esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
For example, for the Cyclone II FPGA device, the utilization of logic elements was reduced to 1030 LEs, and the power consumption was reduced to about 99 mW, but the operating frequency was still high (around 140 MHz) and the throughput was around 100 Mbps. Therefore, the proposed architecture can be used for many potential applications ranging from video surveillance to IoT. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref. **} & **Generic** & **Logic** & **Clock** & **Max. Freq. ** & **Power** & **Logic** & \multirow{2}{*}{**Platform**} \\  & **Composition** & **Utilization** & **Cycles** & **(MHz)** & & **(mW)** & **Rate (\%)** & \\ \hline
[10] & PRESENT and & & & & & & \\  & SPONGENT & & & & & & \\ \hline
[21] & LED and & & 415 Slices & 44 & 587 & 469 & 8.6 & Xilinx Vertix-5 \\ \hline
[21] & LED and & & & & & & \\  & PHOTON & & & & & & \\ \hline
[33] & AES-OTR & 11 k ALMs & - & - & - & 50 & Altera Stratix 4 \\ \hline
[40] & LED and MonkeyDuplex & 303 Slices & 72 & 640 & - & - & Xilinx Virtex-6 \\ \hline  
hline
[FOOTNOTE:]Footnote : footnotemark: [ENDFOOTNOTE] & LD and & & & & & \\  & PHOTON & & & & & \\ \hline
## 2 Problem Formulation and Mathematical Model for PV Parameters Extraction
### SD Model  
The solar cell exposed to sunlight is represented in the equivalent circuit design of a SD as a diode, a photocurrent source, and two parasitic resistors. Temperature and solar light produce the photocurrent (\(I_{ph}\)). An obstruction to current flow is represented by a lumped-series resistance (\(R_{ss}\)), which is made up of the electrode resistance, contact resistance, and material bulk resistance. The leakage current across the p-n junction is measured by the shunt resistance (\(R_{sl}\)). In a real solar cell, the diode (\(D\)) has a modified diode ideality factor to account for the combined influence of conduction processes. A quality metric used to determine how closely a practical solar cell resembles an ideal solar cell is the ideality factor, whose value is dependent on semiconductor material and manufacturing process. The equivalent circuit diagram of a SD model is illustrated in Figure 1. Using [PERSON]'s current law (KCL), the solar cell's current can be stated as follows [42]:  
\[I=I_{ph}-I_{S1}\bigg{[}\exp\bigg{(}\frac{IR_{ss}+V_{t}}{\eta_{1}\,V_{th}}\bigg{)} -1\bigg{]}-\bigg{(}\frac{IR_{ss}-V_{t}}{R_{sl}}\bigg{)} \tag{1}\]  
where \(V_{t}\) is the terminal voltage, and \(\eta_{1}\) and \(I_{S1}\) stand for the ideality factor and reverse saturated current associated with the diode (D1). The thermal voltage of the PV cell is represented by \(V_{th}\), which could be computationally characterized as indicated by Equation (2):  
\[V_{th}=\frac{K_{BZ}T}{q_{c}} \tag{2}\]  
where \(T\) and \(k\) stand for the cell temperature in Kelvin and Boltzmann's constant (1.3806503 \(\times\) 10\({}^{-23}\) J/K). The [PERSON] diode equation uses \(q_{c}\) (1.60217646 \(\times\) 10\({}^{-19}\)) to represent the electron's charge magnitude.  
From the measured I-V data of solar cells, five unknown parameters (\(X\) = [\(I_{ph}\), \(R_{ss}\), \(n\), \(I_{S1}\), \(R_{sl}\)]) in Equation (1) must be determined. The precise choice of these factors has a significant impact on a solar cell's performance. To illustrate, the SD model can be used to determine the maximum conversion efficiency if the parameters are chosen accurately.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
###### Abstract  
Image fusion is a pivotal image-processing technology designed to merge multiple images from various sensors or imaging modalities into a single composite image. This process enhances and extracts the information contained across the images, resulting in a final image that is more informative and of superior quality. This paper introduces a novel method for infrared and visible image fusion, utilizing nested connections and frequency-domain decomposition techniques to effectively solve the problem of lost image detail features. By incorporating depthwise separable convolution technology, the method reduces the computational complexity and model size, thereby increasing computational efficiency. A multi-scale residual fusion network, R2 FN (Res2 Net Fusion Network), has been designed to replace traditional manually designed fusion strategies, enabling the network to better preserve detail information in the image while improving the quality of the fused image. Moreover, a new loss function is proposed, which is aimed at enhancing important feature information while preserving more significant features. Experimental results on public datasets indicate that the method not only retains the detail information of visible-light images but also highlights the significant features of infrared images while maintaining a minimal number of parameters.  
