esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The PrefixSpan algorithm was compared with sequential patterns, such as the Apriori algorithm, GSP algorithm, and Freespan algorithm. The performance of the PrefixSpan algorithm is significantly higher than the others [62; 63]. Moreover, the PrefixSpan had a consuming time that was faster than the others because the proposed algorithm was not generated from the frequency of items within the search space in the projected databases. However, the PrefixSpan's long pattern length is a limitation. Researchers have expressedinterest in prefix algorithms and their improvements [46; 56; 57; 58; 59]. The study of [59] is relevant to this paper from an application standpoint, as it revealed that shelf layout affects the display of available food items, information that could be useful in mobile applications. The authors of [64] proposed a sequential pattern mining method for supermarkets that is similar to our method, but our proposed method is different because it orders products in regard to both their shelves and individual profit. A common problem in sequential pattern mining methods is still the criteria design, which should be solved by focusing on individual shelves and using the MBHS algorithm for multi-objective optimization. The MBHS algorithm is a population-based meta-heuristic optimization algorithm and can solve the complex problem to reach the optimal solution. In [65; 66; 67; 68], the MBHS algorithm was developed to reduce the cost by objective function design, where the MBHS in many studies has different objective functions, and developed the combination of the search method with harmony search. The MBHS in our purposed methods is applied to the design as a multi-objective function combined with the identified sequential pattern in shelf layouts to enhance pattern generations with the aid of heuristic algorithms that have defined multi-object functions. These multi-objective functions were designed to filter and solve product layout problems. A list of related works, along with their methods and domains, is shown in Table 1.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 2 Overview of the Existing Data Acquisition Techniques for Road Extraction
### High-Resolution Imaging Technology
#### 2.1.1 Data Acquisition Methods and Characteristics  
High-resolution images are primarily recorded using spaceborne and airborne sensors. Spaceborne high-resolution imaging techniques have a broad area coverage and stable revisit periods; however, the cost of the satellite is high, and the images are easily affected by the atmosphere [79]. Compared to the spaceborne high-resolution images, the airborne high-resolution images possess higher resolutions and are less affected by the atmosphere. However, the working efficiency of airborne cameras is lower than that of the spaceborne instruments because of the lower flight altitude and smaller coverage [80]. Airborne high-resolution images can be obtained using manned aircrafts and unmanned aerial vehicles (UAVs). In recent years, several imaging systems with high-resolution cameras mounted on UAVs have been rapidly developed; these systems can achieve centimetre-level spatial resolution [81].
# A Time Bank System Design on the Basis of Hyperledger Fabric Blockchain
## 2 Background and Related Work
### Blockchain in Regular Banking Industry  
In facing the impact of economic transformation, internet development, and financial innovations, the regular banking industry requires urgent transformation and is seeking new growth avenues. As indicated in References [9; 10; 11], blockchain technology has the potential to facilitate global money remittance, smart contracts, automated banking ledgers, and digital assets; therefore, it could revolutionize the underlying technology of the payment clearing and credit information systems in banks. However, the regulation and actual implementation of a decentralized system are problems that remain to be resolved. Therefore, Reference [9] promoted the establishment of a \"regulatory sandbox\" and the development of industry standards, while Reference [10] addressed the key issues that must be considered in developing such ledger-based technologies in a banking context. Reference [11] reported, specifically, the application of blockchain in the China Foreign Exchange Trade System.  
In a nutshell, blockchain technology benefits a time bank system in several ways. Firstly, blockchain enables peer-to-peer token trading. In a time bank system, there is a token called time credit and it is used for quantifying the value of services in terms of time units. All the on-chain data are immutable. That is, no one can modify the data recorded on the blockchain, such as the service-related data or the balance in the time credit wallet. The reward rate and the matching algorithm are transparent to everyone on the blockchain. The reward-related information is written on smart contracts. The reward rate is the rate for rewarding service providers, as detailed in Section 4. Paying transaction fees is essential in a few currently existing time bank systems. In a blockchain-based time bank system, the payment of transaction fees is optional.
