esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Generalized boundary conditions characterize EM field relations across two-dimensional surfaces based on characteristic parameters of surfaces. Research related to GBCs constitutes an important theoretical foundation of surface electromagnetics. Based on generalized boundary conditions, scattering properties of EM surfaces can be analytically computed for applied fields with arbitrary propagation directions and polarizations once surface susceptibilities are determined.  
In this paper, generalized boundary conditions were investigated in depth. Firstly, five representative features were discussed for categorizing various types of two-dimensional surfaces. Then the evolution of boundary condition theories were reviewed and the relations between scattering coefficients and different kinds of parameters were discussed. For surface problems related to homogeneous effective, isolated-scatterer, isotropic, single-layer and space-wave mode, the extraction method of surface susceptibilities from limited sets of full-wave simulations were introduced and the results were utilized to compute reflection and transmission coefficients with arbitrary incident angles and polarizations. Next, the characterization methods for two representative surface problems were derived. For isolated-aperture surfaces, surface susceptibilities of their complementary structures were utilized with field relations defined by [PERSON]'s principle, where the surface porosities required in [36] was not necessary. For surface-wave mode characterization, the surface impedances were analytically computed based on the equivalent circuit model and the transverse resonance technique, where surface susceptibilities were utilized to determine the \(\theta\)-related sheet impedances. Examples of surface characterizations with different features were given, where good agreement between computed and simulated results was observed, validating the effectiveness of generalized boundary conditions.  
Figure 20: Comparison between the simulated and computed dispersion diagram for the impenetrable metasurfaces with different \(a\): (**a**) \(a\) = 1.5 mm (**b**) \(a\) = 2.5 mm (**c**) relative error.  
Future work is still required. One necessary and challenging problem is the characterization of spatially dispersive surfaces, which are quite useful for surface applications having periods comparable to corresponding wavelengths. Characterization methods of anisotropic and multiple-layer surfaces also require further research and development. These issues should be properly addressed in order to make generalized boundary conditions applicable to more kinds of surface problems.  
Conceptualization, X.L. and F.Y.; methodology, X.L. and F.Y.; software, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L., F.Y., M.L. and S.X.; supervision, F.Y.  
This research was partially funded by Beijing National Research Center for Information Science and Technology (BNRist).  
The authors declare no conflict of interest.
## 4 Experimental Results and Performance Evaluation  
The simulation is run on a machine with 3.60 GHz and 16 GB RAM equipped with a Ryu SDN controller, and Mininet version 2.2.2. The study considered two (2) real network topologies: ATT (25 nodes, 58 edges) and Spain (30 nodes, 110 edges). These topologies are created by writing a script in the Mininet emulator. Each switch is deployed using a software switch (OpenVswitch) in Mininet running on virtual machines. Typically, the OpenFlow switch is constrained with limited capacity and OpenVswitch can support many entries. The study modifies OpenVswitch to support the limited as in real OpenFlow switch. The study also assumes traffic in ATT and Spain network topologies follows the Poison (interval, packet size) distribution. The D-ITG Srivastava et al. [36] utility is used to generate the traffic based on the Poison model. Random traffic flows demand pairs correspond to a source and destination in the network were generated in Mininet.  
The simulation results were reported, and the performance of the proposed scheme is evaluated against its counterpart benchmarking work under different scenarios. The results of the proposed solution are compared based on the following metrics: path stretch, path setup time, Throughput, packet delivery ratio, and flow table occupancy. It is effectiveness and improvement were also discussed and analyzed in the following subsections.
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 1 Introduction
### In-Situ Resources Utilisation (ISRU)  
In-Situ Resource Utilization (ISRU) involves the practice of collecting, processing, storing, and using materials brought to, found, or manufactured on other celestial bodies [12]. ISRU aims to harness and utilize local or in-situ resources to create products and services for robotic and human exploration and sustained presence [14]. Local resources include 'natural' resources found on extraterrestrial bodies, residues, garbage, as well as equipment and hardware that are no longer used [14], such as:  
* Water [15];
* Solar wind implanted volatiles (hydrogen, helium, carbon, nitrogen, etc.) [16];
* Vast quantities of metals in mineral rocks and soils [17];
* Atmospheric constituents [18];
* Human-made resources (trash, waste from human crew, discarded hardware) [19].  