image fusion; infrared image; multi-scale features; lightweight model; attention mode
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 3 Proposed SS-FGSM Scheme
### Online Deployment  
The online deployment or the deep learning test stage can be performed after the offline training using the optimized \(\theta\). Therefore, the received data is pre-processed and put in the DNN to estimate the transmitted bits in real-time. In other words, the proposed DL-based detector does not need extra training for updating the parameter \(\theta\). Most importantly, our detector achieves better results with less complexity and without extra training for the DL model.
## 5 Discussion
### Limitations and Suggestions for Future Research  
Due to the limitations of time and budget as well as the COVID-19 pandemic, the researchers could not return to the country on which this research was focused. For the convenience of collecting the sample, the data were collected via an online questionnaire, so the sample might not have been enough to represent an effective result. The generalizability of the analytical results may be limited. However, the participants' responses were reliable with respect to their service perceptions because most reported traveling by bus 4-6 days per week. Thus, we suggest that further research could consider increasing the collected sample to enhance the study's results. Future studies are required to investigate the differences in the service demands and satisfaction levels of different age groups and passenger categories to provide additional practical recommendations for service improvement.  
This study referred to and revised the quality attributes of the Bangkok bus service previously identified in other studies. The 12 quality attributes related to bus service were identified in four dimensions proposed by [PERSON] [62]: interaction with passengers, tangible service equipment, convenience of service, and operating management support. Further research related to public transportation can consider the service attributes in this study and modify them to suit the respondents better. In addition, researchers could study and compare bus users' satisfaction with other transportation systems to use that as a guideline for improving the organization's service model to be more diverse. This study aimed to combine an IPA, [PERSON]'s model, and an FMEA to evaluate public bus service quality risks in Bangkok. The number of questions might have been excessive since the researcher had to ask the same questions for each model, including those regarding importance and satisfaction in the IPA and functional and dysfunctional questions in [PERSON]'s model. This could lead respondents to abandon the survey.  
Lastly, this study focused only on bus service in Bangkok. The method proposed by this study can be also applied in other cities. Not only the questionnaire but also the quality risk model proposed in this study can be used for other cities' bus services. Further research can consider comparing with other countries to make suggestions and recommendations to the Bangkok Mass Transit Authority for improvements and service developments to be more efficient and enhance the results of this study. Conducting similar studies in other major cities can facilitate the comparison of results and identification of various factors that could help in converting car users to bus users. Such studies can provide policymakers with a deeper understanding of how to improve the mass transportation systems in their respective countries.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments
### Assessment of Indicators
and NIQE [59] as follows: \[\text{PI}=\frac{1}{2}((10-\text{Ma})+\text{NIQE})\] (16)
4. LPIPS is a full reference metric that measures perceptual image similarity using a pre-trained deep network. We use the AlexNet [60] model to compute the l\({}_{2}\) distance in feature space. LPIPS can be computed using a given image y and the real image y\({}_{0}\) as follows: \[\text{LPIPS}(y,y_{0})=\sum\limits_{l}\frac{1}{H_{l}W_{l}}\sum\limits_{h,w}w_{ l}\odot\left(f_{h,w}^{l}-f_{b,w}^{l}\right)_{2}^{2}\] (17)  
where H\({}_{1}\) and W\({}_{1}\) denote the height and width of layer lth, respectively, \(f_{h,w}^{l}\) and \(f_{b,w}^{l}\) denote the features corresponding to y and y\({}_{0}\) of layer lth at position (h, w), respectively, w\({}_{1}\) is the learned weight vector, and \(\odot\) is the elementwise multiplication operation. 5. SAM is mainly used to measure the consistency between the reconstructed image and the original image in terms of spectral information, which calculates the angle between the spectral vectors of the reconstructed image and the corresponding pixels of the real image; the smaller the angle is, the more similar the spectral distributions of the two are, and the better the spectral information is retained. The formula is as follows: \[\text{SAM}_{i}=\text{cos}^{-1}\left(\frac{\sum\limits_{j=1}^{M}(x_{ij}\cdot y _{ij})}{\sqrt{\sum\limits_{j=1}^{M}(x_{ij}^{2})}\cdot\sqrt{\sum\limits_{j=1}^ {M}(y_{ij}^{2})}}\right)\] (18) \[\text{SAM}_{\text{avg}}=\frac{1}{N}\sum\limits_{i=1}^{N}\text{SAM}_{i}\] (19) where M is the dimension (i.e., number of bands) of each spectral vector, \(x_{ij}\) and \(y_{ij}\) denote the value of the jth band at the ith pixel for image 1 and image 2, respectively, and N is the total number of pixels in the image. 6. RMSE calculates the pixel error between the reconstructed image and the original high-resolution image; the smaller the value, the closer the reconstructed image is to the real image and the smaller the error. The formula is as follows: \[\text{RMSE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(y_{i}-y_{i})^{2}}\] (20) where N is the total number of data points, y\({}_{i}\) is the actual observed value, and \(\hat{y}_{i}\) is the model predicted value.
