esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Robot Appearance  
Some studies have found that the appearance of robots can be a predictor, with humanoid robots being more suitable as co-workers in organizations [58,59]. Anthropomorphism is the human tendency to look for characteristics that make something look like a human [60,61]. According to [62], as the degree of anthropomorphism increases, it becomes necessary to match the robot's behavior to its appearance to avoid falling into the uncanny valley.  
Survey Question: If you had the chance to work with a robot colleague, which appearance would you prefer? Please slide the number that best suits you on this scale from 1 (machine-like) to 5 (human-like).  
From the data obtained from 546 participants, regardless of the country, the machine-human-like research robot, ADA7, falls outside the limits of the uncanny valley [62]. According to [63], in the uncanny valley area, people may never fully accept robots.  
The results regarding robot appearance preferences varied, as shown below:  
Category 1: 23.8% favored \"machine-like\" robots;  
Category 2: 28.0% preferred \"more machine-, less human-like\" robots;  
Category 3: 26.0% selected \"machine-human-like\" robots;  
Category 4: 8.8% selected \"less machine-, more human-like\" robots;  
Category 5: 13.4% preferred \"fully human-like\" robots.  
In the survey, 77.84% of people responded that they would rather co-work with \"machine-like robots\", \"more machine-, less human-like\" robots, and \"machine-human-like\" robots in an office environment. The responses given by all participants are shown graphically in Figure 4. The graph's polynomial curve looks like the uncanny valley curve. In our case, \"less machine-, more human-like\" robots fall into the valley, resulting in the lowest preference rate.  
Figure 4: Responses on robot co-worker’s appearance graph.  
These findings align with [PERSON]'s uncanny valley theory, which posits that robots with designs too close to human appearance can evoke unease or discomfort among users. Participants exhibited the lowest preference rates for robots that fell into the \"uncanny valley,\" such as less machine-like but not fully human-like designs. This suggests that while a degree of anthropomorphism is appreciated, it must be carefully balanced to avoid adverse reactions.  
Users with preferences for anthropomorphic robots (categories 4 and 5) prioritize sociability, likely due to expectations of meaningful and human-like interactions in office environments. Conversely, users who favor less anthropomorphic designs (categories 1 and 2) are influenced more by external validation, reflecting apprehensions about interacting with purely mechanical robots. Balanced designs (category 3) appeal broadly due to their perceived utility and sociability, striking a balance that avoids the discomfort of the \"uncanny valley\".  
By considering these appearance preferences and their psychological implications, this study provides actionable insights for developing robots that are both visually appealing and functionally effective in workplace settings.
# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
## 3 Main Architecture
### Prefetch Engine
#### 3.5.4 Adaptive Hyperparameter Setting for Gaussian Kernel  
In weight function (4) in Algorithm 3, \(y_{i}\) is the center of the kernel function. As a hyperparameter, \(k\) controls the radial range of the radial basis function, that is, the data width. However, due to the different amounts of data in each entry, the value of \(k\) may cause side effects. If the data width is too large, the drop gradient of weights will disappear in the entry with smaller offset differences. Meanwhile, if the data width is insufficient, the group with larger offset differences and more samples will have a high percentage of weights close to 0. Therefore, a fixed \(k\) value cannot be applied well to all entries. It is necessary to design an adaptive dynamic \(k\)-value algorithm to match all entries in different situations.  
