esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of the Metal Substrate  
When adding an Au substrate with a size of \(60\times 120\) um\({}^{2}\)(nearly infinite) under the Au nanotip, the electric field distribution near the nanotip with a substrate is shown in Figure 3a. The relation between \(E\) and the apex-substrate distance \(D\) is presented in Figure 3b. It can be seen that \(E\) rapidly decreases from 715 to 125 V/m when \(D\) increases from 4 to 100 nm. At \(D>100\) nm, \(E\) decreases slowly and eventually approximates the case without the substrate. The maximum electric field of the nanotip-substrate model is about 28 times larger than that of the model with only the nanotip. At \(x=-2\) nm, the electric field distribution along the \(y\)-axis is shown in Figure 3c. Compared with the case without the substrate, the electric field enhancement has been greatly improved. With the presence of a substrate, the FWHM of electric field along the \(y\)-axis improves from 18 nm to 12 nm, which  
Figure 2: **(a)** Electric field distribution on the Au nanotip with a radius of 10 nm; **(b)** The maximum electric field amplitude \(E\) as a function of the radius \(r\) of the tip; **(c)** The electric field distribution along the \(x\)-axis and the red line is a reciprocal square law fitting; **(d)** The electric field distribution along the \(y\)-axis and the full width at half maximum (FWHM) is marked with a dash line.  
indicates an obviously strong electric field coupling and a better local field enhancement effect. What we can conclude is that the metal substrate will enhance the electric field amplitude and the enhanced electric field decreases with the increase of the apex-substrate distance. The electric filed amplitude of the Au nanotip-Au substrate depends on the incident angle of the THz waves. In our simulations, the maximum electric field amplitude between the substrate and nanotip first increases with the incident angle increasing from 10\({}^{\circ}\) to 45\({}^{\circ}\), and then decreases for the incident angle varying from 45\({}^{\circ}\) to 80\({}^{\circ}\) (see Figure 3d). This result is similar to that observed in TERS study that the total field is the interference of the reflect field and the incident field when a substrate exists [17].
## 6 Results
### Network Transmission  
To simulate and test the performance of the proposed scheme in the network, we used 26 nodes to construct the network, as shown in Figure 11a. These included 16 routing nodes, 2 service nodes, and 8 user nodes. The bandwidth of the routing node was 40 Mbps, and the bandwidth of both the service node and the user node was 100 Mbps. _Servera_ and _Serverd_ sent data to eight user nodes. We conducted multiple repeated experiments on the total network throughput of the four schemes under different link loss rates.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transmission** & **NewReno** & **Cubic** & **Bbr** & **Nodectl** \\ \hline A & 12.81 Mbps & 15.25 Mbps & 0.01 Mbps & 11.38 Mbps \\ B & 13.80 Mbps & 9.64 Mbps & 1.77 Mbps & 11.38 Mbps \\ C & 7.65 Mbps & 9.39 Mbps & 29.5 Mbps & 11.38 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average transmission throughput.  
Figure 10: Shared bottleneck.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmission** & **MPTCP-Coupled** & **Nodectl** \\ \hline \(A\) & 1.89 Mbps & 2.79 Mbps \\ \(D\) & 3.74 Mbps & 2.79 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average flow throughput.  
Figure 11b shows the average result of the total network throughput. When there was no link packet loss, the throughput of the BBR scheme was the lowest, while the throughputs of the other three schemes were similar. Like in the previous experiments, the BBR scheme overestimated the BDP in the connections initiated later, resulting in a queue backlog or congestion at the network nodes. The previously connected transmission RTT would also increase sharply, resulting in the original transmission rapidly reducing the transmission rate within several cycles and taking some time to recover to a reasonable rate. As the packet loss rate increased, the throughput of Cubic and NewReno significantly decreased. The throughput performance of the BBR scheme was unstable, but overall, it was superior to Cubic and NewReno. The Nodectl scheme performed best, consistently maintaining the highest throughput, with only minor throughput decreases and fluctuations as the packet loss rates increased.  
Table 9 shows the throughput ratio at a packet loss rate of 0.5% to the throughput without packet loss for four schemes. The Nodectl scheme had the smallest performance loss, while the NewReno and Cubic schemes had severe performance losses.
