esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### V-Model and RFLP Approach  
Systems Engineering consists of a technical process that follows two main stages, (i) decomposition and (ii) integration. The designing process starts with a not well-specified set of objectives and a few concepts, often very complex. Such a system is broken down into smaller parts even further, until they are simple enough to be engineered. On the second stage, when all the components are designed and built, they are integrated until obtaining a system that satisfies the original purpose.  
A methodology based on the above-mentioned principles, suitable for the design of mechatronic systems, is known as V-model [34] due to its graphical representation (Figure 6).  
The descending branch of the V-model consists of a top-down process for system design. The input are customer's needs translated into system requirements while the output is a cross-domain solution concept of the system. Requirements lead the system specification through a decomposition process into subsystems. The horizontal part contains three specific-domain design processes: (i) mechanical, (ii) electrical, and (iii) software. The ascending branch presents a bottom-up approach where the subsystems are developed and integrated into the whole system. Finally, the verification is guaranteed by the comparison of the ascendant path on the right (integration) and the corresponding elements on the left (requirements).  
Pointing the attention to the descending branch of the V-model, it can be divided into four main steps named Requirement, Functional, Logical and Physical. The RFLP approach summarizes the purpose of the SE. The requirements definition puts together the needs of the stakeholders, project managers, and engineering team. The functional step defines the functional architecture of the system accordingly with system requirements. The logical architecture of the system is presented in the logical step where all the connections among the domains and the parts are made explicit. Finally, in the physical step the components are selected to ensure the correct functionality of the system. Modeling and model analysis come to the aid of all the steps of the presented approaches. In the current paper Matlab environment is used to model and simulate the system. In particular, \"Matlab-Simulink\" and its related add-ons for the MBSE (like \"Simulink Requirements\" and \"System Composer\") are used as a modeling tool. A particular mention could be given to \"Simscape\"  
Figure 6: V-model and RFLP approach.  
which is a Simulink extension that offers the possibility to model multi-domain systems, such as mechatronic systems [35].
## 2 Data and Methods
### Sensitivity Analysis Methods
#### 2.3.3 FAST Analysis Method  
The Fourier amplitude sensitivity test (FAST) is a method introduced in the 1970s for analyzing the parameter sensitivity of models [24; 25; 26]. Its theory is derived from the analysis of variance and Fourier transformation. The core of this method is to use the appropriate search curve to search in the multidimensional space of parameters to transform the multidimensional integral into the one-dimensional integral. Then, the model becomes a periodic function with independent parameters. Fourier analysis is performed on the output of the model to generate the Fourier amplitude for each frequency. The magnitude of the Fourier amplitude is used to indicate the sensitivity of each parameter. The larger the amplitude is, the more sensitive the model is to this parameter and vice versa. This amplitude is also known as the importance measure [56; 57].  
For the hydrological model \(y=f(X)=f(x_{1},x_{2},\cdots,x_{M})\), \(M\) is the number of model parameters. Depending on the appropriate search function, model \(y=f(X)\) can be converted into \(y=f(s)\). According to the characteristics of the sine function, for any positive integer frequency, the \(s\) value at \([-\pi,\ \ \pi]\) or \([0,\ 2\pi]\) can show complete periodicity, so that the model output can be expanded into the form of the Fourier series.  
\[f(s+2\pi)=f(s) \tag{11}\]  
\[f(s)=\sum_{i=1}^{M}A_{i}sin(\omega_{i}s) \tag{12}\]  
\[A_{i}=\int_{-\pi}^{\pi}f(s)sin(\omega_{i}s)ds \tag{13}\]  
where \(A_{i}\) represents the amplitude of the \(i\)th parameter, \(\omega_{i}\) is the frequency, \(s\) is sampled at equal intervals within the interval \([-\pi,\ \ \pi]\), and each parameter obtained by sampling is input into the model. \(A_{i}\) can be obtained with the approximate calculation of the following formula.  
