esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Setup  
Our proposed approach was evaluated by comparing its performance with eight state-of-the-art approaches for HSI feature extraction (Algorithm 2, see Section 3.4.5). These include SVM [7], Edge Preserving Filter (EPF) [26], superpixel-based classification via multiple kernels (SCMK) [34], region-based relaxed multiple kernel (R2 MK) [35], adjacent superpixel-based multiscale generalized spatial-spectral kernel (ASMGSSK) [36], Multiscale superpixel-based PCA (MsuperPCA) [37], 2D Singular Spectrum Analysis (2D-SSA) [29], and 2D Multiscale Singular Spectrum Analysis (2D-MSSA) [31]. A common way to measure the efficiency of feature extraction is through the accuracy of the classifier scored by the experiments. As a result, the classification setup must be appropriate with the current state-of-the-art. In light of this, SVMs have demonstrated themselves to be robust and efficient in multi-class classification applications.  
The LIBSVM toolbox [48] is used to implement SVM as the default classifier for all of the involved methods. A Gaussian RBF kernel is utilized for SVM implementation,  
Figure 8: (**a**) False-Color Composite Image, (**b**) Ground Truth Image, and (**c**) Class names for the Houston-2018 Dataset.  
Figure 7: (**a**) False Color Composite Image, (**b**) Ground Truth Image, and (**c**) Class names for the Salinas Dataset.  
and a grid search is applied to tune both key parameters of RBF-SVM; the penalty \(c\) and the gamma \(\gamma\). The SVM parameters are kept constant across all competitive experiments for a fair comparison. To avoid systematic errors and reduce random discrepancies, all experiments were independently carried out ten times each with different training and testing subsets, with no overlap between each training and the corresponding testing subset. This was intended to ensure good statistical significance for our experiments.  
Stratified sampling was used to randomly obtain the training and testing subsets. For training, 3%, 2%, 1%, and 0.2% samples per class were selected for the IP, PU, SAL, and Houston 2018 datasets, respectively. Additionally, four objective quality indices are utilized to evaluate image classification results: namely the OA, the average accuracy (AA), the kappa coefficient, and class-by-class accuracy. All experiments were conducted using MATLAB R2018b software, installed on a personal computer with an Intel core i5-6200 CPU clocked at 2.30 GHz, and 16 GB RAM.
## 3 Results and Discussion
### Effect of Treated Sludge on Soil Fertility, and Chemical and Microbiological Properties
More specifically, Zn was increased 2.7-7.4 and 5.1-11.6 times in treatments with treated or untreated sludge of the acid and alkaline soil, respectively, compared to the control. As far as B is concerned, its concentration in the acid soil treatments with sludge increased by up to 2.5 times relative to the control, whereas in the alkaline soil treatments B was at levels close to the control. Again, the highest increases for both elements and soils were observed in the air-dried sludge treatments (data not shown). Before the establishment of the pot experiment, microbial respiration was similar among treatments compared to the control for both soils, and the overall mean values were 0.20 \(\pm\) 0.19 and 0.26 \(\pm\) 0.13 mg CO\({}_{2}\) g\({}^{-1}\) d\({}^{-1}\) for the acid and alkaline soils, respectively. Similar results were obtained after the biological experiment and the respective overall mean values, much lower than the initial values, were 0.05 \(\pm\) 0.02 and 0.08 \(\pm\) 0.05 mg CO\({}_{2}\) g\({}^{-1}\) d\({}^{-1}\), for the acid and alkaline soils. As far as N\({}_{\rm mic}\) is concerned, it significantly increased after the application of treated or untreated sludge to acid soil (19.9-30.8 mg g\({}^{-1}\)) compared to the control (1.2 mg g\({}^{-1}\)), indicating the conversion of added and indigenous soil N into microbial biomass. However, this increase was not apparent after the pot experiment, probably because a large part of the labile nutrient sources was consumed by plants and microorganisms [44]. For the alkaline soil, N\({}_{\rm mic}\) only significantly increased in the treatment
## 5 Results and Discussion
### Fault Diagnosis Phase
#### 5.2.3 PCA-SOM Fault Diagnosis Analysis  
The PCA-SOM algorithm was used for fault diagnosis of large sample fault data sets, and the diagnosis results are shown in Figure 22. The fault diagnosis effect of PCA-SOM is very good, the fault types are accurately identified, and the data dimension reduction analysis is realized. At the same time, according to the pie chart, the component proportion of each cluster is close to 100%, and the classification boundary is clear. According to the confusion matrix of diagnosis results, the accuracy of fault diagnosis is 99.5%.  