# Monitoring Safron Crops with UAVs
## 6 Conclusions  
To sum up, in this study, we present the results of the pilot application of UAVs for monitoring safron cultivations. We used a model training process for pixel and object recognition and estimation in safron cultivations. UAV photogrammetry techniques were initially employed to collect and process images taken from the fields. We performed 62 flights over 6 fields, producing a total of 1863 photos. Afterward, two different image-processing methods, pixel- and object-based analyses, were implemented to create datasets that were used as inputs for two different machine learning algorithms, RF and MLP. Both methods presented satisfying estimation accuracy, with the pixel-based method outperforming the object-based method. In more detail, the pixel-based method resulted in 100% accuracy in the RF algorithm and 95% in MLP, in contrast to object-based method, which had 80% and 76%, respectively. As for the learning algorithms, RF presented better accuracy (100%-80%) than MLP (95%-76%) in both cases. The results are highly dependent on the initial processing of the images. More specifically, we advise practitioners to annotate the images in more detail, perform flights in lower heights, and obtain images periodically to increase the efficacy of the estimation models. Additionally, we advise researchers to further work on object-based analysis methods in an attempt to increase the accuracy of the methods when applied for the identification of the smallest objects within a field. Finally,the study's findings show that the utilization of such methods can lead to the recognition of object categories with a high success rate.  
Conceptualization, K.K.; Formal analysis, D.C.T.; Methodology, D.C.T. and A.T.; Software, K.K. and F.D.; Writing--original draft, K.K., F.D., A.T. and S.B.; Writing--review & editing, P.S. and P.A. All authors have read and agreed to the published version of the manuscript.  
This research was co-funded by the European Union and Greek National Funds through the Operational Program Competitiveness, Entrepreneurship, and Innovation, grant number T1 EDK-04873, project \"Drone innovation in Safron Agriculture\", DIAS.  
Not applicable.  
**Data Availability Statement:** users.uowm.gr/sbibi/download/ (accessed on 3 May 2022).  
The authors declare no conflict of interest.
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 4 Experimental Results and Analysis
### Dark-Center Target Detection  
From Section 3.1, it can be seen that the images after the defogging operation have more prominent insulator contours, richer information, and higher recognizability as well as contrast. Therefore, the defogging algorithm is jointly trained with the target detection algorithm, which we called Dark-Center, to achieve insulator detection in foggy environments. The comparison experiments were carried out on the CPILD dataset, and the experimental results are shown in Table 7.  
For the CenterNet target detection algorithm, the accuracy of detecting insulators is only 77.77%, but the improved Center model has a substantial improvement for the recognition of insulator defects, with an increase of 16.56% in mAP value and up to 94.33%. The addition of the defogging algorithm in the preprocessing module further improves the mAP value to 96.76%. Moreover, the Dark-Center model can also improve the detection accuracy of both insulators and defects, with an increase in Insulator AP from 95.89% to 98.85% and that of Defect AP from 59.64% to 94.67%.  
To visualize the differences between the original CenterNet algorithm, the proposed Center model, and the Dark-Center model with the defogging process, a detection image is selected for comparison and analysis, and the detection results are shown in Figure 11.  