\[A_{i}=\frac{a_{i}}{n}\sum_{j=1}^{n}f(s_{j})sin\big{(}\omega_{i}s_{j}\big{)} \tag{14}\]  
where \(n\) is the number of samples, and \(a_{i}\) is an integer. We want to ensure \(\sum_{i=1}^{M}a_{i}\omega_{i}\
eq 0\). In this paper, the larger the \(A_{i}\) of a parameter in the SWAT model is, the greater the sensitivity of this parameter.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 6 Lessons Learned, Open Issues, and Research Challenges
### Lessons Learned
#### 6.1.4 Reward Function Design  
Because the reward function reinforces the algorithm convergence, designing a good reward function is very important to improve the routing performance. The QMR [22]and QTAR [23] jointly consider the link delay and residual energy level of UAVs in their multi-objective reward function and achieve significant performance improvement for PDR, end-to-end delay, and balance in energy consumption. However, designing the reward function considering path stability, delay, and UAV residual energy may provide better routing performance in FANETs.
## 3 Results
### Meat Mineral Content Analysis  
In addition to lipid profiling, analyses of the mineral content of all rabbit meat samples obtained from this study were also carried out (see Table 3).  
Figure 4: Comparison of rabbit meat caloricity across diet groups (CG, G1, and G2) with SD.  
Figure 3: The nutrient improvements across the study groups (CG, G1, and G2) with SD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Minerals** & \multirow{2}{*}{**Mean CG \(\pm\) SD**} & \multirow{2}{*}{**Mean G1 \(\pm\) SD**} & \multirow{2}{*}{**Mean G2 \(\pm\) SD**} & \multirow{2}{*}{_p-Value_} \\ (ppm) & & & & \\ \hline Ca & 16,014.67 \(\pm\) 103.05 & 19,108.48 \(\pm\) 697.49 & 22,132.50 \(\pm\) 506.13 & 5.43 \(\times\) 10\({}^{-31}\) \\ Mg & 17,079.34 \(\pm\) 925.78 & 20,008.90 \(\pm\) 597.34 & 23,017.69 \(\pm\) 609.17 & 4.60 \(\times\) 10\({}^{-24}\) \\ P & 215,271.67 \(\pm\) 739.18 & 259,416.36 \(\pm\) 8036.55 & 287,322.01 \(\pm\) 3785.62 & 1.48 \(\times\) 10\({}^{-33}\) \\ K & 349,148.33 \(\pm\) 1252.28 & 377,426.27 \(\pm\) 4024.00 & 382,655.16 \(\pm\) 6573.08 & 1.05 \(\times\) 10\({}^{-23}\) \\ Fe & 3081.33 \(\pm\) 74.76 & 2783.54 \(\pm\) 94.38 & 2817.11 \(\pm\) 78.61 & 8.46 \(\times\) 10\({}^{-13}\) \\ Zn & 2914.67 \(\pm\) 82.78 & 2804.24 \(\pm\) 88.35 & 2838.95 \(\pm\) 133.58 & 0.0181 \\ Cu & 172.67 \(\pm\) 12.10 & 181.10 \(\pm\) 5.00 & 182.72 \(\pm\) 6.40 & 0.0044 \\ Mn & 29.67 \(\pm\) 0.80 & 28.33 \(\pm\) 1.29 & 28.74 \(\pm\) 1.55 & 0.0172 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average mineral content of rabbit meat.  
Peak Ca, Mg, P, and K levels were observed in G2, with 22,132.50 (Ca), 23,017.69 (Mg), 28,7322.01 (P), and 38,2655.16 (K) ppm, respectively, probably owing to the increased content of macro- and micro-elements in linseed meal, and calcium fructoborate (Figure 3).  
Furthermore, when comparing the mineral content of CG with that of G1, dietary supplementation (for G1) increased mean levels of Ca, Mg, P and K, recording 19,108.48 (Ca), 20,008.90 (Mg), 259,416.36 (P), and 377,426.27 (K) ppm, respectively, due to the higher mineral content present in fodder yeast (values noted on the product label).  
Statistically significant differences (\(p<0.05\)) were also found between the values of each mineral element investigated across all investigated groups. In addition, a positive correlation was also observed between the CF% content of the diets and overall total mineral values within rabbit meat samples, highlighting that fibers ensure enhanced digestion and absorption of minerals (Tables 1 and 3).