Instead of bringing everything from Earth (Section 1.1), ISRU covers a wide range of potential applications [20], technologies [21] and technical disciplines [22]. According to [PERSON] et al. [14] ISRU covers three broad areas:  
* In-Situ Propellant and Consumable Production;
* In-Situ Construction;
* In-Space Manufacturing with, for example, ISRU-Derived Feedstock.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Whisper
#### 3.4.2 System Deployment  
Wideband sounding experiments were conducted over a 170 km path, East-West, from Malvern to Cobbet Hill radio station in UK during the spring of 2001. The transmitter at Malvern used a 100 W power amplifier which fed a droopy loaded dipole. The sounder receiver was located at Cobbet Hill, which is an electrically quiet radio station, and was fed from a wideband (2-8 MHz) dipole antenna.  
Initial sounding experiments gathered wideband records of approximately 140 s duration every five minutes during day and night. PN-1023 sequences at a rate of 61.4 kchips/s were used to measure 8192 complex channel impulse responses. To decide which carrier frequency was used for each experiment a chirp sounder was located at the transmission site. Prior to each wideband transmission a 10 W linear chirp sounding from 2 to 16 MHz was performed. Moreover, this chirp signal was also used to generate ionograms and compute absolute time-of-flight due to the fact that both end links were synchronized to GPS time.
# Push/Pull Parts Production Policy Optimization in the ATO Environment
## 3 Bi-Objective Model for Push/Pull Policy Selection in an ATO Environment
### Nomenclature  
\begin{tabular}{l l} \hline Indices & \\ \(i=\) & \(1\):... \(J\) parts \\ \(j=\) & \(1\),... \(J\) production processes \\ \(p=\) & Production strategy (MTO or MTS) \\ Part Parameters & \\ \(a_{i}\) & agility of part \(i\) [pieocs/h\({}^{2}\)] \\ \(EPQ_{i}\) & Equivalent Pallet Quantity, number of parts to be stacked on the pallet of part \(i\) [pcs/pallet] \\ \(A1_{i}\) & upper limit to the dimension of part \(i\) [mm] \\ \(A2_{i}\) & lower limit to the dimension of part \(i\) [mm] \\ \(T_{i}\) & thickness of part \(i\) [mm] \\ \(Q_{i}\) & annual required pieces for part \(i\) [pcs/year] \\ \(B_{i}\) & Batch dimension according to MTS policy for part \(i\) [pcs/batch] equivalent to the Economic Order Quantity (EQO) according to ([PERSON] et al., 2010) \\ \(O_{i}\) & Number of orders of the part \(i\) in a pure pull MTO policy [orders/year] \\ \(E_{i,p}\) & number of productive event of part \(i\) for production strategy \(v\) [productive event/year] \\ \(PR_{i,p}\) & number of pallet required by applying the production strategy \(v\) to manage part \(i\) [pallet] \\ \(EPQ_{i}\) & Equivalent Pallet Quantity for part \(i\), i.e., n\({}^{c}\) of stack parts in an EPLal pallet [pcs/pallet] \\ \(EPQ_{i}\) & Equivalent Pallet Base Quantity for part \(i\), i.e., n\({}^{c}\) of parts Notable in the pallet base [pcs/layer] \\ \(EPQ_{i}\) & Equivalent Pallet Height Quantity for part \(i\), i.e., n\({}^{c}\) of stackable levels of \(EPBQ_{i}\) [levels/pallet] \\ \(T_{S_{ij}}\) & setup time of part \(i\) in the process \(j\) [hours/productive event] \\ \(T_{C_{ij}}\) & production time of part \(i\) in the process \(j\) [hours/pieocs] \\ \(T_{STOT_{i}}\) & total setup time of part \(i\) [hours/productive event] \\ \(T_{CTOT_{i}}\) & Total production time of part \(i\) [hours/pieocs] \\ \hline \end{tabular}
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 4 Experimental Results and Analysis
### Communication Complexity Assessment  
The effectiveness of the incentive mechanism was tested by training on the FEMNIST dataset and the CIFAR-10 dataset. The results of comparing our proposed ETFL scheme with the Vanilla FL scheme, FedAvg scheme, and the FAIR scheme from reference [27] are shown in Figure 13. The iteration time of our ETFL scheme fluctuates around 22 s, significantly lower than the iteration times of FedAvg and the FAIR scheme. This is because of the effect of the incentive mechanism introduced in ETFL, which encourages nodes to participate in the training process, thereby reducing local training time and decreasing the time gap for local gradients to reach the server.  