Figure 21: Interval of fault occurrence.  
Figure 20: (**S3**) UMAP weight chart. (**S4**) Confusion matrix.  
Table 8 shows the detailed results of PCA-SOM processing of a large data sample, including time consumed and recognition accuracy. PCA-SOM is not limited by the size of the data sample due to its powerful reduction and feature enhancement functions, which shows the superiority of the PCA-SOM method.
## 5 Conclusions  
To investigate the proapoptotic mechanism of juglone, the role of reactive oxygen species (ROS) in juglone-induced apoptosis in HL-60 cells was investigated. Glutathione reduction was consistent with ROS production after treatment with juglone. It has been reported that juglone inhibits growth and causes human leukemia cell HL-60 apoptosis through a reactive oxygen species-dependent mechanism [26,27]. In our study, while no significant difference was observed in MDA and catalase levels, a significant difference was observed in SOD activity. In addition, the decrease in the negative effect of EF in the group given juglone was supported by our immunohistochemical and comet results. In studies investigating the apoptotic effect of SGC-7901 cells with different doses of 5, 10, 15 and 20 Mmol/L of juglone in gastric cancer, it was determined that the effect was higher at 20 Mmol/L doses compared to other doses. Juglone may have either pro- or antioxidant characteristics depending on the concentrations [28].  
In conclusion, 50 Hz EF created damage to the brain tissue. It was observed that the damage seen in the EF group was less in the EF juglone group. Degeneration in brain tissue was decreased with the antioxidant effects of juglone.  
**Author Contributions:** N.S. and E.K. conceived and planned the study. N.S., E.K. and R.A. drafted and revised the manuscript. N.S., E.K., R.A., O.C. and S.C. collected the data. N.S., E.K. and R.A. analyzed the data. All authors approved the final version of the manuscript for submission. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by grants from Suleyman Demirel University Scientific Research Project Unit of Turkey, contract no. 4550-M2-16.  
**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Ethics Committee of Suleyman Demirel University Animal Experiments Local Ethics Committee Chairman Turkey (protocol code 21438139-267 and 05.11.2015).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All the research data related to this manuscript will be available upon reasonable request from the corresponding authors.  
**Conflicts of Interest:** The authors declared no potential conflict of interest with respect to the research, authorship and/or publication of this article.
# A New Compound Staged Gelling Acid Fracturing Method for Ultra-Deep Horizontal Wells
## 2 Results and Discussion
### Optimal Fracture Parameters of Acid Fracturing for Horizontal Wells  
The optimization of fracture spacing is the key to the optimization of acid fracturing design [47; 48; 49]. Reasonable fracture spacing can reduce unnecessary packers and mutual interference between acid fracturing fractures. Reducing the fracture spacing is conducive to expanding the drainage area of gas wells, but the research shows that, when the fracture spacing is shortened to a certain extent, the productivity of gas wells will not be significantly improved, and there is an economic optimal fracture spacing for staged acid fracturing of horizontal wells [50; 51; 52; 53]. According to the reservoir porosity, permeability, gas saturation, and other physical parameters in the Sichuan Basin, we used Eclipse to establish the numerical simulation model of staged acid fracturing horizontal wells in carbonate gas reservoirs, and the relevant parameters selected in the model are shown in Table 1.  