## 3 Experimental Results and Analysis
### Energy Distribution with Scattering Angle of BAD
Figure 13: Average energy distribution of BAD in different scattering angle ranges. When the \(L/D\) was large (\(L/D\) = 3.86) or small (\(L/D\) = 1.66), the average energy of the target fragment and EFP fragments decreased exponentially with the increase in scattering angle from 0\({}^{\circ}\) to 25\({}^{\circ}\). Conversely, the average energy of the EFP fragments did not decrease but basically remained at a certain level within the range of 5-20\({}^{\circ}\) when the \(L/D\) was moderate (\(L/D\) = 2.4). Versus the other two types of EFPs (\(L/D\) = 3.86 and 1.66), the energy of the fragments was more uniform over different scattering angle ranges, which is consistent with the results in Figure 12b. This suggests that it is not the case that a higher \(L/D\) is better; rather, there is an appropriate \(L/D\) that can lead to a uniform spatial distribution of fragment energy. The average fragment energy of the EFP with a large \(L/D\) (\(L/D\) = 3.86) was small within a scattering angle range of 0-5\({}^{\circ}\) because of the large number of fragments in the range (Figure 12c). Additionally, the scattering of these fragments was greatly affected by the residual EFP and the fragments basically followed the residual EFP in the small scattering angle range. The diameter of the residual EFP with a large diameter (small \(L/D\)) was large (Figure 3), thus the perforation on the recover plate was large, allowing more small fragments to pass through. Therefore, the perforation information of such fragments is missing, and it is difficult to accurately calculate the number of perforations in the small scattering angle range in the experiment.
## 3 Bimatrix Stackelberg Game  
### Game Model  
In the scenario described, the UAV initially selects its transmission center frequency, taking into account the jammer's potential actions. Subsequently, the jammer responds based on the UAV's chosen center frequency. This interaction aligns well with the [PERSON] game framework, prompting us to model the UAV and jammer's interactions using this game-theoretical approach. Specifically, since the UAV makes the initial decision, it acts as the leader in the game, while the jammer, responding to the UAV's actions, plays the role of the follower. Given that our scenario involves a single UAV and one jammer, they collectively represent the participants in the game. The game is formally defined as  
\[\mathcal{G}=\{\mathcal{U},\mathcal{J},\mathcal{F}^{c}_{U},\mathcal{F}^{c}_{J},\mathcal{U}_{U},\mathcal{U}_{I}\}, \tag{6}\]  
where \(\mathcal{F}^{c}_{U}\) and \(\mathcal{F}^{c}_{J}\) represent the action sets of the UAV and the jammer, respectively. The UAV's frequency selection is denoted by \(f^{c}_{U}\in\{f^{c}_{U1},f^{c}_{U2},\ldots,f^{c}_{UIM}\}\), while the jammer'sfrequency selection action is represented by \(f_{J}^{c}\in\{f_{I^{*}I^{*}}^{c},f_{J^{*}}^{c},\dots,f_{JM}^{c}\}\). The utility functions \(\mathcal{U}_{U}\) and \(\mathcal{U}_{I}\) for the UAV and the jammer, respectively, are derived from the signal-to-jamming-plus-noise Ratio (SJNR) received by the receiver. These utility functions can be expressed as follows:  
\[u_{U}(f_{U}^{c},f_{J}^{c})=\frac{g_{U}P_{U}}{\int_{f_{U}^{c}-b/2}^{f_{U}^{c}+b/2 }[N(f)+g_{I}J(f-f_{J}^{c})]\mathrm{d}f}-\lambda_{1}P_{U}, \tag{7}\]  
\[u_{J}(f_{U}^{c},f_{J}^{c})=-\frac{g_{U}P_{U}}{\int_{f_{U}^{c}-b/2}^{f_{U}^{c}+b/ 2}[N(f)+g_{J}J(f-f_{J}^{c})]\mathrm{d}f}-\lambda_{2}P_{J}, \tag{8}\]  
where transmission power of the UAV is denoted by \(P_{U}\) and the jammer's transmission power is represented by \(P_{I}=\int_{f_{J}^{c}}^{f_{J}^{c}+(M-1)b}f(f)\mathrm{d}f\). The variables \(\lambda_{1}\) and \(\lambda_{2}\) signify the transmission costs per unit power for the UAV and the jammer, respectively.