As seen in Figure 11, the CenterNet detection algorithm only detects the overall insulator orientation at 0.78 and fails to accurately identify the defective part (Figure 11b), while the improved Center algorithm not only detects the insulator with an accuracy of 0.96 but also accurately determines the defects at 0.38 (Figure 11c). For the Dark-Center model with the additional defogging algorithm, its defogging effect is obvious, the insulator identification accuracy is up to 0.96, and the defect identification is 0.48 (Figure 11d).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Network** & **Params [MB]** & **Speed [FPS]** & **mAP [\%]** \\ \hline YoLov5 & CSPDarknet53 & 46.73 & 26.72 & 80.49 \\ YoLov7 & — & 37.30 & 28.95 & 83.24 \\ Faster R-CNN & ResNet50 & 28.47 & 10.50 & 79.31 \\ SSD & VGG16 & 26.15 & 46.89 & 78.59 \\ CenterNet & ResNet50 & 32.67 & 31.40 & 79.64 \\ Center & ResNet50 & 35.75 & 18.88 & 83.42 \\ \hline \hline \end{tabular} Note: — indicates no specific name for the YoLov7 backbone feature extraction network.  
\end{table}
Table 6: Comparison of target detection results of different algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Insulator AP [\%]** & **Defect AP [\%]** & **mAP [\%]** \\ \hline CenterNet & 95.89 & 59.64 & 77.77 \\ Center & 98.45 & 90.21 & 94.33 \\ Dark-Center & 98.85 & 94.67 & 96.76 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Foggy day environment experiment comparison.
## 2 Materials and Methods
### PBD-YOLO Algorithm Architecture
#### 2.4.1 ShareSepHead Detection Head  
As depicted in Figure 6, the ShareSepHead architecture includes three components: task decoupling, cross-layer parameter sharing, and dynamic distribution prediction. The regression branch extracts spatially sensitive features through the lightweight yet faster REG module (composed of vanilla convolution and Depthwise Separable Convolution [28]), combined with convolutional layers sharing weights and \(1\times 1\) convolutions to achieve lightweight modeling of bounding boxes. The classification branch adopts a three-tier structure consisting of the CBS module, convolutional layers with shared weights, and \(1\times 1\) convolutions, leveraging stacked vanilla convolutions to enhance the semantic abstraction capabilities. In order to enhance the compatibility of cross-scale feature representation, independent parameters are retained at lower levels to avoid inter-task gradient conflicts, while features at higher levels are forced to map to the same hidden space at different levels through weight sharing. Additionally, the detection head adopts the bounding  
Figure 5: PBD-YOLO algorithm architecture.  
box loss function, distribution focal loss (DFL) [29], from the YOLO series networks. Its core idea is to model the position of the bounding box as a probability distribution rather than a deterministic value, thereby enhancing the model's ability to predict ambiguous or uncertain boundaries. The equation of DFL is listed as follows:  
\[DFL(S_{i},S_{i+1})=-((y_{i+1}-y)\text{log}(S_{i})+(y-y_{i})\text{log}(S_{i+1})), \tag{1}\]  
where \(y\) represents the true position of the target bounding box, \(y_{i},y_{i+1}\) denote the adjacent integer positions after discretization, while \(S_{i}\) and \(S_{i+1}\) are the normalized probability values predicted by the model for the positions \(y_{i}\) and \(y_{i+1}\), respectively.
## 5 Discussion  
In this study, Euclidean distance terrain entropy demonstrates significantly better performance than the other four commonly used terrain entropy methods across five special terrain types. These differences are primarily reflected in the ability to capture spatial information. Many scholars have emphasized the importance of spatial information measurement [22; 23; 24]. However, the three elevation-based terrain entropy methods fail to effectively capture the changes in the spatial disorder of grid DEMs, while Euclidean distance terrain entropy demonstrates a strong ability to capture spatial disorder and exhibits significantly better measurement capability than slope-based terrain entropy, highlighting its potential in analyzing highly complex terrains. This phenomenon has been validated in the simulated DEM experiments. For real DEMs, there is currently no effective method to accurately assess the complexity of different terrains. Therefore, although Euclidean distance terrain entropy shows better measurement capability in simulated terrains, its superiority over slope terrain entropy in real DEMs cannot be directly concluded. In practical applications, it may be necessary to integrate multiple terrain entropy methods for comprehensive evaluation [11], which is consistent with previous research findings [25; 26].  