We described the design and functionality of the history table in Section 3.4. As shown in Figure 4, each entry can cover a memory address space of \(2^{18}\). To determine the data width of the Gaussian kernel, we first calculate the difference between the highest offset and the lowest in the entry. Then we take the page size set by the system as the step size to calculate the preliminary data width. The amount of the address space covered by the sample is not the only factor that needs to be considered. The degree of linear correlation between the data is also a decisive factor affecting the weight distribution. The more obvious the linear features, the higher the confidence in the data acquisition weights. Therefore, we introduce the linear correlation coefficient (\(cc\)) as an activation function to dynamically adjust the weight assignment more finely. The detailed function is as follows:  
\[k=cc\frac{(y_{max}-y_{min})}{2 pageSize} \tag{5}\]
# SDN-OpenFlow Topology Discovery: An Overview of Performance Issues
## 2 Topology Discovery in SDN Networks
### Control Channel  
Each OpenFlow Logical SDN-Switch is connected to an OpenFlow SDN-Controller through the OpenFlow channel. The SDN-Controller installs and maintains the SDN-Switch using this link, collects events from the SDN-Switch, and transmits data from the SDN-Switch to the SDN-Controller. The SDN-Switch's control channel can handle a single OpenFlow channel with a single SDN-Controller or several OpenFlow channels with multiple SDN-Controllers that share SDN-Switch management. In addition, the communication connection between both the data stream and the OpenFlow channel is managed independently, but it must be liable to the OpenFlow SDN-Switching protocol rule. Moreover, the OpenFlow channel is normally performing over TCP and is secured using TLS, [58].  
Furthermore, if an SDN-Switch becomes connected to a group of SDN-Controllers, the SDN-Controllers status update should be sent to the SDN-Switch with only one SDN-Controller and the other SDN-Controllers in'standby' mode if the first controller stops. Additionally, the SDN-Switch should provide an improved Controller-Status indication for all SDN-Controllers when the OpenFlow channel is reconnected. Moreover, If the SDN-Switch loses connectivity to all SDN-Controllers for various reasons, including echo request timeouts, TLS session timeouts, or other disconnects, it should switch to \"Fail-Safe Mode\" or \"Fail-Standalone Mode\", depending on the SDN-Switch design and configuration. The only difference in the SDN-Switch's behavior in Fail-Safe Mode is that packets and messages addressed to the SDN-Controllers are dropped. In Fail-Safe Mode, flow entries should terminate according to their timeouts, while in \"Failure-Standalone\", the SDN-Switch uses the OFPP_NORMAL reserved port to process all packets. In other words, the SDN-Switch acts like an old Ethernet SDN-Switch or router. Additionally, the SDN-Switch can use flow tables in any way it wants when in \"Fail-Standalone\" and can delete, add, or edit any flow entry. By the way, only Hybrid SDN-Switches usually have a Fail-Standalone mode [58].  
Moreover, according to [66; 67], a slow control channel significantly reduces data layer throughput and response time in addition that it will threaten network availability. Therefore, several recent works have been proposed to help maintain OpenFlow availability.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
###### Abstract
Instead of selecting the image subregions by using prerequisite landmark objects information and human judgement as proposed in [25], this paper proposed a novel method for the meteorological visibility estimation based on image feature fusion, which can be able to find the effective image subregions through image pre-processing and gray-level averaging. This proposed method used deep learning neural network to extract features and established visibility evaluation models for each subregion through support vector machine (SVM). According to the results of the fusion analysis, the visibility estimates of subregions were fused together to obtain the final image visibility. Since effective subregions coordinates were already obtained by the preprocessing method, this method only performed feature extraction on the selected subregions, which would reduce the calculation time and increased the efficiency of visibility evaluation. A visibility estimation method with intelligent subregions selection, feature extraction and feature fusion is proposed in this paper. The step by step procedures of the proposed method are briefly described as follows. Firstly, the proposed algorithm performed the gray-weighted averaging or the image pre-processing on all the images in the database. Coordinates of the effective subregions were determined. After extracting the effective subregions, feature extraction was performed on the subregions. Deep learning neural network (VGG-16 network, VGG-19 network, DenseNet network, and ResNet_50 network) were then used to extract the subregions' features. Regression analysis model of each subregions was established through the support vector machine (SVM) and visibility estimates of subregions can be obtained. According to the results of fusion weight analysis, the visibility fusion was performed on all the subregions so as to obtain the final estimate of the visibility.
## 8 Conclusions  
This review synthesized recent progress in DL-based seizure prediction, with an emphasis on how different model architectures and feature representations affect the ability to distinguish between normal, preictal, and ictal EEG states. The analysis revealed that leveraging frequency-domain transformations and combining them with temporal features yield models that are more adept at capturing the complex dynamics associated with seizure onset.  
