esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Site Description and Eddy-Covariance Flux Measurements  
The MOFLUX field site is located in central U.S., in the Ozarks region of Missouri state (38\({}^{\circ}\)44.65\({}^{\prime}\)N latitude, 92\({}^{\circ}\)12.00\({}^{\prime}\)W longitude, 219 m elevation; Figure 1). It is part of the AmeriFlux network and has provided continuous eddy covariance measurements of carbon dioxide, water and energy fluxes since 2004. The 32-m tower is surrounded by a continuous broadleaf deciduous forest within a 1-km radius. The tower extends about 10 m above the top of the canopy. The forest is oak dominated, with two thirds of the trees belonging to white and red oak species, and the remaining third being hickory, sugar maple, ash, and juniper. With this abundance of isoprene-emitting species of oaks, the Ozarks area has gained the nickname of 'isoprene-volcano', holding the record for the highest isoprene fluxes ever reported (53.3 mg m\({}^{-2}\) h\({}^{-1}\)) during the 2011 campaign [30]. The relatively thin silt loam soils prevent the trees from reaching deep soil moisture during droughs, which exacerbates plant water stress [63]. Soil water retention curves ([64]) for the Weller silt loam soils near the MOFLUX eddy covariance tower indicate a wilting point of 0.13 m\({}^{3}\) m\({}^{-3}\) for the top 0-30 cm soil depth, and 0.23 m\({}^{3}\) m\({}^{-3}\) for soil depths below 30 cm. These figures correspond to the water content at \(-1.5\) MPa soil matric potential, a value that commonly defines \(\theta_{\rm W}\)[65].  
The area is classified as a humid subtropical climate in the Koppen classification and is prone to droughs between July and September [66; 67]. In 2011 and 2012, the site has respectively suffered from mild and severe droughs for which two campaigns of VOC flux measurements were conducted. [30] used a chemiluminescence analyzer for the 2011 campaign spanning from 18 May to 2 September. The dataset is provided with a half-hour frequency. The 2012 campaign of [32] was extended to other compounds than isoprene. Eddy covariance fluxes were calculated half-hourly with a proton transfer reaction quadrupole mass spectrometer (PTR-Quad-MS) and covered a nearly six-months period from 2 May to 22 October. According to the U.S. Drought Monitor, the most intense period  
Figure 1: The location of the MOFLUX site in the Missouri state and the 32-m AmeriFlux tower overlooking the forest, approximately 10 m above the top of the canopy.  
of drought occurred in August 2012 with exceptional drought conditions (i.e., level D4) affecting more than a third of Missouri land ([[https://www.drought.gov/states/missouri](https://www.drought.gov/states/missouri)]([https://www.drought.gov/states/missouri](https://www.drought.gov/states/missouri)), accessed on 23 June 2021).