Figure 12: Parameter aggregation times for several scenarios for this.  
Figure 13: Comparison of one iteration time on FEMNIST Dataset and CIFAR-10 Dataset.  
Simultaneously, we compared the training times for the ETFL scheme proposed in this paper, the Vanilla FL scheme, the FedAvg scheme, and the FAIR scheme when the model accuracy reached 95% on two different datasets, as illustrated in Figure 14. Due to the inclusion of an incentive mechanism in our approach, it facilitated node participation in the training process. Furthermore, the rewards provided to nodes encouraged them to upload more precise model parameters to the server, thereby reducing the time required for global model aggregation.
## 5 Multi-Energy Microgrid Double-Layer Planning Model
### Multi-Energy Microgrid Planning Process  
The planning model of the multi-energy microgrid optimization also has two parts: planning optimization and operation scheduling optimization. The configuration scheme is optimized based on the overall energy load level in the microgrid system under the given constraints. In alternative energy supply and storage equipment, considering Equation (42) and the optimal operation conditions in the system's whole life cycle, an optimized combination scheme for energy supply devices and energy storage devices of the microgrid system is proposed here with the equipment type and quantity as optimization variables. The planning optimization denotes a non-linear integer programming algorithm that does not readily provide an analytical solution, so this study instead employed the quantum-behaved particle swarm optimization (QPSO) algorithm [28]. Considering that the velocity vectors are usually assumed to be small values during the iterative process of the traditional PSO algorithm, one of the main drawbacks of the traditional PSO algorithm is that the solution of the problem is easy to trap into the local optimum rather than the global optimal. Therefore, in this paper, the QPSO algorithm is employed to avoid this problem. By using the wave function to describe the state of a particle instead of the velocity as in a traditional PSO algorithm, and by improving the dynamic behavior of particles, the solution of the problem can effectively avoid falling into local optimum.  
Next, based on the hourly load demand curve in the planning period, the power supply of the microgrid system, the demand profile of the microgrid system, and the operation constraints in the energy supply/storage equipment, an hourly optimization scheduling scheme for each component is built by combining the equipment configuration scheme given in the planning part and taking the start/stop and output of the energy supply and storage equipment. Scheduling optimization denotes a mixed non-linear optimization problem, which can also be solved through the QPSO algorithm. Economical indicators under a certain planning scheme throughout the system's operation can be obtained at the operation level. Thus, they play an important part in the economical target of microgrid system planning. Optimizing the operation and scheduling level is virtually a sub-planning-level optimization process.  
The optimized operation and scheduling level denotes the economic performance of the microgrid system under normal operation conditions. The economical efficiency converted by the energy supply reliability of the microgrid system is also calculated to determine the economic loss expectation produced under fault conditions and improve the economical indexes. This process is illustrated in Figure 2.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 3 Methods
### Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMD)  
#### 3.3.1 Empirical Mode Decomposition (EMD)  
EMD algorithm [47] attends to decompose a complex series data to several basic ones, which is under the assumption that the series data is influenced by several simple factors. The phase of simple factors is composed of Intrinsic Mode Functions (IMFs), which satisfy 2 conditions: (i) in the whole series data, the number of extrema and the number of zero-crossings must be equal or differ by one; and (ii) the mean value of upper and lower envelopes in each signal is zero. The detailed process can be described as following:  
1. Mark all the local minimums and maximums in the series data \(S(t)\).
2. Apply cubic spline method to determine the lower and upper envelops along the time line.
3. Obtain the local mean \(m(t)\) according to the lower and upper envelops.
4. \(temp_{1}(t)=S(t)-m(t)\), if the variance of \(temp_{1}(t)\) is less than a pre-defined threshold, then \(IMF_{1}(t)=temp_{1}(t)\). Otherwise, replace \(S(t)\) by \(temp_{1}(t)\) and repeat the above steps.
