esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Further investigations of both methods or their combinations are needed to achieve the reliable results relevant for application in sustainable agriculture. The persistence of changes induced in plant growth by seed exposure to CP or other stressors implies that response to seed stress is manifested by alterations not only in seed germination and early growth but also in all following stages of plant development. It has been recognized that seed treatment with CP induces numerous adaptive changes observable in the growing plants (see the most recent reviews [23; 24; 25; 26; 27; 28; 29]). Seed treatment with CP results in chemical modifications in seed coat leading to increased surface wettability [55], increased seed electron paramagnetic resonance (EPR) signal [33; 56; 57], rapid changes in the balance of phytohormones [34; 35; 58; 59] and DNA methylation in dry seeds [60] resulting in changes of protein expression and enzymatic activities underlying the effects on germination. The systematic response of plants and seeds to EMF treatment has been reviewed recently [25]. Although EMF treatment did not affect seed coat structure [32], in dry seeds EMF increased EPR signal [33], induced changes in the balance of phytohormones [58; 59], and increased reactive oxygen species (ROS) production in germinating seeds [33]. Seed treatments with CP and EMF are further followed by changes in DNA methylation [61], protein expression and numerous enzymatic activities in tissues of growing seedlings [25; 34] including proteins that are important for photosynthetic activity and antioxidant defense. Substantial changes in plant associated microbiome due to sunflower seed treatment with CP have been reported [62]. Numerous findings indicate that secondary metabolism is activated in plants growing from the CP- or EMF-treated seeds [30; 36; 45; 63], and that may lead to increased plant fitness and improved growth due to stimulated communication with beneficial microorganisms [59; 64]. It remains to be determined which of these mechanisms are involved in the improvement of agricultural performance of common buckwheat and other plants. It has been suggested that plant adaptations can bring novel solutions for invigoration of agriculture and increasing crop productivity [65]. However, systematic and interdisciplinary knowledge on the detailed se quence of the internal molecular changes induced both in seeds and in plants is needed for the complete understanding of CP and EMF treatment effects and reliable their application in agriculture.
## 4 Experiments And Results
### Segmentation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **DS** & **FPC** & **mIoU (\%)** & **Dice** & **Recall** & **HD** & **Prediction Time(s)** \\ \hline
3D UNet [18] & x & x & 82.83 & 89.81 & 0.8904 & 9.5284 & 0.6292 \\
3D R2 UNet [41] & x & x & 82.93 & 90.23 & **0.9647** & 11.3500 & 1.6317 \\
3D UNet++ [42] & ✓ & x & 89.13 & 94.16 & 0.9453 & 8.0095 & 1.1544 \\ Point 3D UNet++ 1 & ✓ & x & 90.57 & 95.02 & 0.9504 & 7.2804 & 1.7031 \\
3D UNet3+ [40] & ✓ & x & 89.17 & 94.11 & 0.9534 & 7.2050 & 1.2444 \\ Attention 3D UNet [43] & x & x & 90.61 & 95.04 & 0.9462 & 7.1513 & 1.0590 \\ Attention 3D R2 UNet 2 & x & x & 88.39 & 93.78 & 0.9380 & 7.9479 & 1.8732 \\ Render 3D U-Net & x & x & 91.12 & 95.28 & 0.9451 & 8.0839 & 1.7767 \\ Render 3D U-Net & ✓ & x & 91.68 & 95.57 & 0.9635 & 7.3197 & 1.8511 \\ Render 3D U-Net & ✓ & ✓ & **92.56** & **96.12** & 0.9603 & **6.1453** & 1.8647 \\ \hline \hline \end{tabular}  
* Point 3D UNet++ is the integration of 3D UNet++ and the point-sampling method; 2 Attention 3D R2U-Net is the integration of 3D R2U-Net and Attention 3D U-Net.  