It can be observed from Figure 4 that the gas production of horizontal wells increases significantly with the decrease in fracture spacing. However, when the fracture spacing is reduced to 50 m, the gas production of horizontal wells is only 5.3% higher than that when the fracture spacing is 70 m, and the gas production does not significantly increase. Therefore, the optimal fracture spacing for the segmented acid fracturing of horizontal wells in carbonate gas reservoirs in the Sichuan Basin is 70 m.  
\begin{table}
\begin{tabular}{c c c c} \hline Grid side length (m) & 0.1 & Reservoir permeability (mD) & 0.308 \\ \hline Model I-axis length (m) & 3000 & Gas saturation (\%) & 86.2 \\ \hline Model J-axis length (m) & 600 & Fracture conductivity (D-cm) & 14 \\ \hline Model K-axis length (m) & 12 & Rock compressibility & \(3.9\times 10^{-7}\) \\ \hline Reservoir porosity (\%) & 3.12 & Formation pressure (MPa) & 98.2 \\ \hline \end{tabular}
\end{table}
Table 1: Numerical simulation-model parameters.  
Figure 4: Productivity simulation under different fracture spacing.
## 2 Methodology
### Damage Detection Based on a Hypothesis Test  
Changes in the natural frequencies are primarily caused by varying temperatures but can also result from damage to the structure. Therefore, the results of the least squares approach can also be used to test for damage, provided that temperatures are measured, and an undamaged reference state of the bridge was previously observed. The reference measurements correct the relationship between temperature and frequency, ideally resulting in normally distributed frequencies. This correction is also applied to the test measurements to test for equality of the two distributions' means. A significantly different mean can indicate damage to the structure. Usually, the number of reference measurements will be much higher than the number of test measurements, resulting in unequal sample sizes. It can also not be assumed that the variances of the distributions are equal because damage might influence the variance of the natural frequencies. Therefore, [PERSON]'s \(t\)-test can be used to test for equal means. [PERSON]'s \(t\)-test defines the test statistic \(T\) with the distributions' means \(\bar{x_{i}}\) and the standard deviation \(s\):  
\[T=\frac{\bar{x}_{1}-\bar{x}_{2}}{s} \tag{8}\]  
The standard deviation \(s\) results from the distributions' standard deviations \(s_{i}\) and sample sizes \(n_{i}\):
## 2 Research Background
### RIS Relationship with 5G and Internet of Things (IoT)

Table 1: Technologies for communication based on Intelligent surfaces. modulation (SM) and space shift keying (SSK) for data transfer supported with RIS, to make these devices spectrally and energy-efficient in accordance with advanced 5G and 6G specifications. In terms of outage probability and bit error rate, performance examination of various schemes revealed that RIS-enabled SM and SSK, along with ABSc, outperform conventional communications. Designing a joint active and passive beamformer is investigated in [1,19]. In a multi-user downlink communication scheme, the authors focused on minimizing total transmit power at the BS by the RIS's phase shifts while placing SINR constraints on the users. Authors in [20] proposed a technique to improve the phase shifts at the RIS and beamformer at the AP while considering the highest possible spectral efficiency.
## 4 Conclusions  
Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) powders prepared by a hydrothermal method without a dispersant agent or mineralizer for use in UVA-attenuating applications have been investigated using DTA, XRD, SEM, TEM, ED and UV/VIS. The results are summarized as follows:  
1. When the zinc titanate precursor powders were prepared at pH = 7 and calcined at 600 \({}^{\circ}\)C for 1 h, the XRD results show that the phases of ZnO, anatase TiO\({}_{2}\) and Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) coexisted in the sample. However, when calcined at 900 \({}^{\circ}\)C for 1 h, the XRD result reveals the existence of Zn\({}_{2}\)TiO\({}_{4}\), rutile TiO\({}_{2}\), and ZnO.
2. The SEM results reveal significant agglomeration in both the freeze-dried and post-calcined samples.
3. The TEM and ED examination indicates the existence of near spherical Zn\({}_{2}\)Ti\({}_{3}\)O\({}_{8}\) crystallites with size of about 5 nm on larger ZnO particles with a length of 200 nm and width of 100 nm. The microstructure ZnTiO\({}_{3}\) shows a somewhat belt-shaped morphology, with a length of 200 nm and width of 50 nm for precipitates calcined at 900 \({}^{\circ}\)C for 1 h.