5. \(S(t)=S(t)-IMF_{1}(t)\), repeat the above process to get \(IMF_{2}(t)\), \(IMF_{3}(t),\cdots,IMF_{K}(t)\). The value of K is determined by the termination condition.
6. In sum, we get \(S(t)=\sum_{i=1}^{K}IMF_{i}(t)+res(t)\) where \(res(t)\) is a term to indicate the overall trend of series.
## 1 Introduction
### Related Work
The obtained accuracy with this configuration is 4.3 cm horizontal and 4 cm vertical. The second case study is a corridor configuration of \(1200\times 180\) m and the camera is pre-calibrated. Without GCPs in BBA procedure, an accuracy of 5.9, 3.3 and 7.0 cm is reached on \(x\), \(y\) and \(z\) components, respectively. A new approach concerning relative aerial control is proposed for integrated sensor orientation in [21]. To some extent, the procedure is simpler since the boresight matrix vanishes from the model; moreover, high-grade dual-frequency GNSS receiver can be replaced by single-frequency and carrier-phase GNSS receiver since relative aerial control is more resistant to GNSS bias. A block configuration is carried out with relative aerial control [6]. With one GCP and partially biased GNSS data, the accuracy is maintained compared to without GNSS bias and is of 2.9, 2.2 and 3.8 cm on \(x\), \(y\) and \(z\) components, respectively. An dynamic network is proposed to tightly integrate GNSS/INS measurements into BBA procedure in [22]. For a two-flight-strip acquisition of corridor configuration with pre-calibrated boresight, lever-arm and camera intrinsics, an accuracy of 2.5, 1.5 and 1.2 cm on easting, northing and vertical components, respectively, is obtained without GCPs.
## 3 Results and Discussion
### CO Diffusion Law  
The experimental findings indicate a distinction in the CO transportation pattern between V-shaped tunnels and flat tunnels. Figure 11a,b shows the work of [PERSON] et al. [31] and the conclusions drawn in this study, respectively. The work of [PERSON] et al. [31] shows the CO transport law under the construction of a flat tunnel. The peak CO concentration in the flat tunnel decays progressively with increasing distance from the closed end. Instead, the peak CO concentration within the V-shaped tunnel undergoes a fluctuating decrease along the longitudinal direction of the model. As illustrated in Figure 11b, the peak CO concentrations at 5.8 m and 13.8 m from the closed end of the model are higher than the previous measurement point, but still show a decreasing trend in general. This makes V-shaped tunnels, such as underwater tunnels, prone to generating additional pollutant accumulation areas behind the working face, making the safety of ventilation in the rear space of such structural tunnels a concern. A subsequent analysis of the effects of the four influences mentioned earlier on CO transport in the V-shaped tunnel is made.
## 3 Results and Discussion
### Quantitative Comparison of the Present Method and Previous Method
According to the previous studies, blood viscosity was employed to detect hypertensive and diabetic rats [24; 25]. Under an ex vivo rat bypass loop, variations of blood viscosity were obtained over time after continuously injecting 1\(\times\) PBS into a closed loop (i.e., hemodition condition). Lastly, malaria-infected RBCs were detected via RBC aggregation or RBC deformability [26]. As a limitation of the proposed method, the blood sample was prepared as a suspension by adding RBCs into a specific diluent, rather than whole blood. Additionally, this study assumed that the blood sample behaved as a Newtonian fluid. However, if a blood sample flows at lower shear rates, RBC aggregation contributes to increasing the blood viscosity. Thus, blood viscosity tends to decrease by increasing the shear rate. While blood samples behave as non-Newtonian fluids, a non-Newtonian model should be taken into account to derive an analytical expression of pressure and compliance. As a future work, it will be necessary to evaluate the performance of the method for blood samples collected from clinical patients. Additionally, for clinical applications, it will be necessary to improve the present method substantially in available facilities with regard to the microscope and syringe pump used.