# A Method of Bus Network Optimization Based on Complex Network and Beidou Vehicle Location
## 3 Weight Definition Method of Weighted Bus Network
### Road Impedance Function  
Road impedance is a physical quantity used to indicate the magnitude of resistance encountered by a vehicle on the road, reflecting the fluency of vehicle passing through a road section. Because the vehicle travel time on the road can directly reflect the degree of obstruction in the process of vehicle driving, the vehicle travel time is generally used to describe the road impedance.  
BPR (Bureau of Public Roads) function is usually introduced when calculating the travel time of public transport vehicles. The BPR function is as follows:  
\[T_{z}=T_{z}^{0}\bigg{[}1+a\bigg{(}\frac{x_{z}}{c_{z}}\bigg{)}^{\beta}\bigg{]} \tag{1}\]  
\(T_{z}^{0}\) represents the impedance value of the road section \(z\) when the traffic flow is 0; \(x_{z}\) is the traffic flow of road section \(z\); \(c_{z}\) represents the actual capacity of section \(z\); \(T_{z}\) is the vehicle travel time of the section \(z\); \(\alpha\) and \(\beta\) are correction coefficients, the US Highway Board recommends \(\alpha\) = 0.15, \(\beta\) = 4. Thus, the bus travel time between the two adjacent stations is obtained:  
\[t_{ij}=\sum_{z\in\beta_{ij}}P_{ij}T_{z} \tag{2}\]  
Figure 2: Public transportation network system floor plan. (The Chinese in the figure is the actual place name of Jinan City.).  
\(S_{ij}\) represents all the sections of the road; \(P_{ijz}\) represents the proportion of passing distance to the total distance of section \(z\). The distance of a road section is acquired by Beidou high-precision vehicle positioning equipment in real time.
# The Corrosion Failure Mechanism of a Peak Load Boiler in a District Heating System
## 2 Materials and Methods
### Corrosion Scale and Boiler Water Quality Analysis  
To investigate the formation mechanisms of oxidation scales and their role in tube degradation, both solid-phase and solution-phase analyses were performed. Oxidation scales were collected from two distinct sources: (i) the inner surface of the PLB tubes and (ii) the surface of detachable turbulators removed from the convection section. The oxidation scales from the tube surfaces exhibited visually distinct colors, appearing as either white or red-brown deposits, suggesting differences in their chemical composition and formation conditions. To assess these variations, the white and red-brown scales were analyzed separately for structural and compositional differences. Additionally, oxidation scales from the turbulator surfaces were examined to determine whether their formation differed from those on tube surfaces.  
X-ray diffraction (XRD) analysis using MDI Jade 5.0 software was conducted on both types of oxidation scales to determine their crystalline structures and phase compositions. However, XRD alone was insufficient to fully characterize localized elemental composition, particularly for micro-scale corrosion products. Therefore, energy-dispersive spectroscopy (EDS) was additionally employed to analyze fine-scale elemental distributions. Furthermore, cross-sectional scanning electron microscopy (SEM) was conducted to examine the morphology and internal structure of the oxidation scales, providing further insights into their formation and degradation behavior.  
To further evaluate the chemical reactivity of the turbulator oxidation layer, a solution-phase analysis was performed to assess potential acidic species generated from the oxide layer. At least 1 g of oxidation scale was collected and dissolved in water for ion analysis using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS). This approach aimed to determine whether acidic species formed in solution could contribute to corrosion mechanisms within the PLB system.  
In addition to the turbulator oxidation scale analysis, the chemical composition of the boiler feedwater was assessed over multiple sampling dates to evaluate its potential contribution to scale formation and corrosion. IC and ICP-MS were employed to quantify the concentration of dissolved ions and contaminants in the feedwater.
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 2 Background
### Kasami Code  
When compared with the gold code generator shown in Figure 2, the kasami code generator is identical in that it consists of two LFSRs but is different in that the length of the LFSR is different. In detail, the shift register of the kasami code generator uses an \(N\)-bit shift register and a half-length \(N/2\)-bit shift register to generate the kasami code, which is \(2^{N}-1\) long at maximum. Signals that use the code type of the kasami code include GLONASS LIOCp [18], GLONASS L2 OC [20] and GLONASS L3 OC [21].  
