esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We incrementally developed an online DA mechanism for the market of perishable goods to realize fair resource allocation among traders by considering the losses due to trade failures. We explained that sellers have a high risk of losing money by trade failures because their goods are perishable. To reduce trade failures in the perishable goods market, our DA mechanism encourages buyers' truthful bidding by penalizing buyers' failed bids without spoiling their individual rationality. The experimental results using multi-agent simulation showed that our DA mechanism was effective in promoting truthful behavior of traders for realizing the fair distribution of large utilities between sellers and buyers.  
The experimental results in this study are very limited for any comprehensive conclusion on the design of online DA for perishable goods. For example, we assumed that buyers accept the penalty imposed by the mechanism as long as buyers' payment does not exceed their bid (thus, not violating their individual rationality). However, their tendency to accept the penalty might depend on possibility of their successful trades and potential profits from their resales. The influence of traders' sophisticated behavior in the market needs to be investigated further in future research. We need to investigate other types of bidding practices and test them under various experimental settings. In addition, human behavior in the market must be examined carefully to evaluate the designed mechanism [25].  
In developing countries, there is strong demand for improving efficiency in agricultural markets [26] and this is also true for rural societies in developed countries, including Japan.  
Figure 5: Agents’ utility under seven market conditions. (**a**) Buyers’ utility; (**b**) Sellers’ utility; (**c**) Total utility.  
As a real-life application of our online DA mechanism, we have been operating a substantiative e-marketplace for trading oysters cultivated in the north-eastern coastal area of Japan (Miyagi prefecture) for five years. Only local traders used to participate in the markets run by the fisheries cooperatives because the highly perishable nature of fishery products prevents their trade from being open to wider participants. This has led to low incomes and the collapse of local fishery industries, which also suffered severe damage from a massive earthquake and tsunami in 2011. Although we do not have sufficient data to show the effectiveness of our online DA mechanism, we hope to contribute to promoting successful deployment of electronic markets for fisheries and improve the welfare of people in the area by attracting more traders online.  
This work was supported by Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (C) Grant Number 00358128.  
The author declares no conflict of interest.
# Three-Dimensional-Printed Polymeric Cores for Methane Hydrate Enhanced Growth
## 2 Materials and Methods
### X-ray Computed Tomography  
A micro- and nanofocusing X-ray computed tomography system (General Electric V! l come! X S 240; Wunstorf, Germany) was used. Two samples of Berea sandstone cut to 4 \(\times\) 4 \(\times\) 4 mm and 2 \(\times\) 2 \(\times\) 2 mm were selected to create 3D model of the core. The samples were scanned using a microfocus tube at a voltage of 100 kV and a current of 100 mA. The scanning resolution was 3.4 and 5.8 \(\upmu\)m, respectively. Then, the 3D models were reconstructed in the Phoenix data! x software. Segmentation and processing of samples were carried out in the Avizo software. Sandstone models with dimensions 4 \(\times\) 4 \(\times\) 4 mm and 2 \(\times\) 2 \(\times\) 2 mm (in the latter case, only PLA* sample) were enlarged 9 and 20 times, respectively. A rectangular parallelepiped with dimensions of 27 \(\times\) 15 \(\times\) 7 mm cut out of the obtained models was used for 3D printing. The pore space was segmented using the Magic Wand function, and the effective porosity along the Z scale was selected using the Axis connectivity function. Finally, a mesh model (STL format) based on effective porosity subtracted from the parallelepiped volume was obtained using the Meshing operation.  
The 3D-printed polymeric cores were rescanned at 100 kV, 130 mA (the resolution of all samples was 32 \(\upmu\)m). The pore space was segmented for each obtained digital model. Effective porosity along Z-axis and equivalent diameters distribution over the volume were obtained.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### The Need for CR-Based UAVs
#### 4.1.3 Spectrum Scarcity  
Despite a massive growth in the number of wireless connected devices, most of the radio spectrum is underutilized. Because of the fixed spectrum allocation policy, a big portion of the radio spectrum is reserved to sporadic PUs, while other spectrum portions are overloaded, such as Wi-Fi and mobile bands. Moreover, the UAVs traditionally operate within unlicensed spectrum bands (i.e., open/free spectrum), such as IEEE S-Band, IEEE L-Band, and ISM, fixedly defined in their hardware, under the same fixed spectrum allocation policy [31].  