4. The calcined samples have an acceptable transmittance when the wavelength is 400 nm. This result indicates that zinc titanate precursor powders calcined at 700 \({}^{\circ}\)C for 1 h can be used as an UVA-attenuating agent.
# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 4 Experiments and Results
### Experiment 3-BiGRU-GA
#### 4.3.4 Model Parameters  
The experimental parameter settings are shown in Table 9:  
In order to verify the advanced nature of the BiGRU-GA relation extraction model in the field of fault text relation extraction, we selected the following classical entity recognition models for comparative experiments and analyzed the following models:  
1. BiLSTM [33] model: a BiLSTM network is used to capture two-way dependence, and then capture global features;
2. BiGRU [34] model: replaces the BiLSTM network with a BiGRU network which uses fewer parameters;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Training Set** & **Test Set** \\ \hline
3625 & 2537 & 1087 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Specific size of data set for relation extraction.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Relationship Type** & **Quantity** \\ \hline Cause of failure & 1627 \\ Fault phenomenon & 542 \\ Solutions & 1456 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Relationship type and quantity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Meaning** & **Parameter Value** \\ \hline
**batch\_size** & Number of samples per batch & 16 \\
**lr** & Initial learning rate & 0.001 \\
**num\_class** & Number of relationship categories & 3 \\
**num\_epoch** & Number of iterations & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Relation extraction experimental parameters.  
As can be seen from the experimental results in Figures 17 and 18, compared with the BiLSTM model, the accuracy, recall, and F-measure of this model were improved by 4.66%, 11.91%, and 12.55%, respectively. Compared with the BiGRU model, the accuracy, recall and F-measure of this model were improved by 1.78%, 0.64%, and 3.25% respectively. Compared with the BiLSTM Attention model, the accuracy, recall, and F-measure of this model were improved by 1.79%, 13.83%, and 0.30%, respectively, and the time overhead was reduced by about 16%. The results aptly demonstrate that the introduction of a two-way GRU neural network with fewer parameters and a Gated Attention Mechanism can capture local features and reduce the cost of training time, whilst also improving the effect of model recognition.  
Different dimension vectors generated by BERT model training also have a certain impact on the experimental results. This experiment will use GRU\({}_{\text{dim}}\) values of 100, 200, 300, and 400 for comparison. The comparison results are shown in Figure 19. It can be seen that the F1 value extracted by the entity is the best when the vector dimension is set to 300. When the dimension is low, the vector features are not complete enough, resulting in underfitting. When the dimension is too high, the noise generated in the experiment will be captured, resulting in overfitting.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Performance Comparison  
We compare the performance of the proposed scheme with the scheme proposed by [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40]. Apart from evaluating the malicious node detection rate, we compare the proposed scheme based on throughput, energy consumption, and end-to-end delay. Throughput reflects data integrity and routing success under trust-guided paths. Energy consumption is critical for evaluating resource efficiency, especially in battery-operated sensor nodes. End-to-end delay captures the responsiveness of the trust mechanism in facilitating timely communication. Together, these metrics provide a comprehensive assessment of both the trust system's security and its operational impact.