## 1 Introduction  
Irreversible electroporation (IRE) is a nonthermal ablation technique that uses high-voltage electrical pulses to create permanent pores in the cell membrane of target tissue, leading to cell death [1, 2]. Currently, IRE is considered one of the most efficient methods of destroying tumors in various organs, including the liver, pancreas, kidney, lung, brain, and prostate [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. In clinical use, needle-shaped electrodes puncture the target tissue surrounding the tumor to deliver electrical pulses with the electric field strength of a few kV/cm [13]. However, this electrode configuration is unsuitable for vulnerable gastrointestinal tracts with several hollow organs [14].  
Catheter-based IRE presents a promising alternative tool for effective treatment of gastrointestinal tumors or undesired tissues [14, 15]. In the clinical process, a catheter is inserted into the body and guided to the tumor site, where it delivers electrical pulses to the tumor. This method is suitable for preventing perforation or injury. [PERSON] et al. designed two types of catheters, the needle-type and the basket-type, and investigated the feasibility and effectiveness of endoscopic IRE therapy on hollow viscous tissues [16]. Further, [PERSON] et al. demonstrated the feasibility of IRE based on a three-electrode tubular catheter in an experimental liver model in both ex vivo and in vivo studies [17]. In addition, interdigitated electrodes (IDEs) have been studied for their ability to reduce the required voltage and localize the electric fields at the desired region [13; 14]. IDEs further allow the electrode distance to be closer and thinner due to simple in-plane geometry [18; 19]. Most of the finger structures of IDEs reported so far have a rectangular cross-section, and most studies in this area have focused on the effects of the finger's width, length, and period have been conducted [20; 21]. Similarly, studies on IDEs used for electroporation have mainly concentrated on electrodes with a rectangular cross-section and various pulse settings (duration, total pulse number, frequency, voltage), but there has been no study on finger shape according to the authors' knowledge [13; 22; 23; 24]. Therefore, further improvement in IDEs to enhance the electric field strength is required to increase the practicability of catheter-based IRE.  
In this work, we propose triangular IDEs for catheter-based IRE by demonstrating electrical injury in a gastrointestinal model. The triangular IDEs allow strong electric fields to be induced at the tip of the electrode fingers and are simply fabricated using laser-induced graphene technology on a flexible polyimide film [18]. A three-dimensional finite element model based on COMSOL Multiphysics 5.4 software was adopted to investigate the enhancement in the electric field through the electrode design. With triangular IDEs, the probability of cell death due to electrical injury (EI) in tissue increased around 10 times compared to that of conventional rectangular IDEs at the same electrode distance. These results could indicate an alternative strategy for designing IDEs for catheter-based IRE.
# Effects of Co-Solvents on the Performance of PEDOT:PSS Films and Hybrid Photovoltaic Devices
## 4 Discussion  
Devices with neat PEDOT:PSS have very poor device performances especially lower short-circuit current. This is primarily a reflection of the lower conductivity of PEDOT:PSS, as the anti-reflective properties remain largely unchanged with the addition of co-solvents. It is unlikely that the neat PEDOT:PSS films coated on textured substrates were incomplete as the coating properties of the dispersions without co-solvents should not be significantly different, however, we cannot rule out this as a possible reason for the decreased performance. It is also possible that the contact resistivity of the Ag/PEDOT:PSS interface for neat PEDOT:PSS is larger than the value demonstrated for the films with co-solvents which would result in a non-optimal carrier collection and increased series resistance. On the addition of co-solvents, the Jsc values increase to as high as 30 mA/cm\({}^{2}\). Devices with either co-solvent show similar devices performances but the champion cell was with 7 wt% DMSO recipe at lower spin speed. The DMSO sample at 4000 rpm also has the highest Jsc, a reflection of the low reflectance throughout the solar spectrum as seen from UV-VIS plot. Devices fabricated at 4000 rpm were consistent and had higher Jsc values which can be related to a lower reflectance. However, the highest performing cells for both EG and DMSO were obtained at lower speeds. In this work, considerable attention was given to investigate and optimize the individual properties of the materials being used, as well as the cell architecture as a whole, considering the a priori assumption that the addition of co-solvents would yield a better performing solar cell such that the same rigor was not given to neat PEDOT:PSS.  
