esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Coastal wetlands provide the unique biogeochemical functions of storing a large fraction of the terrestrial carbon (C) pool and being among the most productive ecosystems in the world. However, coastal wetlands face numerous natural and anthropogenic disturbances that threaten their ecological integrity and C storage potential. To monitor the C balance of a coastal forested wetland, we established an eddy covariance flux tower in a natural undrained bottomland hardwood forest in eastern North Carolina, USA. We examined the long-term trends (2009-2019) in gross primary productivity (GPP), ecosystem respiration (RE), and the net ecosystem C exchange (NEE) seasonally and inter-annually. We analyzed the response of C fluxes and balance to climatic and hydrologic forcings and examined the possible effects of rising sea levels on the inland groundwater dynamics. Our results show that in 2009, a higher annual GPP (1922 g C m\({}^{-2}\) yr\({}^{-1}\)) was observed than annual RE (1554 g C m\({}^{-2}\) yr\({}^{-1}\)), resulting in a net C sink (NEE = \(-\)368 g C m\({}^{-2}\) yr\({}^{-1}\)). However, the annual C balance switched to a net C source in 2010 and onwards, varying from 87 g C m\({}^{-2}\) yr\({}^{-1}\) to 759 g C m\({}^{-2}\) yr\({}^{-1}\). The multiple effects of air temperature (Tair), net radiation (Rn), groundwater table (GWT) depth, and precipitation (\(p\)) explained 66%, 71%, and 29% of the variation in GPP, RE, and NEE, respectively (\(p<\) 0.0001). The lowering of GWT (\(-\)0.01 cm to \(-\)14.26 cm) enhanced GPP and RE by 35% and 28%, respectively. We also observed a significant positive correlation between mean sea level and GWT (\(R^{2}\) = 0.11), but not between GWT and \(p\) (\(R^{2}\) = 0.02). Cumulative fluxes from 2009 to 2019 showed continuing C losses owing to a higher rate of increase of RE than GPP. This study contributes to carbon balance accounting to improve ecosystem models, relating C dynamics to temporal trends in under-represented coastal forested wetlands.  
Keywords:gross primary productivity; ecosystem respiration; net ecosystem exchange; ghost forest 4.0.1
## 4 Methods
### Deep Learning Methods
#### 4.2.5 Gate Recurrent Unit (GRU)  
The gate recurrent unit (GRU) is a kind of RNN and a variant of _LSTM_, which reduces the number of gates in the gating unit to 2, eliminates the second-order nonlinear function of the output, combines the forgetting gate and the input gate into an \"update gate\", fuses the cell state and the hidden state, and makes the structure and parameters more concise, so it is easier to compute the structure and parameters while maintaining a similar effect as _LSTM_. Figure 9 shows an example cell of GRU, where \(h_{t-1}\) represents the output of the previous cell, \(x_{t}\) represents the input of the current unit, \(\tilde{h}_{t}\) represents the information to be updated to the hidden state of the current cell, \(h_{t}\) represents the output of the current cell, and \(\sigma\) represents the sigmoid layer (capable of mapping data in the 0-1 range and acting as a gating signal).  
In the GRU model cell, first, we combined the \(h_{t-1}\) and \(x_{t}\) sigmoid layers to obtain the reset gate \(r_{t}\) and update gate of \(z_{t}\), where the reset gate combined signals \(r_{t}\) and \(h_{t-1}\) and multiplied them together to attain the \"reset\" data \(h^{\prime}_{t-1}\), which was then combined with the input \(x_{t}\). Then, the \"reset\" data were obtained through the \(tanh\) layer containing the current input data \(h^{\prime}\). The update gate performed both forgetting and remembering steps according to Equation (11) and used \(z_{t}\) for \(h_{t-1}\) selective forgetting and \(h^{\prime}\) selective remembering; finally, the output of this unit is \(h_{t}\)[45].  
\[h_{t}=(1-z_{t})\odot h_{t-1}+z_{t}\odot h^{\prime} \tag{11}\]  
We can abstract the GRU model as a function with two inputs and one output, as shown in Equation (12), where the current unit receives the input \(x_{t}\) and combines the hidden state of the previous unit containing the information of the previous unit, \(h_{t-1}\), to obtain the output of the current unit, \(h_{t}\).  