###### Abstract  
The polarization fields and electro-optical response of PIN-diodes based on nearly lattice-matched InGaN/GaN and InAlN/GaN double heterostructure quantum wells grown on (0001) sapphire substrates by metalorganic vapor phase epitaxy were experimentally quantified. Dependent on the indium content and the applied voltage, an intense near ultra-violet emission was observed from GaN (with fundamental energy gap \(E_{S}\) = 3.4 eV) in the electroluminescence (EL) spectra of the InGaN/GaN and InAlN/GaN PIN-diodes. In addition, in the electroreflectance (ER) spectra of the GaN barrier structure of InAlN/GaN diodes, the three valence-split bands, \(\Gamma_{9}\), \(\Gamma_{7+}\), and \(\Gamma_{7-}\), could selectively be excited by varying the applied AC voltage, which opens new possibilities for the fine adjustment of UV emission components in deep well/shallow barrier DHS. The internal polarization field \(E_{pol}\) = 5.4 \(\pm\) 1.6 MV/cm extracted from the ER spectra of the In\({}_{0.21}\)Al\({}_{0.79}\)N/GaN DHS is in excellent agreement with the literature value of capacitance-voltage measurements (CVM) \(E_{pol}\) = 5.1 \(\pm\) 0.8 MV/cm. The strength and direction of the polarization field \(E_{pol}\) = \(-\)2.3 \(\pm\) 0.3 MV/cm of the (0001) In\({}_{0.055}\)Ga\({}_{0.945}\)N/GaN DHS determined, under flat-barrier conditions, from the Franz-Keldysh oscillations (FKOs) of the electro-optically modulated field are also in agreement with the CVM results \(E_{pol}\) = \(-\)1.2 \(\pm\) 0.4 MV/cm. The (absolute) field strength is accordingly significantly higher than the \(E_{pol}\) strength quantified in published literature by FKOs on a semipolar \(\left(11\overline{2}2\right)\) oriented In\({}_{0.12}\)Ga\({}_{0.88}\)N quantum well.  
group-III nitrides; polarization fields; PIN-Diodes; electroreflectance; electroluminescence
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 3 Test Methods and New Approach  
This section outlines a new approach based on Kriging and introduces the test methods, applied to evaluate the accuracy of resampling. For simplicity, the sampling rate was set to \(f_{S}=1\) Hz, which is equivalent to \(T_{S}=1\) s. For jitter test signals, the average sampling rate was set to \(T_{AV}=1\) s. The simulation results can easily be scaled to other sampling rates or converted to the OSR.
## 6 Results and Discussion
### Model Validation
#### 6.2.3 Task Efficiency  
The item that experts considered would make the study's model superior in terms of task efficiency compared with the current scheduling methods received a score of 4.48. Experts stated that using the developed model could reduce the time spent on document preparation for scheduling. They also believed that generating detailed initial schedules at the planning stage using the developed model could minimize onsite scheduling modifications and consequently reduce workload.  
The results of the expert survey confirmed that the model developed in this study can effectively address the issues present in the planning stage process charts currently utilized in the construction industry. Notably, according to the expert survey results, the primary advantage of the developed model was its ability to enhance task efficiency, which is particularly important for construction site operations where a thorough understanding of the detailed processes is essential. Therefore, it can be inferred that the characteristics of the model in this study contributed to its effectiveness in devising detailed process plans from the planning stage onward for successful construction site operations.
# Photon Enhanced Interaction and Entanglement in Semiconductor Position-Based Qubits
###### Abstract  
CMOS technologies facilitate the possibility of implementing quantum logic in silicon. In this work, we discuss a minimalistic modelling of entangled photon communication in semiconductor qubits. We demonstrate that electrostatic actuation is sufficient to construct and control desired potential energy profiles along a Si quantum dot (QD) structure allowing the formation of position-based qubits. We further discuss a basic mathematical formalism to define the position-based qubits and their evolution under the presence of external driving fields. Then, based on Jaynes-Cummings-Hubbard formalism, we expand the model to include the description of the position-based qubits involving four energy states coupled with a cavity. We proceed with showing an anti-correlation between the various quantum states. Moreover, we simulate an example of a quantum trajectory as a result of transitions between the quantum states and we plot the emitted/absorbed photos in the system with time. Lastly, we examine the system of two coupled position-based qubits via a waveguide. We demonstrate a mechanism to achieve a dynamic interchange of information between these qubits over larger distances, exploiting both an electrostatic actuation/control of qubits and their photon communication. We define the entanglement entropy between two qubits and we find that their quantum states are in principle entangled.  