\end{table} 
Table 4: Heart segmentation performance results on the SegTHOR test dataset. Figure 10: Comparison of model outputs and ground truth, where the prediction-missed areas are highlighted with red arrows: the prediction of Render 3D U-Net is generated under the point-sampling method and false positive classification module. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **DS** & **FPC** & **mIoU (\%)** & **Dice** & **Recall** & **HD** & **Prediction Time(s)** \\ \hline
3D UNet [18] & x & x & 82.18 & 89.77 & 0.8527 & 7.2025 & 0.5602 \\
3D R2 UNet [41] & x & x & 84.24 & 91.22 & 0.8957 & 8.1131 & 1.6186 \\
3D UNet++ [42] & ✓ & x & 89.53 & 93.67 & 0.9539 & 6.1618 & 1.0727 \\ Point 3D UNet++ 1 & ✓ & x & 90.35 & 94.83 & 0.9498 & 5.4299 & 1.6401 \\
3D UNet3+ [40] & ✓ & x & 87.04 & 92.83 & 0.9118 & 6.0401 & 1.1923 \\ Attention 3D UNet [43] & x & x & 84.97 & 91.39 & 0.8807 & 6.0987 & 1.0432 \\ Attention 3D R2 UNet 2 & x & x & 88.69 & 93.19 & **0.9581** & 6.7743 & 2.2095 \\ Render 3D U-Net & x & x & 89.00 & 93.01 & 0.9304 & 5.4782 & 1.6269 \\ Render 3D U-Net & ✓ & x & 90.54 & 94.89 & 0.9510 & 5.0097 & 1.6819 \\ Render 3D U-Net & ✓ & ✓ & **92.16** & **95.89** & 0.9451 & **4.9487** & 1.6829 \\ \hline \hline \end{tabular}  
* Point 3D UNet++ is the integration of 3D UNet++ and the point-sampling method; 2 Attention 3D R2U-Net is the integration of 3D R2U-Net and Attention 3D U-Net.  
\end{table} 
Table 3: Kidney segmentation performance results on the KiTS test dataset. It is obvious that the proposed network outperformed other networks in the above three segmentation tasks. The comparison between Point UNet++ and UNet++ without point-sampling method proved that the introduction of point-sampling method can help Point UNet++ always perform better than UNet++. These comparisons strongly prove that the improvement is attributed to the proposed point-sampling method and false positive classification module.
# SoK: Delegated Security in the Internet of Things
## 2 Application Domains of Security Delegation  
In this paper, we conduct a review of 254 existing research works related to security delegation in the context of the IoT. The methodology used to identify and select these works is described in Appendix A.  
During the analysis of the selected works, it was observed that they focus on four application domains: access control and/or authorisation delegation, authentication delegation, rights delegation, and computation delegation. Within these domains, the proposed solutions exhibit similar architectural patterns, allowing us to identify generalised system architectures presented in Figure 2. These architectures and their illustrative examples are described in detail in this section.  
Figure 2: _Cont._  
The identified system architectures include the following parties:  
* _Resource-constrained (RC) System:_ This party wants to benefit from security delegation. It can be a single device (e.g., a light bulb, a smart vacuum cleaner, a sensor, etc.) or several networked IoT devices. It is assumed that the RC system does not have enough resources to perform some security functions on its own.
* _Delegate:_ A device that has sufficient resources to perform resource-intensive cryptographic operations, maintain necessary security and communication protocols, etc. The delegate performs these operations on behalf of the RC system.
* _Third Party:_ Any party that interacts with the RC system via the delegate is called a third party. For example, the RC system may want to access resources or services of the third party, or the third party requires access to the RC system.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials
### Feature Extraction  
Feature extraction is an essential step for detecting epilepsy attacks. It is used to establish epilepsy data using standard and collected epilepsy data. The wavelet transform is often used to decompose a signal into scaled and translated versions of a mother wavelet and a scaling function. The discrete wavelet transform (DWT) has been frequently used in epileptic seizure detection with promising results. Enhancing the precision of epilepsy detection heavily relies on improving the accuracy of feature extraction. The power spectrum, which represents the relative magnitude of frequency components comprising a signal, plays a vital role in this process. It is essential that the data utilized to determine the power spectrum adequately capture the signal's excitation levels to ensure reliable results. In this study, four important frequency and time features were extracted separately for epileptic and non-epileptic data in the alpha, gamma, and beta frequency bands: FMD, FMN, FR, and WL. These features were extracted from EEG recordings of stimulation with open and closed eyes. The extracted features are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Frequency Domain Features** & **Number** & **Time Domain Features** \\ \hline
1. & Frequency median (FMD) & 1 & Waveform length (WL) \\ \hline
2. & Frequency mean (FMN) & 2 & - \\ \hline
3. & Frequency rate (FR) & 3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extracted feature.  