\[h_{t}=GRU(x_{t},\ h_{t-1}) \tag{12}\]  
In our experiments, we built a GRU model containing one layer of GRU with three fully connected layers. Among them, the hidden layer matrix dimension in the GRU layer was set to 64, the activation function between layers was set to LeakyReLU, the inputs were the spliced (3 \(\times\) 18) journal metric time series data, and only the output of the last time series was returned. The neurons in the fully connected layer were 32, 16, and 1, and the 64-dimensional output of the GRU layer was processed into a single output value layer by layer in order to complete the one-step prediction of the 3 timesteps for the journal metrics time series data. We chose the MSE metric as the main loss function, and the ratio of the training set to the validation set was 9:1. Finally, we finished the model training, validation, and experiments on the test set and obtained the evaluation metric results, which proved that the GRU model can be applied to the dynamic journal evaluation task in this study.
# Low-Profile Dual-Band Reflector Antenna for High-Frequency Applications
###### Abstract  
A high-gain low-profile reflector antenna with dual-band radiation ability is presented in this paper. The antenna achieves a relative 2 dB gain bandwidth of 10% around \(f_{i}\), and a relative 2 dB gain bandwidth of 20%, around \(f_{h}\), where \(f_{i}\) and \(f_{h}\) are the center operating frequencies of the frequency bands of 29.4-32.4 GHz and 142-174 GHz, respectively. To achieve the dual-band radiation ability, a composite dual-band feed with an \(f_{h}/f_{l}\) ratio of around 5 is proposed as the feed for the reflector antenna, which includes a higher-band circular waveguide and a lower-band coaxial horn. The metallic elliptical surface serves as the subreflector (SR) in the higher band, while the SR is the planar reflectarray in the lower band. Due to the design of the dual reflector, the dual-band reflector antenna features a low focal-to-diameter (F/D) ratio of approximately 0.2. According to the simulated results, the proposed reflector antenna achieves efficiencies of 59.0% and 42.9% at \(f_{l}\) and \(f_{h}\), respectively. For verification purposes, a Ku/E-band scaled prototype is manufactured. The measured VSWRs, radiation patterns, and gains are in reasonable agreement with the simulated ones, proving the correctness of the proposed design method.  
Keywords:dual band; reflector antenna; low profile; reflectarray; high gain; low cost +
Footnote †: journal: Sensors
# Nondestructive Surface Crack Detection of Laser-Repaired Components by Laser Scanning Thermography
## 5 Discussion
### Influence Analysis of Surface Roughness of Repair Layer
As shown in Figure 20c,d, the fluctuation of cladding track is large, and there is an obvious cladding track noise in the edge detection result, which has a significant influence on the crack detection. Figure 20: Thermographic detection of different surface structures: (**a**) local 3D morphology of repair area 2; (**b**) combined form of temperature change field after mask treatment in repair area 2; (**c**) Prewitt edge detection results for temperature change field in the repair area 2; (**d**) local 3D morphology of repair area 3; (**e**) combined form of temperature change field after mask treatment in repair area 3; (**f**) Prewitt edge detection for temperature change field in the repair area 3. In Figure 19b,e or Figure 20b,e, the vertical line \(L_{1}\) at the position of the 800 th pixel on the horizontal axis is selected, and \(L_{1}\) is perpendicular to the cladding track. The temperature distribution curves along \(L_{1}\) are drawn in the combined temperature change field, as shown in Figure 21. Due to the same laser scanning parameters and thermal image processing process, the temperature distribution curves of several surface structures are similar. The distribution of temperature change under the influence of cladding track structure is periodic, and the periodicity of repaired areas 2 and 3 is more significant, while the temperature fluctuation of the smooth area is small.
## 3 Methodology and Data
### Results of Qualitative Interviews
#### 3.2.5 Additional Findings  
Through the qualitative interviews, this study found some noteworthy issues. The first issue is user habit. As a provider, respondents P1, P2, and P3 reported that most of the broadband mobile applications must be operated and connected to information through personal mobile devices. One of the most pervasive challenges is to alter the habits of the users in the client's company. Respondent P2 said even if the client company's staff had learned the benefits of broadband mobile applications and showed a very positive attitude, once they knew that some of their personal habits had to be changed, they began to hesitate immediately. User habits could be considered a kind of compatibility; however, most of the above situation is based on private usage on the personal mobile devices rather than compatibility issues with the company's internal systems or operating processes. As respondent A2 said, some staff of the branch stores are not accustomed to the broadband mobile application adopted by company. They still used their own personal communication tools, and this is the main problem in the full adoption and implementation of the new application.  