Rather than reiterating architectural superiority, this review underscored the importance of aligning feature representation strategies--particularly temporal and spectral domains--with the neurophysiological characteristics of seizures. The choice of domain not only influences predictive accuracy but also determines a model's generalizability and interpretability. DL models incorporating spectral-temporal features, especially those using STFT, wavelets, or hybrid CNN-Transformer mechanisms, have emerged as effective tools in capturing ictal transition dynamics.  
Despite these technical advances, key issues persist, including model overfitting to patient-specific patterns, insufficient cross-dataset robustness, and the interpretability of learned features. This review emphasized the need to rethink preictal window design, improve dataset standardization, and integrate explainable AI (XAI) methods that can map model outputs back to meaningful EEG biomarkers.  
Going forward, the convergence of transfer learning, federated learning, and edge computing will offer promising avenues to translate high-performing models into real-world clinical tools. Importantly, future efforts should not only aim for higher performance metrics but also prioritize transparency, adaptability, and clinical relevance. Achieving these goals will mark a significant step toward making seizure prediction systems both scientifically rigorous and practically deployable.  
Conceptualization, Y.A.S. and M.K.; methodology, Y.A.S.; software, Y.A.S.; validation, Y.A.S., M.K. and D.B.; formal analysis, Y.A.S.; investigation, Y.A.S.; resources, M.K.; data curation, Y.A.S.; writing--original draft preparation, Y.A.S.; writing--review and editing, M.K. and D.B.; visualization, Y.A.S.; supervision, M.K.; project administration, M.K. and D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Study on the Performance of Uranyl Sensor
#### 3.4.2 Sensor Stability and Repeatability  
The repeatability of the sensor was verified for different concentration UO\({}_{2}\)\({}^{2+}\) solutions by six continuous measurements using the same electrode MIIP/Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/MCPE, and the relative standard deviation (RSD) was 1.53%, 2.15%, and 2.58%, respectively (Figure S7A). After storing the prepared sensors for 30 days, the electrochemical response signal was 95.08% of the original value, indicating the stability of the modified electrodes (Figure S7B). The results indicated that the repeatability and stability of the current response during the whole test were better.
# Seismic Response of Shield Tunnel with Double-Layer Lining
## 1 Introduction
[24] investigated the seismic performance of single-layer linings, double-layer linings, and isolation layers under different combinations of loading conditions. Based on shake table tests, [PERSON] et al. [25] studied the influence of secondary lining configurations on the seismic response of tunnels, including a tunnel only with segmental linings, a tunnel with additional semi-ring secondary lining, and a tunnel with additional full-ring secondary lining. The aforementioned studies have mostly focused on the tunnel structure itself, with insufficient analysis of the relevant structural parameters that affect its seismic response. In conclusion, current research on the seismic response of shield tunnels predominantly focuses on single-layer linings. However, there are few studies on the seismic response of shield tunnels with secondary linings. Moreover, there is a lack of analysis on the structural characteristics of the parameters related to the shield tunnels with secondary linings. Therefore, this paper aims to investigate the seismic response characteristics of shield tunnels with overlapped double-layer linings and analyze the influence of segmental lining stiffness and burial depth on the seismic response of shield tunnels. The conclusions drawn from this study can provide valuable references for relevant engineering projects.