Figure 3 shows the GLONASS L2 OC code generator, which is an example of a kasami code generator. The generator consists of a 14-bit shift register SR1, a 7-bit shift register SR2, Feedback Taps1/2, and Initial ROMs1/2 [20]. The feedback polynomial of SR1 shown in Figure 3 is \(f(x)=x^{14}+x^{13}+x^{8}+x^{4}+1\), and the feedback polynomial of SR2 is \(f(x)=x^{7}+x^{6}+1\). The initial value of SR1 and the initial value of SR2 are stored in Initial ROMs1/2, respectively, and in this case, the initial value of SR2 has different initial values by satellite number. When the predetermined feedback polynomial and initial value are set and operate, the outputs of the 14 th register of shift register SR1 and the 7 th register of shift register SR2 are modulo-2 summed to generate 10,230 chips long L2 OCp code of GLONASS L2 OC.
## 3 Methodology
### Experimental Setup  
The methodology of our work has been divided in two well-differentiated parts as can be seen in Figure 2. The first step is to find the best hyperparameters for each model (Train/Test Step). Once these parameters, and therefore the best model, have been found, the model is trained on a time period different from the previous one (Validation Step), in order to validate it reliably and avoid over-fitting. In both phases, a Multi-Input Multi-Output (MIMO) technique has been used.  
The first stage is to obtain the data to be used, which consists of a univariate time series (only one variable) that will serve as input to the models to be compared. This time series is formed by the electricity price of the period of time to be studied, which can be obtained from the e-sios website.  
Figure 2: Scheme followed in our proposal using the MIMO strategy. To improve the reliability of the results, the dataset is divided in two subsets: The first subset will be used to train and search for the best hyperparameters of the model, the input-output pairs are formed by training and testing sample time points named _tr_ and _ts_. The second subset will be used to validate the model on a new dataset once the best hyperparameters are identified, in order to avoid overfitting. In this case the input-output pairs are formed by _tr_v_ and \(v\).  
As in [12], we have used the Multi-Input Multi-Output (MIMO) technique to train and evaluate the models. The MIMO strategy increases the predictions' accuracy since it does not accumulate previous predictions' errors. This strategy makes use of a sliding window scheme to create the input-output pairs that will be used by the models to be tested. All these output pairs have a fixed prediction window, in this case 24 h, while the training window has a size defined by a factor whose value also has to be optimized [32]. In both phases, we have tried to make the prediction of day \(D+1\) as realistic as possible, i.e., at a set time on day D, the market price values are predicted for each hour of day \(D+1\).  
To test our models, we have divided our originals datasets in two subsets: train dataset and validation dataset. The training dataset is used to train and search the best parameters of the models, and once they are found, we evaluate their accuracy with the validation dataset. The training datasets include all data except the last month of each dataset, which will be found in the validation datasets.  
To evaluate each model, we obtain the model's prediction feeding it with the validation dataset windows. For any window of the validation dataset, the model will predict the next window. Therefore, we can connect all the predicted windows and compare the forecasted time series's accuracy with the original one. The input-output pairs for this phase are \(tr\_v\) and \(v\), respectively.  
However, before we can obtain the predictions of each model, they must have been trained before. To train the models, they must be fed previously with the training subsets (tr and tr\_v, respectively), which follow the same moving window philosophy as validation subsets (ts and v).
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 5 Redundancy
The storage of data shall be based on principles of redundancy, while the critical application control shall be supported by a Centralized Monitoring System with the integration of PRP and PTP. A well-designed redundant system has several requirements in handling failures and maintaining service availability without interruption. These requirements span hardware, software, and operational procedures, as summarized in Table 19.  
The design of redundant systems for critical applications places demands on operating systems for support for redundancy protocols such as Parallel Redundancy Protocol (PRP) [74] and High availability Seamless Redundancy (HSR) [22] within virtual or software defined networks, as shown in Figure 11. It provides these protocols with the abilities of the function of virtual switches acting as Dually Attached Nodes (DAN), while aggregating traffic across applications. Limitations on PRP pairs and the transmission of packets must be overcome in real-time performance. Having a digital twin operating in parallel or on standby is key to the seamless backup and restoration of data, ensuring less than 10 milliseconds of downtime with active-active systems. It requires, for instance, support from the operating system for active-active configurations to allow for unbroken services, even when an upgrade of the equipment is being performed or any maintenance that may be necessary is being undertaken. In the same vein, cooling systems should be duplicated in those installations with high thermal load to avoid failures. It is at this point recommended to analyze the effects of energy storage systems on availability [57]. Such measures ensure a high level of availability and resilience, following the standards and regulations of critical systems for environments including electrical substations and communication networks resilience [75; 76]. Table 20 summarizes some of the major standards and legislation relevant to redundancy.