Another issue that has been mentioned is data security. In addition to the preservation and confidentiality of information, many interviewed SMEs (A1-3, I1-3) do not want any other company (including service providers) to be able to access or use their data. Some respondents worried that important information could be leaked by the internal users' personal mobile devices. However, those who had this concern also said that after the instructions by the service provider, the current data transmission and storage options were found to be acceptable for the company. Even so, for business clients with concerns about data security, some respondents' companies have also provided other alternative solutions, such as communicating via email or fax. (A3, I1, I3)  
The results of qualitative interviews are summarized in Table 1.
## 3 Results and Discussion
### Results of Measurements of Radiation and Physical Parameters
Among specific mineralogical associations the saponite and minerals of smectite group (including montmorillonite and beidellite) are observed [37; 40; 41; 42; 43]. The contacts of kimberlites and host rocks have local tectonic elements: zones of mylonites, steep fractures and low-amplitude thrusts [21; 40; 43; 44; 45; 46; 47]. The abundance of fracture zones in the near-pipe space of kimberlite bodies is associated with the process of diatreme formation, which influenced the tectonic structure of the adjacent sediment, resulting in the formation of a system of fractures of various types [48]. The occurrence of zones of fracturing and faults is associated with the formation of diatremes as a result of the significant mechanical effect of penetrating gases and melts on the host rocks [49; 50]. As a result, a system of radial and concentric zones of fracturing and faults with crushing and the vertical movement of blocks of enclosing rocks has arisen in the near-pipe space [51]. The impact of kimberlites on the Vendian rocks, which led to the formation of fault zones in the near-pipe space, increased the fracturing and various mineralogical and geochemical changes, influencing the radiation parameters of the host rock. Enrichment with uranium (radium) and the increased fracturing of the near-pipe space created conditions for the production and advection of radon through the host rock mass. As a result, in the soil horizons above the kimberlite bodies of the Lomonosov diamond deposit, an increased volumetric activity of radon can be observed, several times higher than the background values [20; 52]. In the course of this study, we found that the main source of radon observed in the soil air above kimberlite pipes is the enclosing Vendian rocks of the near-pipe space. To understand the relationship between the studied radiation and physical parameters, a statistical analysis of the data was performed.
## 3 Problem Formulation
### Mathematical Modeling
#### 3.1.2 Explanation of the Constraints  
QoS constraints:  
Constraint (IP 1) confines that the end-to-end delay should be no larger than the maximum allowable end-to-end QoS requirement.  
Path constraints:  
Constraint (IP 2) confines that all the traffic required by each O-D pair is transmitted over exactly one candidate path.  
Constraint (IP 3) confines that once path \(p\) is selected and link \(l\) is on the path, \(y_{wl}\) must be equal to 1.  
Capacity constraints:  
Constraint (IP 4) confines the boundaries of aggregate flow on link \(l\).  
Flow constraints:  
Constraint (IP 5) confines the aggregate flow on link \(l\) should not exceed the link capacity.  
Integer constraints:  
Constraint (IP 6) and (IP 7) are the integer constraints of decision variables.
# Vector Signals and Invariant Systems: Re-Tooling Linear Systems Theory
## 4 Time Domain Analysis for LRITI Systems  
This section presents the relevant results from [24], where we introduced LRITI systems and defined a VV convolution. Traditionally, the CV convolution definition in (7) involves the product of two CV signals \(\mathbf{x}(t)\in\mathbb{C}\ \forall\ t\) and \(\mathbf{h}(t)\in\mathbb{C}\ \forall\ t\), yielding a CV output signal \(\mathbf{y}(t)\in\mathbb{C}\ \forall\ t\). Direct extension of (7b) would imply the product of two VV signals \(\mathbf{x}(t)\in\mathbb{L}^{n}\ \forall\ t\) and \(\mathbf{h}(t)\in\mathbb{L}^{n}\ \forall\ t\). However, this immediately contradicts the definition of a VV system, which has both a VV input and VV output, because the product of two VVsignals is not a VV signal \(\mathbf{x}(t)\mathbf{h}(t)\
otin\mathbb{L}^{n}\ \forall\ t\). This leads to the reinterpretation of the CV product in CV convolution, allowing for a VV convolution in terms of a scale-rotation instead of a product.  
