; extended Kalman filter; LEO satellite; SOP; signals of opportunity; IMU; alignment; online calibration +
Footnote †: journal:  
Article Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
# A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals
## 6 Conclusions  
In this work, a water intake monitoring system with animal ID detection and water quality tracking was implemented. The system monitors each drinking event in terms of animal identification, water consumption, drinking behavior, and water quality. The system also can send an alert signal when the water level is too low. The animal identification was performed using aRFID and motion-sensing based camera. The water level is measured by distance sensors. The proposed system was implemented in the remote grazing site. It performs data acquisition and logging automatically. The data are stored on SD cards and displayed in real-time on an LCD screen. Sensing and logging are controlled by low-power microcontrollers. The overall system can be powered by either solar cells or AC power. Besides monitoring livestock drinking behavior, the system could also monitor wild animals' water intake, which would provide new insights for wildlife conservation. The current system automatically records the drinking behavior of 29 cows in a two-week duration in the remote ranch, which could not be done without the proposed system. The system has the potential to save water consumption while providing access to animal health monitoring, which is important for future animal agriculture applications.  
Conceptualization, W.T. and M.W.; methodology, W.T.; software, A.B. and R.E.; validation, R.E. and T.P.; formal analysis, W.T., R.E., and T.P.; investigation, W.T., A.B., R.E., T.P., and M.W.; resources, M.W.; data curation, R.E. and T.P.; writing--original draft preparation, W.T.; writing--review and editing, W.T., S.S., and M.W.; visualization, T.P. and M.W.; supervision, W.T. and M.W.; project administration, W.T. and M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the United States National Science Foundation (NSF) under Grant ECCS-1652944 and ECCS-2015573, and the United States Department of Agriculture (USDA) Western Sustainable Agriculture Research and Education Grant #OW19-341.  
**Institutional Review Board Statement**: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Animal Care and Use Committee of New Mexico State University (The approval number is 2019-022 and the approval period is from October 2019 to September 2023).  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The funders had no role in the design of the study.
###### Abstract  
This study determined antioxidant activity in terms of the 1,1-Diphenyl-2-picrylhydrazyl (DPPH) scavenging ability and total phenolic content of black tea under different infusion and storage conditions. High performance liquid chromatography analysis identified caffeine, (\(-\))-epigallocatechin, (\(-\))-epigallocatechin-3-gallate, (\(-\))-epigallocatechin-3-gallate and (\(-\))-gallocatedchin-3-gallate in the tea sample. The water-tea leaves weight ratio did not affect the DPPH scavenging ability. However, infusion temperature affected the DPPH scavenging activity and the total phenolic content. In the present study, the 50% inhibitory concentrations (IC\({}_{50}\)) for DPPH of black tea infused at 60 to 100 \({}^{\circ}\)C ranged from \(100.0\pm 13.7\) to \(28.4\pm 4.8\) g/mL. The total phenolic content of black tea steeped at 60 to 100 \({}^{\circ}\)C ranged from \(50.4\pm 5.2\) to \(178.6\pm 16.4\) mg gallic acid equivalent/g dry leaf. Black tea exhibited increased antioxidant activity when the infusion temperature was increased. Regarding short-term storage, the DPPH scavenging ability and total phenolic content of black tea did not significantly change within 15 days. This result was consistent for storage temperatures of 4, 9, and 25 \({}^{\circ}\)C.  
antioxidant; black tea; DPPH; storage; time; temperature; total phenolic content +
Footnote †: journal: Applied Soft. 2020, 10, 2685; doi:10.3390/app10082685
## 2 A Voltage Ride-Through (VRT) Control Model for Energy Storage Systems
### Data Preprocessing and Trajectory Segmentation  
1. Input Data Normalization  
To enhance identification speed and accuracy during parameter identification, the three-phase voltage and current at the grid connection point are normalized using the per-unit system:  
\[U_{\text{p.u.}}=\frac{U_{\text{dbc}}}{U_{\text{base}}},I_{\text{p.u.}}=\frac{I_{ \text{dbc}}}{I_{\text{base}}},S_{\text{base}}=\sqrt{P^{2}+Q^{2}} \tag{8}\]  
In the above equations, the base values are defined as \(U_{base}=1.0\text{ p.u.},I_{base}=S_{base}/\sqrt{3}U_{base}\).