## 2 System Model and Problem Formulation
### SINR Assignment for One D2D Multicast Group  
Without loss of generality, we consider the \(m\)-th D2D group, which reuses spectrum resources with the CUs specified in \(\mathcal{S}_{m}\). According to constraint C4 in Equation (5), if the spectrum resource of CU \(k\) is reused by D2D group \(m\), \(0\leq p_{k}^{C}\leq p_{thr}^{C}\) should be satisfied. By substituting Equation (9) into constraint C4, we have  
\[0\leq\frac{H_{m,n}^{DD}+\widetilde{H}_{k,m,n}^{CD}\gamma_{n,m,k}^{D}}{H_{k,B}^ {CB}H_{m,n}^{DD}-\widetilde{H}_{m,B}^{DB}\widetilde{H}_{k,m,n}^{CD}\Gamma_{k}^ {C}\gamma_{n,m,k}^{D}}\Gamma_{k}^{C}\sigma_{0}^{2}\leq P_{thr}^{D}. \tag{13}\]  
Form above inequality, we can get  
\[\left\{ \begin{array}{l}\gamma_{n,m,k}^{D}<\frac{H_{k,B}^{CB}H_{m,n}^{DD}}{ \widetilde{H}_{m,B}^{DB}\widetilde{H}_{k,m,n}^{CD}\Gamma_{k}^{C}},\\ \gamma_{n,m,k}^{D}\leq\frac{p_{thr}^{D}H_{k,B}^{CB}H_{m,n}^{DD}-H_{m,n}^{DD} \Gamma_{k}^{C}\sigma_{0}^{2}}{\widetilde{H}_{k,m,n}^{CD}\Gamma_{k}^{C}\left( \sigma_{0}^{2}+P_{thr}^{D}\widetilde{H}_{m,B}^{DB}\right)}.\end{array} \right. \tag{14}\]Define  
\[\gamma^{D}_{n,m,k,\max}=\min\left\{\frac{H^{CB}_{k,B}H^{DD}_{n,n}}{\widetilde{H}^{ DB}_{n,B}\widetilde{H}^{CD}_{k,m,n}\Gamma^{C}_{k}},\frac{P^{D}_{thr}H^{CB}_{k,B}H^{DD}_{m,n} -H^{DD}_{m,n}\Gamma^{C}_{k}\sigma^{2}_{0}}{\widetilde{H}^{CD}_{k,m,n}\Gamma^{C} _{k}\left(\sigma^{2}_{0}+P^{D}_{thr}\widetilde{H}^{DB}_{m,B}\right)}\right\}. \tag{15}\]  
According to Proposition 1, the multi-objective optimization model for D2D group \(m\) can be simplified to the following problem:  
\[\min_{\{\gamma^{D}_{n,m,k}\}\sum\limits_{k\in\mathcal{S}_{m}} \Phi\left(\gamma^{C\;*}_{k,m},\gamma^{D}_{n,m,k}\right)\] \[s.t. C1:\sum\limits_{k\in\mathcal{S}_{m}}\log_{2}(1+\gamma^{D}_{n,m,k })\geq\log_{2}(1+\Gamma^{D}_{m}), \tag{16}\] \[C2:0\leq\gamma^{D}_{n,m,k}\leq\gamma^{D}_{n,m,k,\max},\forall k \in\mathcal{S}_{m}.\]  
By taking the second order derivative of the \(\Phi\) versus \(\gamma^{D}_{n,m,k}\), we can get  
\[\frac{\partial^{2}\Phi}{\partial\gamma^{D}_{n,m,k}} =\frac{2\eta\sigma^{2}_{0}H^{CB}_{k,B}H^{DD}_{m,n}\widetilde{H}^{ DB}_{m,B}\widetilde{H}^{CD}_{k,m,n}\Gamma^{C}_{k}}{P^{D}_{m,k,\max}}\cdot\frac{H^{CB}_{ k,B}+\widetilde{H}^{CD}_{k,m,n}\Gamma^{C}_{k}}{H^{CB}_{k,B}H^{DD}_{m,n}- \widetilde{H}^{DB}_{m,B}\widetilde{H}^{CD}_{k,m,n}\Gamma^{C}_{k}\gamma^{D}_{n,m,k}}\;\;, \tag{17}\] \[>0\]  
indicating that \(\Phi\) is a convex function. Then, it is obvious that the optimization problem in Equation (16) is a convex optimization problem about \(\gamma^{D}_{n,m,k}\)[24]. In order to solve Equation (16), we introduce the non-negative Lagrangian multipliers \(\lambda\geq 0\), associating with the constraint C1 in Equation (5).