In classical linear time-invariant (LTI) system theory, a single Dirac delta function--yielding a single impulse response--is typically sufficient to fully characterize the system's behavior. However, in the context of vector-valued systems or those formulated within hypercomplex algebras, this assumption may no longer hold [17]. Preliminary insights suggest that at least two or more orthogonally directed, vector-valued impulse inputs are required to probe the system across independent orientations.  
This limitation highlights the necessity of probing the system along multiple, independent input orientations to obtain a complete and informative response. This observation raises a fundamental and largely unexplored research question: What is the minimal and sufficient set of impulse responses required to uniquely characterize a vector-valued or hypercomplex-valued system? We give an answer in the context of LRTTI systems that a single impulse response is sufficient to characterize a system. Additionally, a summary of the operators is provided in Table 2.
## 7 Theoretical and Practical Implications  
The research made a theoretical contribution by integrating context-specific factors into the updated Information System Success Model utilized in this study. Specifically, it introduced self-directed learning as a determinant for evaluating student satisfaction with online learning, a factor that was statistically significant. These results expand the understanding of what influences student satisfaction in online learning, aiding researchers in refining their methodologies and constructing a more accurate model.  
Regarding practical implications, decision-makers and instructors could develop online learning strategies focusing on enhancing self-directed learning skills, for example, by providing online tutorials and self-assessment tools. Online tutorials give students the ability to manage their study time as they can access these tutorials based on their availability. Self-assessment tools enable students to monitor their progress and identify areas needing improvement. By implementing these strategies, universities can adopt a more student-centered approach to online learning. This encourages students to take control of their learning process, ultimately enhancing their satisfaction and improving the quality of online learning outcomes.
## 6 Financial and Operational Insights  
From a financial point of view, using the model results in cost savings, optimized resource utilization and a shorter payback period. By efficiently utilizing automated guided vehicles (AGVs) and reducing equipment idle time, the model helps reduce the operating costs and lower the unit production costs, which in turn improves profitability. In addition, it optimizes resource allocation by ensuring efficient use of AGVs and equipment, reducing equipment wear and tear and lowering maintenance costs. Increased productivity and shorter production cycles also help accelerate return on investment.  
On the operational side, the model improves productivity, supply chain responsiveness and system stability. Through an integrated scheduling of the production and transportation processes, cycle time and idle time are reduced, resulting in increased productivity. This also makes the supply chain more responsive and flexible, helping companies quickly adapt to market changes and customer demand. In addition, by alleviating AGV congestion and equipment idling, the model improves system stability and reliability and reduces uncertainty and risk in the production process.  
In short, through integrated production-transportation scheduling, the model brings significant advantages to the enterprise in both financial and operational aspects. It helps reduce costs, improve efficiency, optimize resource utilization and enhance supply chain flexibility and stability.
## 5 Neural Control Strategies
### System Identification  
The system identification was done by using Nonlinear AutoRegressive eXogenous (NARX) neural networks of type [12]:  
\[y_{nn}(n+1)=f[u(n),u(n-1),\ldots,u(n-i),y(n),y(n-1),\ldots,y(n-i)], \tag{30}\]  
where \(u(n)\) is the input control signal and \(y(n)\) the output system response.  
Several two-layer feed-forward networks with hyperbolic tangent hidden neurons and one linear output neuron were trained. Figure 13 shows one example of a neural network using 10 past values of input and output signals and 15 neurons on the first layer, denoted as 10d15n. We use the Levenberg-Marquardt Backpropagation algorithm to minimize the Mean Squared Error (MSE) between the output of the network and system response [20]. Table 6 shows the MSE obtained for each network after training.  