In individuals with epilepsy, the median and mean frequency increase due to higher brain frequency and the frequency ratio decreases. The waveform length feature measures changes and calculates the absolute value of the next sample minus the previous sample. If the signal is smooth with little change, this property becomes zero. Therefore, in epilepsy, where the disorder entropy is low and brain oscillations are consistent, the waveform length decreases. Shannon entropy is used to calculate the entropy of wavelet transform which can measure the entropy of changes. Shannon's entropy can be defined as the average rate of information generation by a random source. The higher the Shannon entropy value, the greater and more information is given by a new value in the process. Shannon entropy is also used to estimate information. This criterion can be introduced as an important parameter for the distribution of the signal power spectrum which is used in epilepsy diagnosis.
# A Deep Learning Based Dislocation Detection Method for Cylindrical Crystal Growth Process
## 4 Discussion  
Some misdetection sample images are shown in Figure 11, the main reasons for misdetection can be concluded as follows:  
* Before the ingot grows to 300 mm length in the cylindrical growth process as shown in Figure 11a, it is basically not possible to perform visual inspection due to occlusion and view angle. Occlusion will cause the characteristics of the blocked and unblocked areas to be not obvious, thus the detection results of blocked areas cannot be determined;  
Figure 10: Inference results of the tracking process. (**a**) the first 1/4 cycle, (**b**–**d**) the next three 1/4 cycles, the habit lines marked with yellow box are not detected due to occlusion.  
* Unbalanced dataset, the habit line turns from dark black to light gray and white, the amount of such image data is small, the model is prone to error detection. As shown in Figure 11b, a normal habit line is predicted to be NG;
* Due to high temperature in the furnace and strong radiation, the image is blurred as illustrated in Figure 11c, the image effect of normal habit line appears to disappear, some camera parameters need to be adjusted;
* Dirty lens will cause dark spots in the image which will interfere with the detection as shown in Figure 11d;
* The light problem in the furnace will cause glare interference in the image as illustrated in Figure 11e;
* Diameter change, unstable diameter control will also cause interference. As shown in Figure 11f, the diameter of the ingot is smaller than the desired value, the amount of such image data is small, the disappeared habit line was predicted to be OK;
* When labelling, even the experienced operators cannot determine whether some habit lines disappear or not, there are mislabels in the data set.
## 1 Introduction
The Sentinel 2A satellite was successfully launched on 23 June 2015, as part of the European Copernicus program. Sentinel 2 carries an innovative wide-swath, high-resolution (up to 10 m for four spectral bands), multispectral imager (MSI) with 13 spectral bands; this is going to offer unprecedented perspectives on our land and vegetation [14]. Many studies have dealt with the use of Landsat in the study and monitoring of marshes and wetlands. [PERSON] et al. [15] used Landsat images in three different intervals: MSS in 1973, TM in 1990 and ETM in 2000. These were used in the change detection method and in calculating the normalized difference vegetation index (NDVI) to recognize the vegetation cover and determined as a percentage from the total coverage area of the marshes. The results have shown there are large changes that took place between 1973 and 2000 in land cover and land use. The barren land increased; while the water bodies decreased drastically. The work of [PERSON] et al. [16] deals with the spectral characteristics of five wetland types, including bog, fen, marsh, swamp, and shallow water, in a pilot site in Newfoundland were analyzed. This study used the data acquired by the two satellites, Sentinel-2A and Landsat-8. According to the analyses, the best spectral bands for wetlands' discrimination and classification were selected. Then, the optimum bands were inserted into an object-based Random Forest algorithm to classify wetlands in the study area. The overall classification accuracy was 84% with a Kappa Coefficient of 0.77. The objectives of this research are: (1) to extract NDVI values and using Landsat 2, Landsat 8 and Sentinel 2 satellites data of the marshes; (2) to use supervised classification to identify land and water surface classes to detect changes in the marshes during 1977-2017; (3) to determine the potential impacts of climate changes through the study of the annual average of the maximum temperature and the precipitation in different areas in the marshes for 1981-2016; (4) to compare between the results of Landsat 2 and Landsat 8 with Sentinel 2 to find the best tool to detect changes to the cover of land and water.
### A Method for F0 Range Estimation from a Brief Speech Input  
Unlike human listeners who are able to estimate the underlying pitch range of a speaker from a very brief speech, the traditional method of automatic estimation of the underlying F0 range in terms of a direct analysis of F0 profiles works only on a lengthy speech input from which the underlying F0 range can be well approximated by the surface F0 range (e.g., at least 14 s in English, according to [15]).  