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Results and Analysis
#### 4.4.1 Influence of System Parameter Variations on Network Performance  
In our network, the variation in parameters \(\alpha\) and \(
## 4 Results and Discussion
### Splitting Tensile Strength  
In this study, the splitting tensile strength standard of RCLMFRHCC for concrete pavement emergency repair was specified as \(\geq\)4.2 MPa after 28 days of curing. Furthermore, this study aimed at a splitting tensile strength of 3.2 MPa for 4 h. The results of this study showed that the strength of 28 days of curing was satisfied in all mixtures. However, in the case of splitting tensile strength at the curing time of 4 h, the plain mixture without reinforcing fibers showed a strength of approximately 3.1 MPa, which was slightly lower than the target splitting tensile strength (Figure 9). Furthermore, compared with the \"plain\" mix, the mixes with reinforcement fibers showed splitting tensile strength values of \(\geq\)3.2 MPa and 4.5 MPa at 4 h and 28 days, respectively. Thus, the reinforcement fiber increased the splitting tensile strength. RCLMFRHCC for concrete pavement emergency repairs showed high hydration heat in the early stages of curing, improving the initial strength [1; 2; 3; 5; 6]. Thus, although cracks occurred within the concrete, the reinforcement fibers effectively prevented the generation and growth of cracks [16; 17; 18; 19; 20; 21; 22; 23]. This effect was seen in the increased splitting tensile strength of RCLMFRHCC. Furthermore, the addition of reinforcing fibers is more effective in increasing tensile strength than compressive strength [22; 23]. Furthermore, the case using macro synthetic fibers showed better splitting tensile strength than the mixes with natural jute fibers and PVA fibers. This is because the macro synthetic fiber is a structural fiber, which is used to replace steel fiber and improves the structural performance of the concrete [22; 23]. However, natural jute fibers and PVA fibers are crack-control fibers, which are used to improve durability rather than structural performance [16].  
Figure 8: Flexural strength of RCLMFRHCC.  
Figure 9: Splitting tensile strength of RCLMFRHCC.
## 5 Parameter Analysis
### Network Lifetime  
In a 6 TiSCH network, the network lifetime is determined by the minimum lifetime of the nodes closest to the root. If the battery of such nodes is depleted, network connectivity is disrupted. A node's lifetime is primarily influenced by its average energy consumption during each slotframe. In smaller slotframes, timeslots repeat more frequently, enabling higher transmission rates. However, this results in nodes remaining active more often, leading to increased energy consumption. Conversely, a larger slotframe size can cause higher latency and reduced PDR, as nodes have fewer transmission opportunities. Therefore, the slotframe size can be selected based on the critical performance parameters specific to the application's requirements.  
An energy consumption model based on real measurement has been used for the analysis [19]. It describes the energy consumed by every node in each type of cell. Taking the node's initial energy of \(10,157.4\times 10^{6}\) u\(c\), average consumption is determined as the sum of the energy spent in every slot. A node in IEEE 802.15.4e TSCH consumes energy in 6 different types of time slots: 54.5 u\(c\) in TxDataRxAck to send application data and receive an acknowledgment (ACK); 32.6 u\(c\) in RxDataTxAck to receive data and sends back an ACK, 49.5 u\(c\) in TxData to sends broadcast packet without an ACK; 22.6 \(\mu c\) in RxData to receive without ACK; 6.4 u\(c\) in Idle slot during which a node listens for data but does not receive any; and 0 u\(c\) in a sleep slot, where the node's radio remains turned off. Node's lifetime \(life(n)\) in a year is given as  
\[life(n)=\frac{E(n)\cdot SF}{E_{c}(n)\cdot 3600\cdot 24\cdot 365} \tag{14}\]  
where \(E(n)\) is the initial energy of a node \(n\) in u\(c\), and \(E_{c}(n)\) is the average energy consumed by the node during a slotframe in u\(c\). The minimum lifetime of any node, expressed in years, determines the network lifetime and is given by  
\[Net_{life}=\underset{n\in N}{\text{min}}life(n) \tag{15}\]  
In energy-critical applications, longer network lifetime can be achieved by employing longer slotframes.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 3 Simulation Results
### Tests under Dynamic Conditions
#### 3.3.1 Amplitude and Phase-Angle Modulations
The same considerations hold true also for frequency estimation: the SV-TF is also in this case the less sensitive to the sinusoidal frequency oscillation (see Figure 18). The other algorithms show similar errors, with the SV-F approach being the least accurate, even if better dynamic performance would be enabled by different filter design. As far as ROCOF measurements is concerned, SV-IpDFT is again more prone to errors for the aforementioned reasons. The other algorithms are quite similar and it is interesting to notice that SV-TF is no longer the best one, mainly because, when sinusoidal variations are involved, the truncation of the Taylor expansion limits the tracking capabilities. A higher expansion order might help reducing the RFE values. Figure 16: Maximum TVE results under amplitude modulations with modulation frequency \(f_{m}\). Figure 17: Maximum TVE results under phase-angle modulations with modulation frequency \(f_{m}\). Figure 18: Maximum \(|\)FE\(|\) results under phase-angle modulations with modulation frequency \(f_{m}\).
