esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Apparatus  
EPD and electrochemical experiments were carried out using an AMEL (Milan, Italy) mod. 466 polarographic analyzer coupled with a Linseis (Linseis GmbH, Selb, Germany) LY 18100 X-Y chart recorder. For pulsed EPD techniques, an EG&G PAR (EG&G Princeton Applied Research, Princeton, NJ, USA) model 263A or an EG&G PAR model 273 potentiostat/galvanostat was used; potentiostat control and data acquisition for both potentiostats/galvanostats were accomplished using a desktop computer running the M270/250 electrochemical research software (EG&G) version 4.23 or version 3.0, respectively. The electrochemical cell used for EPD experiments was a conventional three-electrode system with a Pt rod as a counter electrode and an Ag/AgCl, KCl sat. reference electrode, while the working electrode acting as a substrate for EPD was a platinum disk (polycrystalline 99.95%, 3.0 mm diameter, Goodfellow, Cambridge, England) embedded in a glass body; the working and counter electrodes were assembled so that they faced each other to develop a uniform electrical field around the working electrode. Electrode pretreatment procedure can be found in the relevant section of Supplementary Materials.  
Electrochemical quartz crystal microbalance (EQCM) experiments were performed with a mod. QCA917 SEIKO EG&G system coupled with a mod. 263A EG&G PAR potentiostat/galvanostat, both controlled using a desktop computer running Model 270/250 electrochemical research software version 4.23 (EG&G). AT-cut 9 MHz platinum-plated (with a geometrical area of the electrode surface of 0.2 cm\({}^{2}\)) quartz crystals (EG&G) were used, mounted on a Well-Type all-Teflon electrochemical cell (SEIKO EG&G) with a platinum wire counter electrode and an Ag/AgCl/KCl set. reference electrode. A single quartz crystal was used for each experiment; these crystals were used as received. Electrochemical determination of the electrode area yielded a value of 0.202 \(\pm\) 0.002 cm\({}^{2}\) (\(n\) = 5) obtained by using potassium ferricanide electrochemistry in 1 mM of potassium nitrate (a diffusion coefficient of 7.19 \(\times\) 10\({}^{-6}\) cm\({}^{2}\) s\({}^{-1}\) was used for ferricanide [32]). Silver electrodeposition experiments [33] showed frequency changes that were linear at up to a 20 \(\upmu\)g mass increase, with a sensitivity of 10.7 +/ - 0.3 \(\times\) 10\({}^{8}\) Hz g\({}^{-1}\) (\(n\) = 3), as calculated using Sauerbrey's equation.
## 5 Experiment and Results
The experimental results in Table 3 indicate that the proposed OGM-IRM algorithm significantly reduces the task completion time and human-machine interaction time, enhancing the efficiency of completing high-difficulty tasks and expanding the range of executable tasks for the WMRA. In the task for the book, the algorithm of the El-E robot selects a relatively fixed docking position to the edge of the table, but the placement of the book is on the other end of the table, resulting in the WMRA being too far from the task target to complete the task as shown in Figure 14. Similarly, in the task for the drawer, the algorithm of the El-E robot selects a docking position that is too close to the drawer to complete the task, as shown in Figure 14.  
In conclusion, the OGM-IRM algorithm presented in this paper enhances the capabilities of robotic arms in completing desktop tasks by enabling precise evaluations of target object positions that may have been difficult to reach because of surrounding obstacles. The algorithm also improves the task completion speed, demonstrating the benefits of positional optimization for subsequent robotic arm tasks and enhancing the coordination of the WMRA, as shown in Figure 15. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Joystick** & **Algorithm of EL-E Robot** & **OGM-IRM** \\  & **Total Time (s)** & **Total Time (s)** & **Total Time (s)** & **Interaction Time (s)** \\ \hline Drawer & 78.12 & Fail & 49.15 & \(1.55\) \\ Elevator & 65.54 & 49.21 & 37.87 & \(1.47\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experiment results of the vertical task. Figure 14: The algorithm of the EL-E robot failed to execute the task. (**a**) Task result for the book; (**b**) task result for the drawer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classified Time** & **Joysticks Time (s)** & **Algorithm of EL-E Robot Time (s)** & **OGM-IRM Time (s)** \\ \hline Human operation time & 83.41 & 3.34 & 1.33 \\ Non-human operation time & 83.41 & 61.44 & 48.24 \\ Total time & 83.41 & 64.78 & 49.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The experiment results.