In this context, CR emerges as a promising technology to solve these issues by enabling DSA [10]. CR-based UAVs are able to select idle spectrum bands for communicating. Thus, the UAV overall communication quality increases when using CR, especially in overcrowded areas.
## 2 Background and Preliminaries
### Translation-Invariant Signal Decomposition Using the Derived Wavelet Frames
#### 2.1.1 Dual-Tree Complex Wavelet Packet Decomposition  
Dual-tree complex wavelet packet decomposition (DCWPD) is constructed based on dual-tree complex wavelet basis, which it consists of two scaling functions and two wavelet functions.  
In orthonormal cases, for the complex valued wavelet function:  
\[\psi^{\mathbb{C}}(t)=\psi^{\mathbb{R}e}(t)+i\cdot\psi^{\mathbb{S}m}(t), \tag{1}\]  
there is a restriction of approximate Hilbert transform shown as:  
\[\psi^{\mathbb{S}m}(t)=H\Big{\{}\psi^{\mathbb{R}e}(t)\Big{\}}, \tag{2}\]  
where \(\psi^{\mathbb{R}e}(t)\) and \(\psi^{\mathbb{S}m}(t)\) are the real part and imaginary part of \(\psi^{\mathbb{C}}(t)\) respectively, and \(i=\sqrt{-1}\) is the imaginary unit. Equivalently, a half sample delay equation exists for impulse response functions of imaginary wavelets \(h_{1}^{\mathbb{S}m}(t)\) and real wavelets \(h_{1}^{\mathbb{R}e}(t)\).  
\[h_{1}^{\mathbb{S}m}(t)(n)\approx h_{1}^{\mathbb{R}e}(n-0.5). \tag{3}\]  
Let the \(\mathbb{Z}\) transform of a discrete series \(\{x(n)\}\) be represented as:  
\[X(z)=Z\{x(n)\}=\sum_{n=-\infty}^{+\infty}x(n)z^{-n}. \tag{4}\]  
The filter-bank structure of DCWPD is shown in Figure 1.  
Figure 1: Filter-bank structure of the dual tree wavelet packet decomposition.  
The notation \({}^{\prime}(\cdot)^{\prime}\) denotes \(\Re e\) or \(\Im m\). This means that the real filter tree of DCWPD is independent of the imaginary filter tree, but uses the same filter structure. Both are iterative decomposition processes based on a special hybrid wavelet bases and a binary tree structure. More details about the two filter branches can be found in the original article published by [PERSON] and [PERSON] [44]. The employed filters can be classified into three categories:  
(1) Wavelet basis at the first level. These filters are \(\left\{h_{10}^{\Re e}(n),h_{11}^{\Re e}(n),h_{10}^{\Im m}(n),h_{11}^{\Im m}(n)\right\}\) (Figure 2a,b)), and they satisfy the following equation.  
\[\left\{\begin{array}{l}h_{10}^{\Im m}(n)=h_{10}^{\Re e}(n-1)\\ h_{11}^{\Im m}(n)=h_{11}^{\Re e}(n-1)\end{array}\right.. \tag{5}\]  
(2) Conventional dual-tree complex wavelet basis to obtain wavelet series \(\left\{d_{k,j}^{\C}(n)\right|k\geq 2,\ j=1,2\right\}\). The associated filters are \(\left\{h_{0}^{\Re e}(n),h_{1}^{\Re e}(n),h_{0}^{\Im m}(n),h_{1}^{\Im m}(n)\right\}\) (Figure 2c,d).  