# Deduplication-Aware Healthcare Data Distribution in IoMT
## 2 Literature Review
It enables users to define a migration trigger value, denoted as T(0, 1), indicating the accepted deduplication level for this specific user. A certain portion of RAM is allocated for indexing prior to the deduplication procedure. The experimental results confirm that EAD enhances the system efficiency fourfold. This scheme illustrates 98% redundancy in the data while utilizing only 5% of the memory. The primary objective behind implementing backup storage is to reduce storage costs, lessen the transmission overhead, and optimize space utilization, mainly in the context of wireless multimedia networks. A frame separated by a comma, denoted as \(V=\{v_{1},\ v_{2},\dots,v_{n}\}\), is generated using the chunking algorithm derived from the sensory data. \(F^{t}\) is \(|F\cap F^{t}|\to k(k\leq l)\) and represents the newly modeled data; if it is similar/equal to \(F\), this shows that the majority of the components of the newly sampled information are steady. Assume that we have a collector node holding data, denoted as R, and the multimedia sensor identifies a chunk, denoted as \(F^{t}\), that bears similarity to a chunk present in R. The replicated content does not need to be switched; however, the indexes of the information may be disseminated. Additionally, the stable sequences ultimately lead to enhanced outcomes in terms of lessening the storage space and addressing the bandwidth needed in a network [19]. The secure deduplication assesses the data, identifying attributes, and if duplicate content is detected, then the cipher-text policy is applied. This approach helps to overcome unnecessary steps or redundancies, permitting smooth and resource-effective storage on the private cloud server. It ensures zero interaction-based key management using ElGamal. It includes a strong verification process that employs attribute keys. These keys ensure that only authorized entities obtain access to the data, which ensures access control [20]. The scheme makes use of prime chunking, which is considered effective for data deduplication within a cloud environment. OPC works by describing boundaries with the use of prime numbers, producing an exclusive and effective chunking strategy. The dynamic strategy of this scheme makes use of these prime chunks until the data chunk achieves an appropriate length. The average data chunk is created using Equation (1). \(T\) represents the selected prime digit, and \(L_{k}\) represents data chunking. \[Chunk_{average}=\sum_{l=0}^{T}l_{k}/T \tag{1}\]  
This scheme maintains a better chunk size along with reduced complexity, increasing the effectiveness with less storage. The scheme mainly depends on prime numbers for chunking, meaning that is not suitable for all types of data and can result in inappropriate chunk sizes [21].
## 3 The Proposed Data Integration Approach for Agricultural Open Data Platforms
### Physical Layer (PL)  
In the agriculture domain, IoT devices have been frequently used for gathering environmental and site-specific data such as soil moisture, soil salinity, soil pH and climate of the site (temperature, rainfall, wind, light, relative humidity). PL, which is the primary level of the proposed approach, comprises IoT devices formed as wireless sensor networks (WSN), which are structured as several routers controlled by one coordinator. Two variants of WSNs are generally used in agricultural applications: terrestrial WSNs and underground WSNs [45]. These agricultural WSNs applications can use different WSN standardizations such as ZigBee, WirelessHART and 6 LoWPAN. There is no restriction in terms of choosing WSN types and standards according to the proposed approach. Thus, any options can be used while establishing IoT devices on any agriculture field.
## 2 Methods
### Algorithm Design and Validation for Tunnel Blasting Evaluation
#### 2.2.2 Algorithm Validation  
To validate the algorithmic framework's performance, field experiments were conducted at the Huangtai Tunnel, part of the National Highway 109 project. The tunnel face was imaged following a controlled blast, and the images were processed using SfM, SIFT, and RANSAC to create a dense, high-resolution 3D model of the tunnel interior. Half porosity was then calculated from this model and compared with traditional manual measurements.  
The calculated half porosity from the algorithmic framework was 58.7%, closely matching the manual measurement of 60%. This minimal deviation--within 1.3%--demonstrates the algorithm's reliability, suggesting it can effectively serve as a practical and accurate alternative to traditional methods. By successfully adapting these computer vision techniques to tunnel blasting evaluation, this study illustrates how well-established algorithms from other domains can be repurposed to meet the specific challenges of a new field. The framework presented here provides a detailed and accurate means of assessing blast quality and tunnel integrity, offering significant benefits over traditional methods in terms of precision, efficiency, and real-time applicability. As such, this approach holds substantial promise for improving safety and operational effectiveness in tunnel construction projects.  
To provide a clear overview of this process, Figure 1 presents a flowchart that outlines each stage of the 3D reconstruction and evaluation workflow, from data acquisition to the final calculation of half porosity and assessment of blasting effectiveness. This chart visually represents the full workflow, highlighting the synergy of the individual algorithms--Structure-from-Motion (SfM), Scale-Invariant Feature Transform (SIFT), and Random Sample Consensus (RANSAC)--within the context of tunnel blasting evaluation.  
Figure 1: 3D Reconstruction Workflow for Tunnel Blasting Evaluation.