## 4 Experiments
### Parameters and Experimental Setup  
The factor \(d\) that adjusts the partial blocks size was set to two. The factor \(\omega\) that adjusts the location for partial blocks was set to 0.5. The excluded partial blocks decided by \(\tau\) were 15 pixels. The regularization parameter \(\lambda\) was 0.001. We used the scale step \(s=1.02\) and the dimension of scale space \(D=33\). The regularization parameter for adaptive learning rate \(\psi\) was \(1/14\). \(v_{1}\), \(v_{2}\) and \(\gamma\) were set to 10, 0.5 and 1.5. \(\rho_{m}\) was set to 0.03 for the maximum learning rate. \(\hat{\tau}\) was set to zero for the sparse update. In addition, we used histogram of oriented gradients (HOG) [40] as a feature to represent images. We conducted the experiments using MATLAB R2017b with an i7-2600 core 3.40-GHz CPU with 16 GB RAM.  
Figure 4: The adaptive learning rate according to response. (**a**) response map with learning rate value of 0.0055; (**b**) response map with learning rate value of 0.0289.
## 5 Lempel-Ziv Compression on a Distributed System
### LZW Compression on a Distributed System  
As mentioned at the beginning of this section, if we use a RESTART deletion heuristic clearing out the dictionary every \(\ell\) characters of the input string we can trivially parallelize the factorization process with an O\((\ell)\) time, O\((n/\ell)\) processors distributed algorithm. LZW compression with the RESTART deletion heuristic was initially presented in [18] with a dictionary of size \(2^{12}\) and is employed by the Unix command \"compress\" with a dictionary of size \(2^{16}\). Therefore, in order to have a satisfying compression effectiveness the distributed algorithm might work with blocks of length \(\ell\) even greater than 300K on realistic data. After a dictionary is filled up for each block though, the factorization of the remaining suffix of the block can be approximated within the multiplicative factor \((k+1)/k\) in time O\((km)\) with O\((n/km)\) processors on a tree architecture. Every leaf processor stores a sub-block of length \(m(k+2)\) and a copy of the dictionary which are broadcasted from some level of the tree where the first phase of the computation has been executed. Adjacent sub-blocks overlap on \(2m\) characters. We call a _boundary match_ a factor covering positions of two adjacent sub-blocks. We execute the following algorithm:  
* for each block, every processor but the one associated with the last sub-block computes the boundary match between its sub-block and the next one which ends furthest to the right;
* each processor computes the optimal factorization from the beginning of the boundary match on the left boundary of its sub-block to the beginning of the boundary match on the right boundary.  
Stopping the factorization of each sub-block at the beginning of the right boundary match might cause the making of a surplus factor, which determines the multiplicative approximation factor \((k+1)/k\) with respect to any factorization. In fact, as it is shown in Figure 3, the factor in front of the right boundary match (sequence of x's) might be extended to be a boundary match itself (sequence of plus signs) and to cover the first position of the factor after the boundary (dotted line). In [10], it is shown experimentally that for \(k=10\) the compression ratio achieved by such factorization is about the same as the sequential one. Then, compression can be effective on a large scale system even if the size of the file is not large.  
Figure 3: The making of a surplus factor.
## 1 Introduction
To the best of our knowledge, these results are novel and help researchers and practitioners deploy low-power wireless networks that are more robust and make an efficient use of the spectrum in real-world scenarios. The remainder of this article is organized as follows. Section 2 provides an overview on the PHYs defined in the IEEE 802.15.4-2015 standard that are evaluated in this article. Section 3 provides a survey of the research related to the SUN-OFDM physical layer for low-power wireless networks. Section 4 presents the methodology and the setup used to conduct the experiments to evaluate the robustness of the SUN-OFDM and OQPSK-DSSS PHYs. Section 5 presents and summarizes the results obtained from the measurement campaign. Section 6 discusses the results obtained and presents a series of recommendations. Finally, Section 7 concludes this article.
# A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
## 4 Findings and Discussions
### Core Themes and Clusters
#### 4.3.1 Theme 1 Supply Chain Management  
A prominent theme during both periods was the exploration of the impact of blockchain technology on supply chain management. Companies invest heavily in various technologies to create and maintain efficient, cost-effective, and viable supply chains [47]. Numerous studies have examined the potential of diverse blockchain-based systems for supply chains and the obstacles to their implementation. Although research suggests that blockchain technology may transform future supply chain management, the current literature indicates that its application in this field remains at an early stage [48; 49; 50; 51]. The following are some of the functions of this technology in supply chain management identified in various scholarly studies.  