# UAV Support for Mission Critical Services
## 3 UAV Characteristics, Classifications and Commercial Deployments Status  
UAVs were developed almost 50 years ago for military services. They recently expanded to non-military applications, including forestry, aerial photography, real-time video transmission, product deliveries, agriculture, surveillance and infrastructure inspections, including power grid or road traffic monitoring [22]. The UAV market has exploded in the last few years due to the large-scale production of small, quadcopter type UAVs with efficient batteries and electrical engines. Along with the amateur usage, there is also interest in the large-scale commercial use of this technology.
## 4 Discussion of Computation Complexity  
Since the input data lengths are quite small and the diagrams representing the data flow during the computation are relatively simple, it is not difficult to estimate the computational complexity of the synthesized algorithms. Tables 1 and 2 show the number of quaternion and real-valued arithmetic operations for computing small-size QDFTs. One can see that the implementation of the obtained solutions requires fewer multiplications than the implementation based on naive methods for computing QDFTs. Reducing the number of multiplications is especially important when designing specialized fully parallel VLSI processors. Minimizing the number of required multipliers allows reducing the dissipated power and reduces the cost of implementing the entire system. This is because the hardware multiplier is more complex than the adder. It also takes up much more space on the chip than the adder. It is known that the complexity of implementing a hardware multiplier grows quadratically with the operand size, while the hardware complexity of a binary adder grows only linearly [32]. Therefore, reducing the number of multipliers is of great importance in the hardware implementation of the developed algorithms, even if this leads to a small increase in the number of adders.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size** & \multicolumn{2}{c}{**Naive Methods**} & \multicolumn{2}{c}{**Proposed Algorithms**} \\ \hline \(N\) & Number of multiplications & Number of additions & Number of multiplications & Number of additions \\ \hline
2 & — & 2 & — & 2 \\
3 & 4 & 6 & 2 & 8 \\
4 & 4 & 12 & 1 & 8 \\
5 & 16 & 20 & 5 & 23 \\
6 & 16 & 30 & 4 & 22 \\
7 & 36 & 42 & 8 & 46 \\
8 & 32 & 56 & 4 & 26 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of quaternion-valued arithmetic operations necessary to calculate small-size QDFT.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Size** & \multicolumn{2}{c}{**Naive Methods**} & \multicolumn{2}{c}{**Proposed Algorithms**} \\ \hline \(N\) & Number of multiplications & Number of additions & Number of multiplications & Number of additions & Number of right or left shifts \\ \hline
2 & — & 8 & — & 8 & — \\
3 & 64 & 72 & — & 40 & 8 \\
4 & 64 & 96 & 4 & 40 & — \\
5 & 256 & 272 & 12 & 116 & 4 \\
6 & 256 & 312 & — & 104 & 16 \\
7 & 576 & 600 & 32 & 216 & — \\
8 & 512 & 608 & 16 & 128 & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of real-valued arithmetic operations necessary to calculate small-size QDFT.  