entanglement; charge qubit; position-based semiconductor qubits; cryogenic technologies; semiconductor photon communication; Jaynes-Cummings-Hubbard formalism Article
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 6 Conclusions  
In this paper, we propose a lightweight Remote-Sensing Image Super-Resolution (RSISR) network named Dual Attention Fusion Enhancement Network (DAFEN), designed for precise RSISR with limited time and spatial overhead. The model offers two versions: a 416 K lightweight DAFEN and a 188 K ultra-lightweight DAFEN-S, accommodating different task requirements. Specifically, we design an extremely lightweight lattice structure, Channel-Spatial Lattice Block (CSLB), as the feature extraction module, composed of Group Residual Shuffle Block (GRSB) and Channel-Spatial Attention Interaction Module (CSAIM). GRSB utilizes a strategy of group convolution combined with channel shuffle as the nonlinear extraction module of CSLB, effectively reducing redundant convolution calculations. CSAIM performs a weighted cross-combination of CSLB's two branches in both spatial and channel dimensions, facilitating information flow between the branches. Furthermore, we develop the Forward Fusion Enhancement Module (FFEM), which uses a forward fusion structure to retain more high-level feature information, efficiently acquiring more contextual features and enhancing fused features through Self-Calibrated Group Convolution (SCGC) and Contrast-aware Channel Attention (CCA). FFEM incrementally fuses and enhances multi-level features using both local and global strategies, ultimately forming a comprehensive feature fusion representation. Finally, experimental results on two remote-sensing and four benchmark datasets demonstrate that our network achieves a better balance between performance and model complexity.  
Conceptualization, W.C.; methodology, W.C. and Y.L.; software, W.C.; validation, W.C.; formal analysis, W.C.; investigation, W.C.; resources, W.C. and S.Q.; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, W.C., S.Q., and L.L.; visualization, W.C.; supervision, S.Q. and L.L.; project administration, S.Q.; funding acquisition, S.Q. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the National Natural Science Foundation of China (Grant No. 12201185) and the Henan Science and Technology Development Plan Project (Grant No. 242102210064).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Fabric Defect Detection Method Using SA-Pix2 pix Network and Transfer Learning
## 3 Results  
### Impact of Self-Attention Mechanism on Defect Detection Accuracy  
In order to assess the impact of the self-attention mechanism and loss functions on defect detection accuracy, separate experimental studies were conducted to investigate the effects of the self-attention mechanism and loss functions on defect detection accuracy.  
The convolutional neural network's local receptive field within GAN networks has limitations, restricting its focus to local image features, thereby diminishing image reconstruction accuracy. As depicted in Figure 7a, the defect appears as a relatively large black stain. In the absence of the self-attention mechanism, the reconstructed image (Figure 7b) retains small black areas, causing missed defect regions. Therefore, the self-attention mechanism was introduced into the generator network. To reduce the network's complexity and training time, the self-attention mechanism was added only to the input and intermediate layers of the generator's upsampling and downsampling layers, as depicted in Figure 1. This enables the network to capture global features of fabric patterns and local features of the defect's surrounding area. The introduction of the self-attention mechanism empowers the generator with the ability to extract global features, effectively addressing the issue of inadequate image reconstruction accuracy. The reconstructed image with the self-attention mechanism, as shown in Figure 7d, exhibits significant improvement in reconstruction accuracy compared to the version without the self-attention mechanism. Furthermore, it enables the complete detection of defect areas, as demonstrated in Figure 7e.