Next, we will briefly discuss the computational efficiency of Euclidean distance terrain entropy. Since there are no significant issues with the computational efficiency of the existing terrain entropy methods, a comparison will not be made. The computational efficiency is influenced by CPU performance and algorithm implementation differences, so the results presented here are for reference only. On a desktop computer equipped with an AMD Ryzen 5 5600 CPU and 32 GB RAM, Euclidean distance terrain entropy was computed for grid DEMs of different window sizes, with the results shown in Table 5. It can be observed that as the window size increases, the computation time for Euclidean distance terrain entropy increases significantly. Therefore, computational efficiency becomes a major limiting factor for the application of Euclidean distance terrain entropy in large-window grid DEMs.
# Integrated Information in Relational Quantum Dynamics (RQD)
## Appendix A Computational Scalability and Practical Estimators for \(\Phi\)  
### Complexity Classification  
Computing the integrated information measure \(\Phi(\rho)\) entails evaluating the quantum Jensen-Shannon divergence for _every_ bipartition \((A|B)\) of the \(n\) subsystems. In brute force, one must compute  
\[\Phi(\rho)\ =\ \min_{A|B}\ D_{QJS}\Big{(}\rho\left\|\rho_{A}\otimes\rho_{B} \right),\]  
where each term \(D_{QJS}(\rho|\rho_{A}\otimes\rho_{B})\) quantifies how much \(\rho\) departs from a factorized split across some cut \((A|B)\). The number of possible bipartitions grows combinatorially: in the worst case (balanced cuts), there are \(\binom{n}{\lfloor n/2\rfloor}\) partitions. Stirling's approximation gives \(\binom{n}{\lfloor n/2\rfloor}\sim 2^{n}/\sqrt{\pi n/2}\), on the order of \(2^{n}/\sqrt{n}\). Consequently, a brute-force search incurs time and memory scaling:  
\[O\Big{(}\binom{n}{\lfloor n/2\rfloor}\Big{)}\ \sim\ O(2^{n}/\sqrt{n})\, \tag{1}\]  
which is exponential in \(n\). In practical terms, the exact evaluation of \(\Phi\) becomes intractable beyond small \(n\) due to this explosive growth in partitions.  
Beyond raw enumeration, one can show that even deciding whether \(\Phi(\rho)\) is below a given threshold is NP-hard. Specifically, the decision problem \"Given state \(\rho\) and threshold \(\phi_{0}\), is \(\Phi(\rho)\leq\phi_{0}\)?\" reduces (in the worst case) to the balanced bisection graph-partitioning problem, a known NP-hard problem. Thus, unless P = NP, no polynomial-time algorithmcan exactly compute \(\Phi\) for arbitrary large \(n\). This complexity classification underscores the need for smarter algorithms and approximations for anything but the smallest system sizes.
# Effect of Human Hair Fibers on the Performance of Concrete Incorporating High Dosage of Silica Fume
## 3 Results and Discussion  
### Compressive Strength  
#### 3.1.1 Human Hair Fiber Concrete Compressive Strength  
The compressive strength test is performed on all specimens using an electrically driven automated compression testing machine with a 1000 kN capacity, as described in the methodology section, after 7, 14, and 28 days of curing. The test results were compared with a control mix design without human hair fiber, as presented in Table 4. The test results of control specimens are depicted in the following figures for compressive, flexural, and split tensile strength test results, which help us to study the positive influence of human hair fibers. When human hairs are included in the various percentages shown in Table 4, the primary criterion determines their concrete structural applicability. The results of all the test specimens' compressive strength tests are shown in Figure 1. The strength of the concrete increases as the curing period progresses. From C1-HHFC-1 to C5-HHFC-2.5, strength gain is noted at each curing phase. This improved compressive strength could be due to the crack-bridging effect of HHF. Similarly, a dramatic drop in compressive strength is found at C6-HHFC-3 dosages of 3% to 5%. Except for C4-HHFC-2, all of the mixes meet the strength requirements of M20-grade concrete. The decrease in compressive strength after a threshold of 2% HHF is due to the amalgamation of human hair fibers, which causes a decrease in the bond strength of the concrete matrix [15; 28]. Compared to C1-HHFC-0, C5-HHFC-2% achieves the maximum compressive strengths of 13.72, 16.45, and 20.04 MPa at 7, 14, and 28 days of curing, respectively. According to these test results, the optimum use of human hair in concrete is 2% to acquire higher strength without altering the strength parameters of structural applications of concrete. Previous studies also confirmed the positive influence of hair fiber in concrete [18; 31].  
Figure 1: Compressive strength of concrete human hair fiber.