The devices fabricated with DMSO and EG show similar performances with the biggest variation coming in the fill factor. An analysis of the fill factor showed that shunting (Rsh) affected these cells the most. Series resistance losses cannot be dismissed but all cells faced similar losses. The origin of the shunt resistance is not known. Devices with EG co-solvents had more reproducible results, as can be seen in Figure 6.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 1 Introduction
To enhance model interpretability, we used LIME and Grad-CAM techniques, which visualize the regions of the input data most influential to the predictions, enabling researchers to gain a clearer understanding of the model's decision-making process. Using only channels T5 and O1 of the TUAB EEG data our method obtained results comparable to multi-channel models for classifying normal and abnormal EEG signals. The main contributions of this study are as follows:  
* Explores signal images, spectrograms, and scalograms as complementary representations of EEG signals, and compares their effectiveness in capturing features relevant to abnormal EEG detection across the time, frequency, and time-frequency domains. * Applies a DenseNet-based transfer learning strategy to enhance model performance with limited EEG data and minimize the need for training deep networks. * Implements a post-processing approach across multiple image representations to further improve classification performance. * Incorporates explainable AI techniques (LIME and Grad-CAM) to visualize the most influential regions of input data, thereby improving the interpretability and transparency of the model's decision-making process. While several studies have explored deep learning for EEG analysis, relatively few have combined DenseNet-based transfer learning with signal images, spectrograms, and scalograms for abnormality detection using only two channels. By integrating lightweight representations, an efficient model architecture, and explainable AI techniques, this work contributes a novel and practical approach to interpretable, low-complexity EEG classification, well suited for real-world clinical applications where computational and interpretability constraints are important.
## 5 Experimentation, Results, and Analysis
### Analysis
#### 5.4.3 Visual Explanation Using Grad-CAM
###### Cultural Relevance and Weighting  
To ensure that cultural appropriateness and semantic resonance were considered, the scoring rubric included a secondary checklist for cultural context:  
* Whether the caption reflects contextually relevant expressions in Tamil.
* Whether culturally significant terms (e.g., objects, relationships, settings) are accurately captured.  
If a caption was found to be grammatically accurate but lacked cultural nuance, a penalty of 0.5 points was applied to the Adequacy score. This adjustment ensures that the evaluation favors not just syntactic correctness but also cultural fidelity, which is critical in low-resource language generation.
## 4 Microgrid Energy Management Approaches
### Commercial Software  
As with microgrid sizing, commercial software such as HOMER, DER-CAM, and EPLANopt are also used to plan energy management within the microgrid. In [82], the EPLANopt software is utilized alongside a multi-objective evolutionary algorithm to optimize the energy system of Favignana Island for sustainability by the year 2050. EPLANopt models and simulates various energy system configurations, incorporating data such as energy demand and renewable energy potential. The evolutionary algorithm analyzes these configurations, evolving a set of solutions through processes like mutation and selection. While EPLANopt provides detailed simulations, the evolutionary algorithm identifies the most efficient and sustainable solutions by balancing objectives such as cost, emissions, and renewable energy usage. In [83], the RETSreen Expert software is used to examine a grid-connected microgrid wind farm in Ghana, evaluating its technical, financial, and environmental benefits. It investigates the NPV, the internal rate of return, the cost of electricity production, and the emission savings to gauge the project's feasibility and its role in sustainable energy progress. RETSreen Expert software is also used in [84; 85; 86; 87]. In [88], a comprehensive study is presented on integrating DER into healthcare systems, focusing on a hospital case study in Azad Jammu and Kashmir. The study uses HOMER Pro and RETSreen Expert software to analyze a proposed hybrid renewable energy system against the current energy setup. The result indicates that HOMER Pro and RETSreen Expert predict the increased energy output with the proposed system, though RETSreen Expert tends to give more optimistic projections. The study highlights the efficiency and sustainability of integrating renewable energy into healthcare while demonstrating significant potential for enhancing infrastructure resilience and reducing costs. HOMER is the most used software in the literature for energy management. In [89], the research details an optimal microgrid design for Basco Island in the Philippines using HOMER software. It integrates PVs, WTs, diesel generators, and BESSs to boost power reliability and sustainability. The study identifies the most efficient and cost-effective system setup by evaluating technical and economic factors. HOMER's simulations, optimizations, and sensitivity analyses are crucial for balancing energy demand, resource availability, and operational cost reduction. In [90], the article explores the best configuration for a hybrid microgrid at a desalination facility in Sfax, Tunisia, by employing HOMER software for simulations and FAHP-PROMETHEE for evaluating multiple criteria. The result shows a dramatic 50.2% cut in energy cost for producing desalinated water, which illustrates hybrid microgrids' significant role in making desalination processes more sustainable and economically viable, especially in areas with inconsistent conventional energy supplies.