## 4 Results and Discussion
### Analysis of the Potential of the CERES-Wheat Model with POD4 DVar  
The objective of this study was to estimate winter wheat yield by assimilating measured or remotely sensed information into the CERES-Wheat model and to examine the suitability of the CERES-Wheat model with POD4 DVar for operational estimates of crop yield at regional scales. In our assimilation strategy, the CERES-Wheat model, as a dynamic agroecosystem model, can be used to simulate temporal changes according to the climate-soil-crop-management system that diagnoses and forecasts crop growth status during various developmental stages. Moreover, remotely sensed data can provide actual information on regional crop growth status in real time. This information is critical for optimizing simulation processes in crop models. Most importantly, the POD4 DVar-based strategy simplifies the data assimilation process and retains the main advantages of the traditional 4 DVAR method [54]. Consequently, significant improvements in regional yield estimation were observed in our experiments when the POD4 DVar-based strategy, rather than the EnKF-based strategy, was used to assimilate remotely sensed LAI maps into the CERES-Wheat model. Contrary to the EnKF-based sequential assimilation strategy, the POD4 DVar-based strategy assimilated all observational data at multiple times in an assimilation window such that less computing time was required. In our work, the  
Figure 4: The 30 m by 30 m winter wheat yield map in Hengshui based on the assimilation of remotely sensed LAI maps into the CERES-Wheat crop model using the POD4 DVar strategy.  
assimilation efficiency of the POD4 DVar-based strategy was at least 7 times faster than that of the EnKF-based strategy in an assimilation window. The significant improvements in yield estimation suggest that the POD4 DVar-based CERES-Wheat model data assimilation technique overcomes the shortcomings of the other approaches and can be applied to accurately and efficiently estimate regional crop yields.  
In addition to the yield, other critical information on regional crop growth may also be estimated, such as the biomass (Figure 6a), harvest index (Figure 6b), evapotranspiration (Figure 6c), and soil organic carbon (Figure 6d). These findings collectively provide strong support for monitoring crop growth, guiding agricultural activities, and evaluating agricultural contributions to climate change. Hence, some of the observed data will be collected for future analysis to determine the accuracy of these additional estimates. These results suggest that the CERES-Wheat model with POD4 DVar-based data assimilation is feasible and has a potential application in operational systems for monitoring regional crops in the near future.
## 5 Network Costs
### Unforeseen Costs
#### 5.4.2 Network Outage Battery Loss  
The gateway, powered by the general electric grid power in the BFNR Nature Center, is susceptible to building power outages. Such instances are typical during storms when lightning strikes or high winds cause breakages to electrical supply wires. During gateway outages, the base station is offline from the wireless network. Without the base station, there is no root node, thereby causing the path cost to increase throughout the network and looping packets will continuously appear. Moreover, route updates continue to be transmitted amongst the nodes in the network. In the CTP-based application, as routes are continuously lost and updated due to the absence of a root node, the routing update interval will be frequently reset to its minimum value (approximately 120 ms).  
The shortened route update interval during gateway outages (in the case of the CTP-based application) causes the duty-cycle of the mote's radio to increase significantly, resulting in a dramatic waste of nodes' battery energy, especially for longer outages. An example of this situation is shown in Figure 18 where four battery discharge curves are overlaid for one of the relay nodes in Site 2 (_i.e._, node 2150, see Figure 3). Three of the four discharge curves overlap representing consistent battery usage. The fourth line (shown in red) represents the battery discharge curve during a period when a gateway outage  
Figure 18: Four battery discharge curves for node 2150 (Site 2) representing normal and truncated battery life. The red line depicts the rapid depletion of battery power during a gateway outage (indicated between the two vertical dashed lines).  
occurred (indicated by the sharp decline between the two vertical dashed lines). In this case, the battery voltage had decreased to a near-depleted level by the time the gateway and the network were restored (approximately six days).  
This problem compounds the issue of gateway outages where node battery life is unnecessarily depleted while no data packets are being collected. It is important to recognize this problem for network maintenance following a gateway outage. Short-interval power outages can be mitigated by means of uninterrupted power supply (UPS) connected to the gateway.