## 3 Proposed Hybrid Optimization Approach
### Master Stage: DSCA  
The DSCA, a metaheuristic optimization technique, explores and exploits the solution space using trigonometric sine and cosine functions whose amplitude varies over a number of iterations [42]. One of the key features of the DSCA is that it is a population-based optimization technique, so the optimization process starts with random solutions. This set is repeatedly evaluated using an objective function and improved by applying a set of evolution rules, which are shown below.
## 3 Results
### Metallization Ratio  
The influence of the metallization ratio on the resonator is also studied. Compared to the above-mentioned factors such as ZnO normalized thickness and top electrode material and thickness, the metallization ratio \(2w/\lambda\) has a relatively small effect on the velocity, but it significantly impacts the \(K^{2}\) of both modes. For better optimization, quasi-Rayleigh mode and quasi-Sezawa mode were simulated under different conditions. Quasi-Rayleigh mode was simulated when changing the metallization ratio to ZnO normalized thickness \(h/\lambda=0.5\), etching ratio \(d/h=1\), and top Cu electrode normalized thickness \(t_{1}/\lambda=0.07\). As shown in Figure 6a, \(V_{\rm p}\) and \(K^{2}\) of quasi-Rayleigh both exhibit a relatively sharp declining trend at first and then increase slightly. As such, a smaller top electrode width as well as the ZnO pillar width is beneficial to improving the \(K^{2}\) of the quasi-Rayleigh. When the metallization ratio was 0.2, \(K^{2}\) reached 11.56% which accompanied a \(V_{\rm p}\) value of 2102.8 m/s. Quasi-Sezawa mode was simulated when changing metallization ratio to ZnO normalized thickness \(h/\lambda=0.6\), etching ratio \(d/h=0.7\), and top Al electrode normalized thickness \(t_{1}/\lambda=0.11\). Figure 6b depicts that the \(V_{\rm p}\) and \(K^{2}\) of the quasi-Sezawa mode initially increase and then decline monotonically with an increasing metallization ratio in contrast to the quasi-Rayleigh. \(K^{2}\) achieved a maximum value of 8.17% when the metallization ratio was 0.75, simultaneously, the \(V_{\rm p}\) value was 5276.0 m/s.
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 5 Discussion
### Constraints and Limitations
We found that the actual harvest operation dates for summer crops (corn and soybeans) and winter wheat occurred during the remotely sensed senescent period but closer to the dormancy (end of the season) dates rather than the detected termination dates. More studies are needed to accurately detect harvest dates for cash crops.
###### Abstract  
In high-resolution remote sensing images, there are areas with weak textures such as large building roofs, which occupy a large number of pixels in the image. These areas pose a challenge for traditional semantic segmentation networks to obtain ideal results. Common strategies like downsampling, patch cropping, and cascade models often sacrifice fine details or global context, resulting in limited accuracy. To address these issues, a novel semantic segmentation framework has been designed specifically for large-format high-resolution remote sensing images by aggregating global and local features in this paper. The framework consists of two branches: one branch deals with low-resolution downsampled images to capture global features, while the other branch focuses on cropped patches to extract high-resolution local details. Also, this paper introduces a feature aggregation module based on the Transformer structure, which effectively aggregates global and local information. Additionally, to save GPU memory usage, a novel three-step training method has been developed. Extensive experiments on two public datasets demonstrate the effectiveness of the proposed approach, with an IoU of 90.83% on the AIDS dataset and 90.30% on the WBDS dataset, surpassing state-of-the-art methods such as DANet, DeepLab v3+, U-Net, ViT, TransUNet, CMTFNet, and UANet.  
high-resolution remote sensing images; optical large-format images; semantic segmentation; transformer; building extraction