## 2 Automatic Segmentation Using U-Net
### Preparation of Training Data  
Before image acquisition, a contrast agent is administered to patient's blood vessels, enabling the vessels to be clearly seen in the obtained medical image, that is, the CTA. In this study, grayscale inverted CTA images were used as training data and we prepared label data via the manual segmentation of optic nerves and ICAs in a 3D slicer environment, with the labeling procedure conducted manually by surgeon [PERSON], one of the authors of this paper. Data label was conducted using wand effect (tolerance: 10, max pixels per click: 150, fill volume mode). Our dataset included the upper part of the head, based on cervical spine 4 and did not use images below the cervical spine 4. The grayscale inverted CTA images and label data were used as a pair of inputs for training, with each single input dataset having paired CTA images and labels, as shown in Figure 5.  
Training data were collected from 1218 slices of six patients. Because there were limited patient data, 11 augmentation data elements were generated for every piece of patient data, giving a total of 13,398 data. Data augmentation applied in this study included rotation ranges of between \(-0.2^{\circ}\) and \(0.2^{\circ}\), width shift changes ranging between \(-25\) and \(+25\) pixels, height shift changes ranging between \(-25\) and \(25\) pixels, shear angle changes (shear angle changes counter-clockwise, in degrees) ranging between \(0\)-\(5^{\circ}\) and zoom changes ranging between \(-5\%\) and \(5\%\) of the image size.
## 2 Materials and Methods
### Methods
#### Calculation of Ecosystem Service Value
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ecological Service** & **Land Use Type** & **Farmland** & **Woodland** & **Grassland** & **Watershed** & **Construction Land** & **Unutilized Land** \\ \hline \multirow{3}{*}{Providing service} & Food production & 1733.65 & 390.46 & 359.23 & 624.74 & 0 & 15.62 \\ \cline{2-8}  & Raw material production & 390.46 & 905.87 & 531.03 & 187.42 & 0 & 31.24 \\ \cline{2-8}  & Water supply & \(-\)2046.02 & 468.56 & 296.75 & 8168.48 & 0 & 15.62 \\ \hline \multirow{9}{*}{Regulating service} & Gas regulation & 1390.05 & 2983.13 & 1889.84 & 749.69 & 0 & 109.33 \\ \cline{2-8}  & Climate regulation & 734.07 & 8918.16 & 4982.3 & 2217.83 & 0 & 78.09 \\ \cline{2-8}  & Environmental purification & 218.66 & 2608.29 & 1639.94 & 4466.89 & \(-\)4263.85 & 327.99 \\ \cline{2-8}  & Hydrological regulation & 2342.78 & 5841.32 & 3654.73 & 85,417.58 & \(-\)13,088.3 & 187.42 \\ \cline{2-8}  & Soil conservation & 812.16 & 3623.49 & 2295.92 & 734.07 & 0 & 124.95 \\ \hline \multirow{2}{*}{Supporting service} & Nutrient cycling & 249.9 & 281.13 & 171.8 & 62.47 & 0 & 15.62 \\ \cline{2-8}  & Biodiversity & 265.51 & 3311.12 & 2092.88 & 1999.17 & 0 & 109.33 \\ \hline Cultural service & Aesthetic & 124.95 & 1452.52 & 921.49 & 1546.23 & 0 & 46.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The ESV coefficient of each ecosystem service function in Sichuan Province (CNY/hm\({}^{2}\)).