## 6 Performance Evaluation
### Average Revocation Delay  
Figure 3 shows the average revocation delay with a different number of users. The delay model of the revocation event is as shown in Formula (16):  
\[T_{delay}=T_{voting}+T_{publish}+T_{resolution} \tag{16}\]  
where \(T_{delay}\) is the average revocation delay of each voting process of the algorithm, \(T_{voting}\) is the time of vote accumulation stage, \(T_{publish}\) is the expiration time for publishing the revocation result, and \(T_{resolution}\) is the average latency when publishers generated revocation information and submitted it to the local SNR node.  
From Figure 3, it can be seen that with the increase in users in the domain, the total revocation delay gradually decreases, because with the increase in the number of users, the density of active users becomes larger, so the total voting delay decreases.
## Results
### Relationships Among NDVI, NDRE, Leaf Water Content, and Leaf Water Potential  
NDVI showed a statistically strong positive correlation with leaf water content. NDVI values from Day 1 and Day 2 correlated well with percent water content (\(r_{s}\) = 0.94 for both days), and the average of all flights and leaf water content was associated with a slightly weaker yet statistically significant correlation of \(r_{s}\) = 0.89 (Figure 3a). A weak correlation was found with NDRE and leaf water content (\(r_{s}\) for Day 1, Day 2, and overall were 0.77, 0.60, and 0.71, respectively) (Figure 3b), therefore we present the rest of the results with NDVI. A simple linear regression was calculated to predict mid-day water potential based on water content. A significant regression equation was found with an R\({}^{2}\) of 0.546 (Figure 3c). Leaf water potential decreased by 0.0516 (MPa) for each percent of water content. The regression was much stronger when Tx4 from Day 2 was removed as an outlier, resulting in an R\({}^{2}\) of 0.921 (Figure S1).  
Figure 3: (a) Relationship ((R = spearman’s rho statistic (\(r_{s}\))) between NDVI and water content at: 1 pm on Day 1; 1 pm on Day 2; and all values; (b) Relationship (R = spearman’s rho statistic (\(r_{s}\))) between NDRE and Water Content at: 1 pm on Day 1; 1 pm on Day 2; and all values; and (c) relationship (R-squared from linear regression) between plant water content and mid-day water potential (Note: the data point at \(-\)5 MPa and 52% water content is an anomaly and likely due to the timing of the treatment (immediately following cutting)).  
The distribution of NDVI and NDRE for all resampled flights (n = 12, 8.7 cm) by treatment and in the overall study area is shown in Figure 4 and Figure S2 and Figure S3. Statistically significant differences were found in the NDVI values between Tx2 and Tx3, between Tx3 and Tx4, and between Tx4 and the control. Nonsignificant differences were found between Tx1 and Tx2, as well as the control and the water treatment (see Figure S2 for NDRE).
## 5 Policy Control
### Fraud  
Earth observation is used in environmental policy control in agriculture [48]. While the EU, as explained in Section 4.4, subsidizes farmers, its member states are obliged to check and report compliance to regulations under the EU Common Agricultural Policy (CAP). The EU supports individual member states in implementing information systems that combine remote sensing and land parcel based information for the purpose of cross checking whether received subsidies comply with EU regulations. [PERSON] [49] gives a detailed description of the use of remote sensing in crop subsidy control in Denmark. While initially developed to detect subsidy fraud, the CAP system has recently been expanded to also include compliance with environmental directives. Remote sensing is also used to detect fraud related to the Federal Crop Insurance Program set up by the US Department of Agriculture, which supports farmers in overcoming drought risks [50].
## 4 Discussion
### Fish and Plant Growth Performance
For example, the optimal growth of the halophyte _Salicornia rubra_ occurs at 200 mM NaCl [60] and decreases with increasing salinity (>200 mM). Previous studies indicate that the optimum salinity level for rock samphire's growth performance is 50 mM NaCl [61], a value that is similar to the 8 ppt salinity used in the present study. Studies on the salt-stress adaptation mechanisms of plants examining the protein expression patterns of _Salicornia europaea_ revealed that energy production and ion homeostasis-associated proteins play essential roles in the plant's salt tolerance ability [34].