2. Coordinate Transformation and Sequence Component Decomposition  
The three-phase voltage parameters are transformed into the synchronous rotating reference frame (d\({}_{\text{dq0}}\)) via the Park transformation:  
\[\begin{bmatrix}U_{d}\\ U_{q}\end{bmatrix}=\frac{2}{3}\begin{bmatrix}-\sin\theta&-\sin(\theta-120^{ \circ})&-\sin(\theta+120^{\circ})\\ \cos\theta&\cos(\theta-120^{\circ})&\cos(\theta+120^{\circ})\end{bmatrix} \begin{bmatrix}U_{d}\\ U_{b}\\ U_{c}\end{bmatrix} \tag{9}\]  
where \(\theta\) is the grid phase angle tracked in real-time by a phase-locked loop (PLL).  
In addition, the three-phase voltage parameters are decomposed into positive-, negative-, and zero-sequence components to analyze asymmetrical grid faults:  
\[\begin{bmatrix}U_{0}\\ U_{1}\\ U_{2}\end{bmatrix}=\frac{1}{3}\begin{bmatrix}1&1&1\\ 1&\alpha&\alpha^{2}\\ 1&\alpha^{2}&\alpha\end{bmatrix}\begin{bmatrix}U_{a}\\ U_{b}\\ U_{c}\end{bmatrix},\;\alpha=e^{i120^{\circ}} \tag{10}\]
3. Dynamic Trajectory Window Segmentation  
Based on the Chinese Standard _Test Procedure for Power Conversion Systems of Electrochemical Energy Storage Systems_[24], the time window thresholds are determined based on the control loop time constants of the energy storage system. The trajectory segmentation is defined as follows:  
* Transient Window (\(t_{0}\)-\(t_{0}+100\) ms): This window captures nonlinear dynamics during fault inception.
* Transition Window (\(t_{0}+100\) ms-\(t_{0}+300\) ms): This window analyzes the parameter adjustment process of the control loops.
* Steady-State Window (\(t_{0}+300\) ms-\(t_{1}\)): This window evaluates long-term stability and harmonic characteristics post-fault.
## 2 Authentication in the Smart Grid (SG) Network
### Authentication Protocols
#### 2.2.4 Obj-5: Trust Management  
Since control systems in the SG are distributed in nature, the establishment and the maintenance of the chain of trust is important [7]. During authentication and communication, the trust among the SG entities such as user-to-network, device-to-network, and device-to-device should be maintained. This can be achieved by introducing a protocol with a trust element or verifying the identity/certificate of the entity, e.g., device attestation.  
[PERSON] and [PERSON] [62] stated that a strong authentication technique is a requisite for all users and devices within the SG network. It is expected that in the near future, the current protocols may not be fast enough requiring a scalable key and trust management system. A Diffie-Hellman-based security aggregation scheme for collecting data, proposed by [PERSON] _et al._[63], generates lower computation and communication overhead, but, does not consider SMs authentication. Recently, [PERSON] _et al._[64] discussed the trust management toolkit, which uses network-flow techniques and reputation-based trust to improve the SG protection system that can work well in the presence of an untrusted SG entity.
### Modelling Flexiblity in Preliminary Design Trade-Off Studies  
Flexibility is a notion that describes \"_a design concept that provides an engineering system with the ability to adapt, change and be reconfigured, if needed, in light of uncertainty\"_[21]. It is different conceptually from a robust design concept, which makes systems functions invariant to changes [22]. Design for flexibility literature (e.g., [23]) suggests conceiving a system architecture with relatively low capability initially, but that allows for expansion if changes occur. To achieve this, the notion of'real options' [24] is used to identify means to embed flexibility in technical systems. A real option is a technical element embedded initially into a design that gives the right but not the obligation to react to uncertain conditions. One example of real option in the satellite business is given by [PERSON] et al. [25] who show that under high uncertainty in demand it is better to launch a set of small, lightweight, affordable satellites and to add more of them as needed, rather than deploying a constellation with few, high-powered, heavy satellites.  
The main challenge when designing flexibility in a product is that flexibility has to be traded against other dimensions (e.g., [22]). For example, highly flexible and modular products could result in systems that are heavier with the risk of degrading performance [26]. For this reason, engineers need more quantitative and information-rich models in order to make confident decisions and to justify more flexible products to the project stakeholders. However, assessing the impact of flexibility is challenged by the need to take into account the likelihood of future events to occur. At present, few, if any, design teams include formally the dynamics of the business scenarios for the future early on in the development process--which leave the decisions on what architecture, or what technology principles to deploy--to tacit and experience-based judgement.  
Works on flexibility quantification use tradespace exploration in combination with scenario planning [13]. To quantify flexibility, [PERSON] and [PERSON] [27] suggest applying a metric called _value weighted filtered outdegree_, which is defined as the number of concepts (represented by both physical and operational parameters) a particular design can be changed into, given an acceptability cost threshold selected by the decision makers.