(3) Additional basis to generate extended wavelet packet series \(\left\{d_{i,j}^{\C}(n)\right|i\geq 2,\ j\geq 2\right\}\). The associated filters are:  
\[\left\{\begin{array}{l}f_{0}(n)=h_{10}^{\Im m}(n)\\ f_{1}(n)=h_{11}^{\Re e}(n-1)\end{array}\right.. \tag{6}\]  
The more similar the filter-bank basis function is to the raw signal, the better the defect-related features will be extracted [45]. This paper employed the wavelet basis constructed by [PERSON] in the literature [46], the related time-frequency atoms are shown in Figure 2.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 4 Discussion
### Intersubject Variability  
Large-scale MI data are needed to build a BCI system. Many subject-specific classification models have been proposed for BCI. One should note that the subject's performance in the MI task will degrade after a certain time. Hence, researchers often collect data across multiple sessions to minimize this performance degradation; nevertheless, intrasubject variability can still be observed [38]. In recent years, databases have been constructed with the MI data of many subjects with fewer subject-specific sessions to perform subjectindependent classification [22; 38]. The subject variability issue in subject-independent classification is controversial; however, this issue is also present in subject-specific classifications. Recent studies have performed subject-independent classification with low accuracy (see Table 4). One reason is that variable subject-related artifacts, such as the subject's mental state, can affect the MI task data. Feature extraction methods such as wavelet features can effectively extract EEG band information related to MI tasks (8-30 Hz) with less computational complexity. This proposed methodology has addressed intersubject variability issues by extracting maximum signal information associated with MI tasks and representing its corresponding class efficiently.
## 4 Experiments and Results
### Experimental Results  
The advantages of GLSNet were verified through experiments comparing it with various models on the Vaihingen and DeepGlobe datasets.
# Advanced Optimization Techniques for Federated Learning on Non-IID Data
## 2 Background
### Data Augmentation  
Data augmentation is a crucial process in training deep neural networks, which helps increase the quantity and variety of training data, ultimately improving the performance of the models. By applying data augmentation techniques, we can address the issue of overfitting and enhance the model's ability to generalize to new, unseen data. This is achieved by generating new, synthetic data that are slightly different variations of the original data.  
For image data, common data augmentation techniques include geometric transformations, color transformations, and the introduction of noise. Geometric transformations involve altering the spatial properties of the images, such as rotation, flipping, cropping, and shifting, to create new versions of an image from different angles and orientations. Color transformations modify the color properties of the images, including changes in brightness, contrast, saturation, and hue, simulating different lighting conditions, and increasing the diversity in color. Lastly, adding random noise helps models become more robust to potential imperfections and lower-quality images [32].  
For improving model performance and achieving greater fairness, addressing data heterogeneity and class imbalance across distributed clients is important in FederatedLearning (FL). New data augmentation techniques have arisen to deal with these difficulties while still safeguarding data privacy. Methods such as Federated Augmentation (FAug) allow clients to share generative models instead of original data, which produces synthetically generated data that facilitates the balancing of class distributions [33]. Among other methods, Federated Generative Adversarial Networks (FedGAN) permit clients to collectively train GANs to develop an assortment of synthetic data, strengthening the global model's learning potential without any compromises on privacy [34]. The approach known as FedMix uses the data augmentation technique Mixup to minimize data heterogeneity and improve model robustness within the framework of federated learning [35]. On top of that, techniques such as federated distillation deal with non-IID data problems by enabling clients to participate by sharing model outputs (logits) instead of model parameters or raw data, which boosts the performance of the global model and protects privacy [36; 37; 38]. The use of these new data augmentation strategies is critical in addressing the restrictions resulting from non-IID and imbalanced data in FL models and applications.  
Ultimately, data augmentation is a valuable tool in machine learning, especially in applications where the available data are limited or when the goal is to increase the diversity of the dataset, as often happens in federated learning scenarios. By using these techniques, we can ensure that the models are exposed to a wider range of data variations, leading to improved generalization and robustness in real-world applications.
## 3 Results and Discussion  
### Composition of the Essential Oil  
The determination of the chemical composition and relative quantification of the compounds in the PHEO were performed via GC-MS. Six components were identified through the chromatographic analysis of PHEO. The main constituents were identified according to their retention index (_RI_) compared to a homologous series of _n-alkanes_ and confirmed by comparison of the mass spectrum of the compounds with the NIST-14 spectrometry library.  