Figure 7: Data-flow diagram for the 8-point QDFT.
# An Architecture for Reliable Transportation of Delicate Goods
## 5 Workflow
### Merchandise Aggregation and Disaggregation  
The aggregation of merchandise in a single container is a common practice. It is important to remember that the devices should not be removed from the merchandise. Otherwise, they might not be able to correctly evaluate the transport conditions. That way, whenever the merchandise is added to a container, the device will stay associated to the merchandise and, as consequence, it will go inside the container too. This scenario poses several obstacles and challenges:  
* Inside a container there might exist several monitoring devices, associated to merchandise with distinct transport requirements.
* Getting access to the merchandise inside the container might not be easy or even possible, without extra work or without jeopardizing the remaining goods.
* Type B devices probably will not be accessible to activate the advertise mode, which means that it may not be possible to collect the data related with the non-compliances, until the merchandise is removed from the container--too late to restore the optimal transport conditions and/or maybe to save the merchandise.
* It may not even be possible to access type A devices from outside the container, using a simple smartphone.
* If there are many devices inside the container, it might not be easy to access a specific one.  
The next sections introduce the specific parts of the architecture conceived to surpass these obstacles/challenges, by adding the type (c) topology.
###### Abstract
As a result, a novel aircraft energy management approach was proposed, which is the integrated power and thermal management system (IPTMS). Many institutions, such as Honeywell, the European Space Agency, and the United States Air Force, have focused on energy-optimised aircraft projects and developed IPTMSs for advanced thermal energy design. The United Kingdom's Aerospace Technology Institute (ATI) has listed novel thermal management systems for EPA as their priority study strategy [12]. It is highly likely that an IPTMS is a big step toward MEA/AEA and will play a more important role in energy management for aircraft [13]. There are published papers that have reviewed relevant fields of TMSs or PMSs, but there are limited sources in the public domain on the integrated system of a PMS and TMS. The authors in [14] reviewed the growing challenges of TMSs for military and civil aircraft due to increasing electrification and discussed future opportunities and potential solutions based on a simple classification of TMS factors and research topics. This article primarily focuses on the TMS for conventional aircraft and did not explore more details of an IPTMS for civil electrical propulsion aircraft. In [15], the authors review the TMS developments for HEA. They focused on different TMS architectures for the key components in electrical propulsion system to investigate an optimised TMS architecture under different operating conditions. Furthermore, a comprehensive review of the TMS challenges for hybrid-electric aircraft is presented in [16]. They investigated the challenges and solutions for the TMS components and subsystems to identify the future opportunities at the component level. The authors in [17] reviewed the architectures, control system design, and energy management strategies for electric propulsion systems to reduce fuel burn and emissions and presented an overview of future trends in aviation electrification. While the authors presented a wide literature review of energy management strategies, they did not review the strategies for managing energy flows in thermal and power domains. Previous research has explored thermal and power management for Electrical Propulsion Aircraft (EPA), yet a gap exists in the literature regarding various types of IPTMSresearch for potential solutions in civil EPA. This paper aims to address this gap by reviewing recent IPTMS progress and exploring potential design solutions for civil aircraft. The paper is organised as follows. Section 2 presents the IPTMS in EPA, introducing the architectures and challenges of EPA, TMS cooling strategies, and power management optimisation approaches. Section 3 focuses on the architecture design of an IPTMS in two domains: (1) an IPTMS with energy recovery and (2) an IPTMS with an advanced electrical propulsion system (EPS) and TMS. Section 4 describes the power management (PM) design of the IPTMS. Sections 5 and 6 present the modelling and analysis methods of the IPTMS. Section 7 discusses the challenges and future opportunities of IPTMS, suggesting the requirements for future IPTMS design. Finally, the conclusion is presented in Section 8.