## 1 Introduction
We hereby propose the following novel ideas to distinguish between single and overlapping nuclei in CPE images using three main steps: (i) nuclei segmentation: extracting candidate nuclei using maximum entropy thresholding supplemented by preprocessing and refinement; (ii) feature extraction: extracting a new combination of nuclei features, 16 geometric features and 10 textural features; and (iii) classification: selecting the most relevant features and determining whether the nucleus is single or overlapping using a double-strategy random forest algorithm. The performance of the proposed method was assessed using six evaluation metrics namely sensitivity, specificity, precision, F1 score, accuracy, and G-mean on a local dataset containing 4000 nuclei. The experiment results were acquired in various ways. Firstly, the classification accuracy of using all features and selecting them by random forest was investigated and compared. Then, the accuracy of four alternative classifiers, namely naive NB, SVM, KNN, and DT, was further examined and compared with the results achieved by random forest. Third, the performance of previous studies was investigated and compared with the results achieved from the proposed method. In addition, the computation efficiency of nuclei segmentation, feature extraction, and classification was analyzed to prove the reliability and suitability of the proposed method for real-time use. This analysis demonstrates that the proposed method is relatively simple, computationally affordable, and yields promising results. Thus, it can serve as a feasible, reproducible and cost-effective tool in the development of an advanced system for diagnosing cancer cells in CPE images. The rest of this paper is divided into five sections. Section 2 presents the image acquisition and dataset description processes. Section 3 presents the methodology proposed in this study containing preprocessing, nuclei candidate extraction, post-processing, feature extraction, and classification. The experiment results and discussion are presented in Section 4. The conclusion is given in the last section, Section 5.
###### Abstract
The proposed IOS-aided C-HNOMA aims to enhance coverage and ensure reliable connectivity in congested networks, making it ideal for smart cities and surveillance systems. It also supports smart agriculture by maintaining internet of things (IoT) sensor connectivity in remote fields [2]. The improved coverage offered by this system facilitates seamless communication, while HNOMA reduces the complexity associated with massive connectivity, thereby boosting network efficiency. In urban environments, IOS-aided C-HNOMA can be utilized in smart streetlights and surveillance systems to enable uninterrupted data transmission. Additionally, HNOMA allows unmanned aerial vehicles (UAVs) to function as relays, providing coverage in disaster areas or remote locations lacking infrastructure [17; 20]. Thus, the proposed IOS-aided C-HNOMA has the potential to revolutionize IoT applications, smart cities, vehicle-to-everything (V2X) communication, and UAV functionality by improving spectral efficiency (SE), coverage, and energy efficiency [21]. It enables intelligent signal control for reliable connectivity in dynamic environments, positioning itself as a crucial enabler for future wireless networks.
## 1 Introduction
Furthermore, the lack of scalable communication architectures that can adapt to future technological advances, such as Beyond 5G networks, represents a significant limitation for long-term industry adoption. Based on the identified research gaps and the compelling need for technological advancement in aquaculture, this study aimed to develop an integrated smart ocean aquaculture monitoring system with four primary innovative contributions. First, it integrated self-developed remotely operated vehicles (ROVs), 4G mobile communication, multi-platform control interfaces, and cloud services to address existing deficiencies in autonomous control and monitoring system integration [19]. Second, it established communication architecturesthat are upgradeable to Beyond 5G/Non-Terrestrial Network (NTN) technologies, ensuring long-term technical compatibility [20, 21]. Third, it developed integrated control systems that encompass computers, smartphones, smartwatches, and multi-site control centers to enhance operational flexibility. Fourth, it achieved a good balance between technological advancement and economic feasibility through self-development and modular design approaches, specifically targeting resource-limited regions as emphasized by [PERSON] et al. [22]. This study used interdisciplinary approaches and multi-site field studies to validate the comprehensive solution across diverse aquaculture environments.
## 2 Materials and Methods
### EE2 Adsorption to the SBP Capsule Membrane  
In order to evaluate the EE2 affinity and adsorption to the SBP capsules, activated SBP capsules were autoclaved for sterilization prior to incubation with MSM solution supplemented with 0.4 mg/L EE2. The treatment group included five sterile SBP capsules, while the control contained the medium only, without any capsules. Samples for EE2 concentration determination were taken before the capsules' insertion and after 24 h. Both treatment and control experiments were conducted in triplicates. EE2 concentration was determined using the HPLC analytical method described below.  