After some trials, the neural network that provided the best MSE is composed of 10 neurons on the first layer and using 10 past values of the input and output signals (10d10n). Table 7 lists the MSE of the best performing networks for the step, sinusoidal, and sawtooth reference signals. The Total column represents the sum of the other three columns, where we confirm that the 10d10n network has the best general performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **MSE** & **Network** & **MSE** \\ \hline
3d5n & \(8.8604\times 10^{-4}\) & 10d5n & \(8.0709\times 10^{-4}\) \\ \hline
3d10n & \(8.7738\times 10^{-4}\) & 10d10n & \(7.8236\times 10^{-4}\) \\ \hline
3d15n & \(8.6531\times 10^{-4}\) & 10d15n & \(7.7818\times 10^{-4}\) \\ \hline
5d5n & \(8.7772\times 10^{-4}\) & 15d5n & \(7.5261\times 10^{-4}\) \\ \hline
5d10n & \(8.3748\times 10^{-4}\) & 15d10n & \(7.3700\times 10^{-4}\) \\ \hline
5d15n & \(8.1346\times 10^{-4}\) & 15d15n & \(7.0542\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean Squared Error (MSE) after training.  
Figure 13: Identification network.  
In Figure 14, we illustrate the responses of the system with the 10d10n identification network when controlled with the lead-lag compensator, for the step, sinusoidal, and saw-tooth reference signals. A time slot of only 5 s is used for the step response to provide a clear view of the transient behavior of the system; the other input references required more time to see multiple periods of the signals. We verify that the network follows the system response very well without having learned the noise and perturbations of the system.  
Note that this identification network will be used as a neural model of the levitation system in the simulation of the neural control systems reported in next subsections.
# Cloud Based Smart City Services for Industrial Internet of Things in Software-Defined Networking
## 5 System Analysis and Performance Evaluation
### Performance Evaluation  
In this section authors have framed three topologies of network with various QoS metrics to calculate the flow of traffic of load and compared with the LBBSRT [26], Random [27], Round Robin [27], Heuristic [28] algorithms. The whole simulations are done in Mininet emulator with OpenFlow switches that imitates the real networking scenarios and have captured the packets from the Wireshark for packets to analyze and calculates the throughput, response time and CPU Utilization (TCP packets captured from Wireshark) of the different topologies. Authors found that proposed algorithms have better efficient results than the existing algorithms on the basis of various QoS metrics namely data transmission, response time and CPU utilization.  
1. **Comparison of Data Transmission:** The data transmission is the number of incoming requests received on the traffic at different time intervals. In Figure 6 the transmission of data is achieved highest in the proposed algorithm as compared to the existing  
\begin{table}
\begin{tabular}{c c} \hline \hline
**MAC Address** & **Port** \\ \hline
00:00:00:00:00:01 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Source MAC to port 1 table.  
algorithms, with the load of 100 Mbps the data transmission is 200 Mbps; and with 900 Mbps of load the data transmission is 800 Mbps and so on. As the load of traffic is increasing the data transmission is rising accordingly. More load on clusters gives rise to more data transmission. Proposed algorithm has shown the 65%, 70%, 70%, 72% improvement over LBSRRT [26], Random [27], Round Robin [27], Heuristic [28], algorithms respectively in data transmission.
2. **Comparison of Average Response Time:** The response time signifies the minimum number of incoming requests received on the clusters. Figure 7 gives the low response time for the proposed algorithm which has been able to provide the unique flows for the IoT nodes. As per the result, with 100 Mbps of load the response time is 0 ms; 300 Mbps of load the response time is 10 ms and so on. Proposed algorithm has shown the 90%, 290%, 257%, 161% improvement over LBSRRT [26], Random [27], Round Robin [27], Heuristic [28] algorithms respectively in response time.
3. **Comparison of CPU Utilization:** The balancing of load on the different servers i.e., server1, server2, server3 by various algorithms on different load on the clusters. As seen, the CPU utilization of existing algorithms in Figure 8 is higher in all the servers as compared to the proposed algorithm and the load imbalance occurs between the IoT nodes if the resources are properly utilized. Proposed algorithm has shown the 64%, 79%, 72%, 63% improvement over LBSRRT [26], Random [27], Round Robin [27], Heuristic [28] algorithms respectively in CPU utilization.  
Figure 6: Data Transmission Rate.  
Figure 7: Response Time.