Figure 4: Co-word network (2022–October 2024).  
Logistics Tracking and Tracing  
Research has shown that blockchain technology offers potential benefits, such as reducing transaction expenses and enhancing logistics performance. It achieves this by simplifying product tracking and providing advanced methods for safeguarding and compiling product information, which can be used for verification, origin determination, and quality assurance [52; 53; 54]. In addition, it may facilitate the development of shared manufacturing and digital systems [55]. This is particularly crucial in contexts where supplier reliability and product conditions are vital, such as food supply chains. In these scenarios, meticulous record-keeping and constant monitoring of goods' locations are necessary to prevent spoilage or to substantiate claims like natural ingredients, organic cultivation, or religious certifications, such as kosher or halal [56].
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 5 Experiments and Results
### Analysis and Comparison of Spectorgrams  
An example of the spectrograms of clean speech, noisy speech, and the enhanced speech by different models are shown in Figure 5. First, we focused on the black box to verify the effectiveness of the progressive generator. Before 0.6 s, a non-speech period, we could observe that the noise containing wide-band frequencies was considerably reduced since the progressive generator incrementally estimated the wide frequency range of the clean speech. Second, when we compared spectrograms of the multi-scale discriminator and that of the single discriminator, the different pattern was presented in the red box. The multi-scale discriminator was able to suppress more noise than the single discriminator in the non-speech period. We could confirm that the multi-scale discriminator selectively reduced high-frequency noise in a speech period as the sub-discriminators in multi-scale discriminator differentiate the real and fake speech at the different sampling rates.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Generator** & **Discriminator** & \(\mathbf{L_{D}(q)}\) & **PESQ** & **RTF** \\ \hline SERGAN [26] & U-net & Single & \(q=16k\) & 2.5898 & 0.008 \\ \hline \multirow{4}{*}{**Proposed**} & Progressive & Single & \(q=16k\) & 2.6514 & \\ \cline{2-6}  & & & \(q=8k\) & 2.6541 & \\ \cline{1-1} \cline{2-6}  & & & \(q=4k\) & **2.7077** & \\ \cline{1-1} \cline{2-6}  & & & \(q=2k\) & 2.6664 & \\ \cline{1-1} \cline{2-6}  & & & \(q=1k\) & 2.6700 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of results between different architectures of the multi-scale discriminator. Except for the SERGAN, the generator of all architectures used the best model in Table 2. The best model is shown in bold type.
# A Deep Learning-Based Crop Disease Diagnosis Method Using Multimodal Mixup Augmentation
## 6 Discussion  
In this study, we proposed a multimodal-based crop disease diagnosis method that uses both image and environmental data. We also extended the mixup augmentation, commonly used in the image domain, to be applicable to multimodal data.  
Previous studies on crop disease diagnosis have focused primarily on a single modality, such as images or environmental data. To improve accuracy, they have worked on refining model architectures [34; 35; 36], collecting new data to simulate real-world conditions [12; 13], or compressing models for real-time detection [37; 38; 39]. Recently, with the advancement of IoT technologies like low-power sensors and remote-sensing, it has become easier to acquire various multimodal data. Given these changes, we explored a crop disease diagnosis approach using multimodal data, rather than focusing on enhancing performance with a single modality data. The underlying assumption of this approach is that multimodal data,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Modality of Data** & **The Number of Params. (M)** & **FLOPs (G)** & **F1 Score (\%)** \\ \hline Environmental Data & 0.19 & 0.11 & 80.84 \(\pm\) 0.91 \\ Image & 23.56 & 10.76 & 87.79 \(\pm\) 0.70 \\ Multimodal & 23.75 & 10.88 & **90.37 \(\pm\) 0.59** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of model complexity and inference time.  
Figure 9: Confusion matrix by augmentation method: multimodal mixup (\(F1\) score 92.4%).  
combining image and environmental data, can be complementary. Experimental results showed that the crop disease diagnosis performance was as follows: environmental data (80.84%) < image data (87.79%) < multimodal data (90.37%). Additionally, we expanded the mixup augmentation method, widely used in deep learning for images, for multimodal deep learning, and observed a 1.33%p performance improvement compared to the original mixup augmentation [24].  
This study has several limitations. The first is having performed validation with different datasets, but it is challenging to obtain paired image and environmental data from crops. Our team is searching for new datasets to validate the proposed study and to explore new follow-up research. Another limitation is the difficulty of applying the method in real-world environments. The data used in this study were carefully curated, but real-world images can have different shooting angles and varying lighting conditions. Finding the best combination of data augmentation methods might be important. Environmental data collected from sensors can be noisy. Sensor errors might require signal processing, and missing data may need imputation. The model for environmental data used a simple LSTM structure with two fully connected layers, but more complex models might be required in real-world scenarios.
# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 3 Materials and Methods
### Design of the Multiscale Feature Adaptive Fusion Module
The output \(U\) is expressed as follows:  
\[U_{c}=S_{c}\cdot X_{c} \tag{3}\]  
where \(U_{c}\) represents the feature map of the \(c\)th channel of \(U\) and \(S_{c}\) represents the weight of the \(c\)th channel of \(U\). The feature map \(U\) with the weight information in the channel dimension is obtained by the squeeze-and-excitation module, which strengthens the expression of the relevant information, weakens the interference of irrelevant information, and extracts the feature information efficiently. To further capture the spatial features and compute the weight scalar maps, \(U\) is convolved with a kernel of size \(1\times 1\), and the channels' dimensions are compressed to obtain \(W\) with a size of \(3\times H\times W\), which is composed of the weight scalar maps \(\lambda_{a}\), \(\lambda_{\beta}\), and \(\lambda_{\gamma}\). Finally, the softmax activation function is used to normalize the weight feature map \(W\) to obtain \(\alpha\), \(\beta\), and \(\gamma\)[30], which are shared across all the channels. The expressions for \(\alpha\), \(\beta\), and \(\gamma\) are as follows:  
\[\alpha_{ij}=\frac{e^{\lambda_{aj}}}{e^{\lambda_{aj}}+e^{\lambda_{\beta ij}}+e ^{\lambda_{\gamma ij}}},\ \beta_{ij}=\frac{e^{\lambda_{\beta ij}}}{e^{\lambda_{aj}}+e^{\lambda_{\beta ij }}+e^{\lambda_{\gamma ij}}},\ \gamma_{ij}=\frac{e^{\lambda_{\gamma ij}}}{e^{\lambda_{\alpha ij}}+e^{ \lambda_{\beta ij}}+e^{\lambda_{\gamma ij}}} \tag{4}\]  
where \(\lambda_{aj}\), \(\lambda_{\beta ij}\), and \(\lambda_{\gamma ij}\) represent the pixel value of the \(i\)th row and \(j\)th column of \(W\), which is learned through standard backpropagation; and \(\alpha_{ij}\), \(\beta_{\beta}\), and \(\gamma_{ij}\) represent the pixel value of the \(i\)th row and \(j\)th column of \(\alpha\), \(\beta\), and \(\gamma\), which are defined by controlling the parameters \(\lambda_{\alpha ij}\), \(\lambda_{\beta ij}\), and \(\lambda_{\gamma ij}\). In addition, \(\alpha_{ij}\), \(\beta_{ij}\), and \(\gamma_{ij}\) should fit the following formula:  
\[ \begin{array}{l}\alpha_{ij}+\beta_{ij}+\gamma_{ij}=1\\ \alpha_{ij},\beta_{ij},\gamma_{ij}\in[0,1]\end{array}  \tag{5}\]  
Using this method, the MFAF module obtains the feature map of the fusion weight parameters at the layer \(l\) as follows:  
\[MFAF_{l}=V3_{l}+V4_{l}+V5_{l}=\alpha\cdot X_{3\to l}+\beta\cdot X_{4\to l}+ \gamma\cdot X_{5\to l} \tag{6}\]The features at all the layers are adaptively aggregated to obtain \(V3_{l}\), \(V4_{l}\), and \(V5_{l}\), which are used to recognize the passenger aircraft targets. When the detected target is a small target, the shallow feature map is given a higher weight. Through the reasonable distribution of the weight value of the shallow features and the deep features, the aircraft targets can be recognized more accurately.
## 3 Results
### Comparison of Spectral Information Versus Angular Performance  
The goal here was to establish whether spectral variability is more significant than angular variability for retrieving structural parameters, or vice versa. Thus, we compared Scenario 1--four spectral bands observed at nadir--against Scenario 2--single spectral band and multi-angular data (Table 2, Figure 5a). The Scenario 2 showed very similar performance when compared to the Scenario 1. The multi-angular green band model showed the smallest rRMSE, for both H\({}_{\text{mean}}\) and CC, with a higher performance for H\({}_{\text{mean}}\) (rRMSE = 36.7% versus 42.7% for H\({}_{\text{mean}}\) and CC, respectively). However, this was only marginally lower than the model using all four spectral bands observed at nadir, with an improvement of 1.8% and 4.54% for H\({}_{\text{mean}}\) and CC, respectively. The overall results indicated a minimal difference in model results when using multiple spectral bands observed at nadir versus multiple angular bands for a single spectral band. There was no statistical significant difference between Scenario 2 and Scenario 1 obtained for both the parameters (\(F_{5,\,7335}=4.36,\,p>0.05\)).  