Based on the previous finding that spectral features contributed to human listeners' pitch range perception, [14] proposed a method for automatic estimation of a speaker's F0 range from the spectral features of a very brief speech. The method used the long short-term memory (LSTM) technique to map a set of FBANKs (logarithmic mel-filter bank coefficients) from a brief speech to two parameters, i.e., mean and standard deviation of F0, corresponding respectively to the average height and the span of F0 variation as defined in previous studies [27]. Due to its high ability of online adaptive estimation, LSTM was chosen to mimic the adaptive process of human auditory perception--pitch range estimation gets more accurate when more speech is fed in. The duration of the speech input was encoded in the net depth of the LSTM model. Experiments showed that an accurate estimate could be obtained when the speech input was 300 ms long, and the accuracy did not increase significantly with a longer speech input [14]. Most importantly, the proposed model gave a more accurate estimate than the traditional LTD method in the condition of a brief speech input.
## 2 Materials and Methods
### In Vitro Permeability Studies--Franz Cells Apparatus  
The in vitro permeability profile of the five APIs incorporated into the cold cream or suspension was studied using a vertical Franz cell diffusion system. The cells consist of two compartments, a donor (upper chamber) and an acceptor (lower chamber), with a cellulose membrane (effective diffusion surface 4.9 cm\({}^{2}\), receptor volume 20 mL) located between them. The donor compartment was either uniformly coated with 250 mg of cream or with 250 \(\mu\)L of suspension, while the receiver was filled with 20 mL of degassed PBS pH 7.4. During the experiments, sink conditions were maintained under continuous agitation (90 rpm) at 37 \({}^{\circ}\)C. Experiments were repeated in triplicate for both cold creams and suspensions, while samples containing only the substrate (blank) were also performed. At regular time intervals (2, 3, 4, 6, 8 h), and within 8 h, a sample volume of 0.5 mL was taken from the receiver compartment and replaced with fresh pre-warmed PBS.  
After the samples were collected, they were lyophilized to completely remove the aqueous medium. Then, 400 \(\mu\)L of methanol was added and the samples were sonicated (15 min), placed in the freezer for 10 min and centrifuged. Two hundred microliters of supernatant was quantitatively transferred to a vial to which 200 \(\mu\)L of acetonitrile with 0.2% formic acid was added, mixed (vortexed for 2 min) and analyzed by HPLC. Quantification of the membrane residue after completion of the experiment was performed according to the formulation analysis procedure.  
The cumulative amount of permeated compound per unit area (\(\mu\)g/cm\({}^{2}\)) was plotted against time (h), and the steady-state flux (Jss) was calculated from the slope of the linear section of the plot. The apparent permeability coefficient (Papp) was derived by Equation (1).  
\[\mathrm{P_{app}}=\frac{\mathrm{J_{ss}}}{\mathrm{C_{d}}},\left(\mathrm{cm\cdot h ^{-1}}\right) \tag{1}\]  
where:  
Cd is the initial concentration of the drug in the donor compartment and  
Jss is the steady state flux.
## 3 Results and Discussion
### Clonogenic Assay Images and Colony Numbers after L-Methioninase and Etoposide Administration  
A colony formation assay was performed to evaluate the proliferative and clonogenic potential of U87 MG cells treated with etoposide, L-methionineinase, or their combination. The incubation of U87 MG cells with L-methionineinase resulted in a significant reduction in colony formation. After three weeks of incubation, the number of colonies formed by U87 MG cells decreased sharply (Figure 21).  
The observed decrease in colony numbers after incubation with L-methionineinase indicated that the enzyme effectively inhibited the clonogenic growth of U87 MG cells. This reduction in colony formation suggests that L-methioninease exerts a suppressive effect on the proliferative capacity and survival of these cells. The precise mechanisms by which L-methioninease inhibits colony formation in U87 MG cells might involve various factors, including the induction of cell cycle arrest, apoptosis, or the disruption of vital cellular processes that are necessary for colony growth. Colony number change in U87 MG cells under the effect of L-methioninease and etoposide and both administration was shown in Figure 22.  