Safrole was the most abundant compound, accounting for 93.0% of PHEO's composition, followed by bicyclogermacrene (2.05%), pentadecane (1.60%), spathulenol (1.46%), _p_-cymen-8-ol (1.20%), and (_E_)-caryophyllene (0.69%) (Table 2).  
The absolute safrole concentration in PHEO was determined through GC/FID, according to the retention time of this substance. In this case, safrole represented 0.85 mg L\({}^{-1}\) of the EO constitution. Other studies confirm safrole as the most abundant component of PHEO [6; 21; 22; 27]. The other identified constituents have also been reported in the literature--namely, pentadecane [22], bicyclogermacrene and (_E_)-caryophyllene [6; 21; 22; 27], spathulenol [6; 21; 27], and _p_-cymen-8-ol [6; 27].
# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 4 Results
### Ablation Experiment  
To quantify the contribution of individual components, systematic ablation experiments were conducted based on YOLOv8n (Table 3) under identical training configurations. The impacts of WDLoss and the FCB, FEB, and GCAB (FFG) modules were evaluated using mAP@50, mAP@50-95, and recall. The baseline YOLOv8n achieved an mAP@50 of 34.6%, mAP@50-95 of 19.7%, and a recall of 35.1%. When WDLoss was integrated, mAP@50 slightly increased to 34.62%, and mAP@50-95 improved to 20.0%, demonstrating its stability and discriminative properties in regression head optimization and a positive effect on bounding box aggregation learning. Integrating only the FFG modules (excluding WDLoss) enhanced multi-scale feature extraction, contextual modeling, and fine-grained target localization. This resulted in an mAP@50 of 34.61% and an mAP@50-95 of 20.3% (a 0.6% increase over the baseline). Recall remained at 34.9%, indicating the modules did not significantly increase false positives. Finally, the combination of WDLoss and the FFG modules (FFG-YOLO) optimized model performance, achieving an mAP@50 of 34.7%, mAP@50-95 of 20.1%, and a recall of 34.5%. This result verified the synergistic effect of WDLoss with the FFG module, enhancing feature characterization, foreground region focusing, and jointly improving the accuracy and stability of small-target detection, especially in complex backgrounds.
## 4 Materials and Methods
### Cell cycle Analysis  
HECa10 and ARPE-19 cells were plated in a 6-well plate for 24 h, and next, the cells at 60-70% confluence were treated with different RSV concentrations (1, 5, 10, 50, and 100 uM) for 24 h. After the treatment, floating cells were collected for future analysis and adherent cells were washed with PBS and harvested by trypsinization. Subsequently, all cells (floating and adherent) were washed twice with PBS, centrifuged (500\(\times\)\(g\)/ 5 min), and fixed with 70% cold methanol at \(-\)20 \({}^{\circ}\)C for 24 h. Fixed cells were resuspended in PBS and centrifuged at 500\(\times\)\(g\) for 5 min. Cell pellets were resuspended in PBS with propidium iodine and RNase A solution according to the manufacturer protocol (Cayman ChemicalCompany, Ann Arbor, MI, USA) and incubated for 30 min. in the dark at room temperature. The stage of the cell cycle was assessed by flow cytometry (FACS Calibur, Becton Dickinson, USA, Franklin Lakes, NJ, USA ) and calculated by ModFit LT 4.1 software (Verity Software House, Topsham, ME, USA). The assay was performed in triplicate (\(n=6\)).