Experimental Set-Up of EE2 Biodegradation by SBP-Encapsulated Bacteria in MSM Medium and MSM Enriched with 2% LB  
The experiment was conducted in triplicates in three 250 mL flasks containing 100 mL MSM solution and three 250 mL flasks containing MSM solution enriched with 2% LB medium. Each flask was supplemented with 0.5 mg/L EE2 and shaken for 2 h, ensuring complete dissolution of the EE2 within the medium. The initial concentration was based on previous published laboratory studies. The flasks were then inoculated with five SBP capsules of one bacterial strain (_R. zopfi_ or _P. putida_ F1) and shaken at 80 rpm and 30 \({}^{\circ}\)C. The duration of the biodegradation experiments was 48-72 h. Flasks containing sterile MSM, with and without 2% LB, supplemented with 0.5 mg/L EE2 with no capsules were used as controls (in triplicates). Samples were collected from each flask at different time intervals for analysis of the EE2 concentration using the HPLC analytical method (see below).
## 3 Material and Methods
### Circular Dichroism (CD) Spectrometry  
Far-UV-CD spectroscopy of protein samples were performed on a JASCO J-715 CD spectropolarimeter (Jasco, Gross-Umstadt, Germany) using quartz cuvettes (Helma, Muhlheim, Germany) with 1 mm path length. Spectra were recorded at protein concentrations of 20 \(\upmu\)M CDC42 WT and variants in 1 mM NaPi buffer, pH 7.0 or 8 \(\upmu\)M IQGAP WT and variants in 12.5 mM TRIS/HCl pH 7.4, 37.5 mM NaCl, 1.25 mM MgCl\({}_{2}\), at 22 \({}^{\circ}\)C with instrument settings as follows: 0.1 nm step size, 50 nm min\({}^{-1}\) scan speed, 1 nm  
Figure 5: Deviation in RAS-binding residues in GRDs cause lack of RASGAP activity. (**A**) Sequence alignment of human RASGAPs p120, neurofibromin (NF1) and the three IQGAP paralogs highlights distinctive deviations in three signature motifs (grey boxes): the finger loop, FLR region and a7/variable loop. RAS-binding residues are shown in red and conserved residues in blue. The catalytic arginine (green) is substituted by threonine in IQGAPs. The numbers correspond to the amino acids of the respective proteins. (**B**) GTP hydrolysis of various RAS family GTPases was measured in the absence (blue) and in the presence of p120 GAP domain (red; positive control, where no GTP detected) or GRD1 wt (orange) and GRD1T1046R (green). The GTP hydrolysis of the RAS proteins (10 \(\upmu\)M) was measured via HPLC and the GTP content normalized to 100% before adding p120 or GRD1, respectively, at 100 \(\upmu\)M concentrations and 1 min incubation time.  
band with. Signal-to-noise ratio was improved by accumulation of 10 scans per sample. The mean residue ellipticity \([\theta]_{\mathrm{mrw}}\) in \(\mathrm{deg\cdot cm^{2}\cdot dmol^{-1}}\) was calculated from the equation \([\theta]_{\mathrm{mrw}}=(\theta_{\mathrm{obs}}\times\mathrm{MRW})/(c\times d\times 10)\), with \(\theta_{\mathrm{obs}}\), observed ellipticity (in degrees); \(c\), concentration (in g/mL); \(d\), cell path length (in cm); MRW (mean residue weight), molecular weight divided by number of peptide bonds.
## 5 Simulations
### Simulation Results
#### 5.2.2 Case 2: LOS Scenarios with Gaussian Noises  
Case 2 is simulated using the same parameters as in Case 1, but with an added Gaussian noise. Two different scenarios are considered, one is using the standard deviation \(\sigma\) of the spatio-temporal Gaussian noise as 1 dBm and another uses 2 dBm. The results of the simulations are shown in Figure 9. In both of the plots of Figure 9, the theoretical optimum position without noise is \((x_{O},y_{O})=(0,0)\) (because in LOS, the \(\eta_{c}\) and \(\eta_{s}\) are same, and therefore, (0,0) is the least equidistant position from both the server and the client nodes). It can be observed that the relay node tries to reach that optimum position. Even though the success rate has dropped compared to Case 1 results due to the addition of noise, we still achieved more than a 75% success rate for the Case 2.