Figure 21: Colonies of U87 MG cells at the end of 21 days. The number of colonies declined with L-methionineinase and etoposide administration. Colony numbers were significantly decreased with L-methioninease and etoposide administration. L-methioninease does not only show cytotoxicity but also inhibits their ability to form colonies of U87 MG cells.  
Figure 22: Colony number of U87 MG cells at the end of 21 days. Combined L-methioninease and Etoposide Administration enabled the lowest number of colonies (\(p\) < 0.001). L-methioninease is abbreviated as L-meth. Additionally, etoposide is etop.
## 4 Results
### GPCR Classification Performance without Virtual Class  
Table 3 shows the performance for the classification of GPCR using SVM at the family level. It can be observed from the results shown that at the family level, FAPSO is the  
Figure 5: Frequency of selection of each wavelet family at each node of the GPCR hierarchy.  
Figure 6: Frequency of selection of each decomposition level at each node of the GPCR hierarchy.  
best feature extraction strategy. The performance level achieved by FAPSO was 97.9% for accuracy, precision, and recall. This illustrates that both PseAAC and FAPSO can be used to classify GPCRs at the family level with high accuracy.  
Table 4 shows the performance for the classification of GPCR using SVM at the subfamily level. As seen from the table, the results show that at the subfamily level, PseAAC is the best feature extraction strategy at this hierarchy level. The performance levels achieved by PseAAC were 85.3%, 87.7%, 87.7%, and 0.887% for accuracy, precision, recall and F-score, respectively. This indicates that PseAAC is good for characterizing the GPCR at the subfamily level. Nevertheless, the FAPSO algorithm achieved better precision than PseAAC at 88.9%. It means that FAPSO produced a lower false positive rate by using DWT features compared to PseAAC.  
Table 5 displays the classification performance of GPCRs using SVM at the subfamily level. This table demonstrates that at the sub-subfamily level, PseAAC has the highest accuracy, at 76.9%. While FAPSO has the highest precision, recall, and F-score values, which are 87.5%, 81.2%, and 84.4%, respectively.  
Tables 4 and 5 demonstrate that the precision value for FAPSO classification results at the subfamily and sub-subfamily levels is greater than the accuracy value. This is indeed a good sign since high accuracy can also be attained by correctly identifying the dominating negative class. It has been shown that the GPCR protein has unbalanced data for each class [63, 77]. Therefore, it is more crucial to obtain a high precision value [78]. In addition, the accuracy value is not a suitable statistic for data sets with imbalance [78]. The recall value was higher than the accuracy value, as seen in Table 5. This suggests that FAPSO worked effectively despite the imbalance that existed in the GPCR protein dataset.  
From the observation, the performance of PseAAC and FAPSO decreased as the hierarchy level became deeper. This is because of the large number of classes (38 and 87, respectively) at the subfamily and sub-subfamily levels.
## 2 Materials and Methods
The largest contribution comes from the \"Others\" category, which aggregates countries with one to three publications each and accounts for approximately sixty-seven papers, more than half of the total dataset. Among the individually listed countries, the United States ranks first with 20 publications (17.9%), followed by Italy with 12 publications (10.7%) and China with 9 (8%). The United Kingdom contributed 4 publications, making it the least represented among the specified countries. The analysis indicates a growing research focus in these leading countries on integrating artificial intelligence into rehabilitation systems. The literature selection process involved sequential filtering at each stage, as illustrated in the PRISMA diagram in Figure 4. An initial search across authoritative scientific databases (including PubMed, Scopus, IEEE Xplore, Web of Science, and Springer) for the period of 2017-2025 yielded 180 publications. These publications spanned four key thematic areas: digital rehabilitation platforms, IoT sensor technologies for monitoring, AI/ML applications in rehabilitation, and LLM-powered virtual assistants for patient support. After the automatic removal of 12 duplicates, 168 unique records were retained for screening. At this stage, titles and abstracts were screened for thematic relevance, leading to the exclusion of 28 publications. Figure 3: Distribution of publications by country. Figure 2: Publication trend (2017–2025). Consequently, 140 articles were selected for full-text retrieval. Of these, full texts could not be accessed for six articles. The remaining 134 publications underwent a full-text evaluation against the inclusion criteria. Based on this evaluation, another 22 articles were excluded for the following reasons: out of scope (\(n=13\)), insufficient methodological detail (\(n=6\)), and unsupported language (\(n=3\)). This process resulted in a final selection of 112 scientific publications for inclusion in this review.