Figure 4: Histograms (**a**) the LiDAR mean tree height and; (**b**) the LiDAR canopy cover at 275 m spatial resolution for the savanna, indigenous forest and plantation vegetation types, separately and combined.
## 4 Parametric Study by Computational Analysis
### Computation Model
When discussing the single U-rib size parameters, the spacing size parameters, \(e\) and \(w\), were set as 4112.5 mm and 10,000 mm, respectively. This setting aims to achieve compatibility between the bottom side of the BUSP and the crossbeams. Once all the size parameters are determined, the coordinates of each node on the BUSP can be calculated using geometric methods. A Python script was developed for ABAQUS to facilitate the process of parametric modeling. In this script program, the nodes of the U-rib are assigned numerical labels, as shown in Figure 11, with Node 1 serving as the origin of the coordinate system. In the case that the nodes of the U-rib have been processed according to Figure 11, the coordinates of all the U-rib nodes can be expressed as a function of the size parameters. Table 5 presents the \(x\)-coordinates (abscissa) and y-coordinates (ordinate) for the eight nodes of the U-rib, in which \(c\) is calculated according to the following formula. \[c=|t/\tan(\alpha/2)| \tag{1}\]  
Figure 11: Node numbering of U-rib. Figure 10: Size parameters of the BUSP (unit: mm). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Node Number** & \(x\)**(mm)** & \(y\)**(mm)** \\ \hline
1 & 0 & 0 \\
2 & \(a\) & 0 \\
3 & \((a-b)/2\) & \(t-h\) \\
4 & \((a+b)/2\) & \(t-h\) \\
5 & \((a-b)/2-c\) & \(-h\) \\
6 & \((a+b)/2+c\) & \(-h\) \\
7 & \(-c\) & \(-t\) \\
8 & \(a+c\) & \(-t\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The abscissa and ordinate of the nodes of the U-rib. In the context of bridge structures, one common type of deck distress is the cracking and failure of the concrete overlay due to excessive tensile stress. These cracks can also lead to water infiltration, which in turn causes corrosion of the underlying steel structure. This deterioration significantly impacts the service life of the bridge structure. Therefore, the focus of this study is to investigate the variations in stresses and displacements of the concrete overlay when different values are assigned to the three independent parameters (\(a\), \(h\), and \(t\)). By examining how these parameters affect the behavior of the concrete overlay, we can gain insights into the potential causes of cracking and failure. This knowledge can then be used to develop strategies for improving the design and maintenance of bridge structures, ultimately enhancing their durability and extending their service life.
## 3 Results and Discussion
### Mesogen Orientation of PAz Films
#### 3.1.1 PAz Film Contacting with Air  
Figure 1 displays the GI-SAXS data for PAz films with a 100-nm (a) and 400-nm (b) thickness on a glass substrate at room temperature. These thicknesses were chosen because the former should have significant influence from the constraint effects from the solid surface, and the latter could be mostly free from such constraints. In general, the surface constraint effect reached the level of some tens of nanometers [41]. The sample was annealed at 120 \({}^{\circ}\)C (isotropic phase) for 10 min and then subjected to measurements. The scattering data were essentially the same for the two thickness films. The scattering spots were observed in the out-of-plane direction at 2\(\theta\) = ca. 3.2\({}^{\circ}\) (spacing: \(d\) = 2.8-2.9 nm) and 2\(\theta\) = ca. 6.4\({}^{\circ}\) (\(d\) = 1.4 nm), corresponding to (001) and (002) scattering, respectively. These data indicate a lamella structure formation oriented horizontally with the substrate. The film with 400 nm gave the clearer scattering signals because more intense X-ray signals were obtained. In this grazing angle measurement configuration, the small deviation in the \(d\) value (0.1 nm) between the two samples was within the experimental error. The \(d\) spacing of 2.8-2.9 nm shows that a smectic C phase with a tilted molecular orientation with the lamella plane was formed [19]. Some disordering was involved in the film of a 400-nm thickness, as can be seen from the azimuthal arc scattering. This seems to reflect that more orientational freedom was allowed, because the constraint from the surface was lessened.