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 3 Deep Keypoint-Based Monocular SLAM
### Deep Keypoint Meta-Learning
#### 3.1.3. MAML-Based Visual Keypoint Meta-Learning  
Learning-based visual keypoint (local feature) extractors face poor generalization due to variances in visual features across scenes. Significant feature disparity between training and unseen datasets can lead to catastrophic forgetting. To address this, we leverage insights from Model-Agnostic Meta-Learning (MAML), adapting network parameters through meta-training, which enhances generalization to new scenarios without requiring additional training during deployment. The MAML-based meta-training consists of inner loop training and outer loop training. The training set is partitioned into a support set \(D_{s}\) and a query set \(D_{q}\), where \(D_{s}\) is involved in inner loop training, and \(D_{q}\) participates in outer loop training.  
In the MAML training strategy, the original network parameter \(\mathbf{\theta}_{a}\) is initially duplicated to \(\mathbf{\theta}_{b}\). Subsequently, both parameters undergo training in both the inner and outer loops. Within the inner loop, the support dataset \(D_{s}\) is utilized for iterative updates to \(\mathbf{\theta}_{a}\). This involves dividing a batch of support dataset into \(n\) distinct tasks \((D_{s}^{i},i=0...n-1)\), performing \(m\) parameter updates on each task, resulting in the updated parameter \(\mathbf{\theta}_{a}^{m}\). After completing one iteration of inner loop training, a minibatch of query set data \(D_{q}^{i}\) is used to update the \(\mathbf{\theta}_{b}\). The detailed steps of this feature meta-training strategy using MAML are shown in Algorithm 1 below.
# Intelligent Traffic Management in Next-Generation Networks
## 2 Machine Learning
### Conventional Machine Learning Models  
Although DL-based models perform well for complex applications, there still remain several challenges. For example, the number of their hyperparameters grows exponentially with the depth of the model. Additionally, finding suitable DL architecture (i.e., number of hidden layers) and identifying optimal hyperparameters (i.e., learning rate, loss function, etc.) are difficult tasks. Furthermore, DL does not perform well when the data volume is small because it needs a large amount of data to find some patterns from the data. In such a context, conventional ML models (e.g., decision tree) can provide better results with a minimal fine-tuning process of the hyperparameters. In addition, compared to the \begin{table}
\begin{tabular}{l l l l l} \hline
**DL Frameworks** & **Creator** & **Available Interface** & **Popularity** & **Released** \\ \hline Tensorflow & Google Brain Team & C++, Go, Java, JavaScript, Python, Swift & High & 2015 \\ \hline Caffe2 & Facebook AI research & C++, Python & Low & 2017 \\ \hline Deeplearning4j & Skymind & Java, Scala & Low & 2014 \\ \hline MCNet & Apache Software & C++, Python, Julia, Matlab, JavaScript, & Medium & 2015 \\ \hline Theano & University of Montreal & Python & Medium & 2017 \\ \hline CNTK & Microsoft Research & C++, C\#, Python & Low & 2016 \\ \hline PyTorch & Facebook AI research & C++, Python & High & 2016 \\ \hline Keras (higher level library for & & & \\ TensorFlow, CNTK, Theano, etc.) & [PERSON] & Python & High & 2015 \\ \hline \end{tabular}
\end{table} 
Table 5: Summary of popular deep learning frameworks [38; 39]. Figure 2: Conventional ML and DL. conventional ML models, DL has a higher computational cost, which makes it hard to be used on machines with a simple CPU. All this makes the conventional ML models still used. They can be broadly divided into two categories: (i) simple (i.e., single) machine models and (ii) ensemble models. The commonly used simple models include, for example, SVM, DT, and KNN, whereas ensemble models aim to combine heterogeneous or homogeneous simple models in order to obtain a model that outperforms every one of them and overcomes their limitations [42; 43]. The ensemble methodology imitates our nature to seek several opinions before making a decision [44]. Bagging, boosting, and stacked generalization (or simply stacking) are the most popular ensemble models. Similar to single models, there are no best ensemble methods, as some ensemble methods work better than others in certain conditions (e.g., data, features). Several conventional ML methods used with SDN are presented in Table 6.  
However, the performance of the conventional ML models depends on the amount and the quality of features. Moreover, extracting a large number of features from the coming flow in the SDN environment is time-consuming and, in turn, can decrease the QoS of the system [45]. To solve these issues, effective pre-processing mechanisms can be applied to filter out the unrelated/noisy data that can reduce the dimensionality of the data.