## 3 Methodology
### Optimization Process  
The geological characteristics of the Pohang Basin are represented by multi-layered aquifers and heterogeneous reservoir properties. Moreover, the basin contains faults, which need to be taken into account for safe injection and storage design to achieve the maximum storage capacity. For example, the increased pore pressure during a CO\({}_{2}\) injection process may cause pre-existing fault reactivation. In addition, induced tensile fractures may occur in the vicinity of the injection well if the operating BHP exceeds the fracture pressure. Therefore, the operating conditions and the relief well location should be carefully determined, both for safe storage and storage capacity.  
The relief well location and the CO\({}_{2}\) injection rate were chosen as the optimization parameters in an attempt to achieve maximum storage capacity. It was assumed that the relief well produces formation fluid with a constant BHP (=7.5 MPa), determined by the hydrostatic pressure, which implies that any increment in the pore pressure induced by the injection process will be removed. Once the location of the relief well was properly optimized, safe injection and storage design could be achieved with the maximum injected CO\({}_{2}\) mass.  
The optimization process was performed with CMG CMOST and the DECE (Designed Exploration and Controlled Evolution) optimizer for three injection scenarios, a total of 300 years, i.e., 10-, 20-, and 30-year-injection period and rest was a monitoring period. In the DE stage, the optimizer generates simulation experiments through the experimental design and the Tabu search techniques, as shown in Figure 5. The experiments employ a Latin Hypercube design to generate the initial experiments, prior to the simulation run. Based on the simulation results, the optimizer determines if the objective function is achieved. During the simulation, the optimizer repeats the creation of additional experiment cases as the Tabu search technique reduces the search area. Once the targeted number of simulation jobs was reached, 500 in this study, the optimization process is completed and the optimized case was analyzed. In the CE stage, the optimizer improves the quality of the solution with a statistical analysis that tests all candidate values. While the process is being performed, the DECE algorithm checks the rejected candidate values to determine if previous decisions are still valid, and prevents the process from being trapped in a local optimal solution [51]. The steps allow the DECE to handle both continuous and discrete parameters, which is suitable for finding the optimal location of a relief well and the CO\({}_{2}\) injection rate in a target aquifer. In relief well optimization, the points in the two candidate sectors, A and B, contributed to the range of optimization parameters for the well location (Figure 6), with the injection rates ranging from 10 tons/day to 200 tons/day.
# PK-APF: Path-Keeping Algorithm for USVs Based on Artificial Potential Field
## 3 Experiments
### MATLAB Implementation
#### 3.1.2 Path Planning in Narrow Corridor  
During USV navigation, the narrow corridor between obstacles often affects the behavior of USVs. The positions of the obstacles are set at (_14.2,15_) and (_15.8,15_). As multiple obstacles simultaneously affect the repulsive force on the USV, when obstacles are some distance apart but close, a narrow corridor is created for the USV to travel along. Figure 7 presents the APF and PK-APF algorithms' planning paths in a narrow corridor environment. After repulsing by multiple obstacles, the traditional APF will be far away from the obstacles and continue to move towards the goal point, resulting in a significant deviation between the original planned path and the actual navigation path. However, because PK-APF has the attractive force of virtual foot points and returns the original path immediately, it helps the USV complete high-precision path-keeping in the narrow corridor path composed of obstacles and complete path planning.  
The statistics of path-keeping errors are shown in Figure 8. As PK-APF can pass through the narrow corridor, the maximum deviation error from the original path is 0.1983 m. Meanwhile, it can be seen that it can achieve high-precision overlap with the original planned path except for avoiding obstacles, and the deviation of the original path is 0 m. In the path planned by APF, the maximum deviation error from the original path is 1.562 m. PK-APF reduced the maximum deviation error by 80.92% compared with APF. The planned path of PK-APF is similar to that of APF because PK-APF passes between obstacles. Therefore, the total time step of PK-APF is similar to that of APF.  
Figure 6: Path-keeping error result with temporal force.