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 2 Mutual Coupling Issue of MIMO Systems  
### Causes of Mutual Coupling  
The information theoretical capacity grows linearly with the number of elements in the MIMO systems [7]. Later, [PERSON] and [PERSON] [11] pointed out that the linear increase of channel capacity was not unconditional, which was achieved by arranging antenna elements with wide spacing (more than several wavelengths). However, it is unrealistic for modern mobile users because most 5G applications, especially portable devices, are composed of integrated circuits and the spacing of each antenna element is not allowed to exceed one wavelength. In this narrow space, the electromagnetic interference existing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Antenna Element** & **System Aize (mm\({}^{2}\))** & **Element Distance (mm)** & **Isolation (dB)** & **ECC * & **MIMO Order** \\ \hline I-shaped slot antenna [8] & 140 \(\times\) 70 & 15.8/21.3 & \textgreater{}11 & \textless{}0.1 & 8 \\ Inverted & & & & & \\ F-shaped antenna [9] & 136 \(\times\) 68.8 & 14/29 & \textgreater{}10 & - & 8 \\ Inverted & & & & & \\ \(\pi\)-shaped antenna [8] & 150 \(\times\) 80 & 10/13.5/17.5/18 & \textgreater{}12 & \textless{}0.15/\textless{}0.1 & 12 \\ \hline \hline \end{tabular}  
* Envelope correlation coefficient: the independence between antennas on radiation pattern and polarization is evaluated.  
\end{table}
Table 1: Comparison among the referenced techniques of distance optimization.  
between antenna elements is known as the mutual coupling effect, which is an unavoidable issue in MIMO systems.  
Probing the causes of mutual coupling, the focus is on analyzing the effect of current. As regardless of the form of the antennas, the working principle of the antennas is based on the common mechanism of radiation generated by variable speed charges [12]. There are three sources of mutual coupling effects in MIMO systems, as follows.  
1. Near field coupling. When antennas are closely spaced, they are in near field zone of another antennas and the effect from near field EM waves is dominant [13]. This fields decay follow distance as \(\rho^{-2}\) or \(\rho^{-3}\)[14];
2. Surface-wave coupling. This coupling often occurs in microstrip antennas, due to sharing the same ground plane [15]. During the feeding process of the antenna element at the feed point, a distributed current is generated on the ground plane. This current will flow to nearby antenna elements, dispersing the energy and creating interference;
3. Free-space coupling. This is because in the process of radiating electromagnetic (EM) waves outward from the excitation element, part of the energy will be coupled on the adjacent antennas and excite the adjacent antennas to generate new currents. In general, the contribution to the mutual coupling of the surface wave is smaller than the space wave due to the fields of surface wave decay as \(\rho^{-1/2}\), which is slower than \(\rho^{-1}\) dependence of space wave coupling [14; 16].
## 7 Conclusions  
This paper presents a multiple chain oriented logical topology for WSNs. The design of the topology is governed by various factors including resource constraints such as energy, time, and computational complexity, as well as networking and architectural factors, and network management issues. Detailed descriptions of the construction of the proposed topology are provided. Moreover, a three-layer hierarchical management architecture is proposed for the multiple chain oriented topology. The network management scheme works in line with the proposed topology for managing different issues such as fault detection, performance management, and security management.  
The proposed topology entails three phases: topology formation phase, steady state phase, and topology update phase. While the first phase takes place only once during the initial stage, the remaining two phases continue in rotation. Various issues, such as the optimal number of chains in the system, the optimal number of nodes in a chain, the time when the leader nodes need to be changed, and when the chains should be reconstructed, are described in detail. The communication abstraction describes the process by which sensor nodes send and receive different control messages and sensed data.  
In designing the proposed multiple chain oriented topology, it is important to note that reducing the energy consumption will not always result in a longer system lifetime. Instead, balancing resources among sensors and saving energy for those more resource-constrained sensors are very helpful in lengthening the overall system lifetime. The chains of the proposed topology were constructed, and the leader nodes were selected on the basis of this principle.  
Simulation results showed excellent results in favour of the proposed logical topology. The proposed logical topology outperformed LEACH, PEGASIS and COSEN not only in total system energy consumption but also in system lifetime. The key reason behind this is the more even distribution of energy consumption. The proposed topology also solves the high delay problem of PEGASIS.  
However, there are still some areas where the performances of the proposed logical topology can be enhanced further. These areas may include: (i) the use of node scheduling so that a number of sensor nodes can be turned off while still maintaining the coverage or other user requirements; (ii) the creation of localised chains (where all the chains are restricted in precise areas so no chain crosses any other chain in order to avoid more interferences); and (iii) designing mobile data collectors.