esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Polymer Flooding in Oil-Saturated Micromodel
This opposite ions' reaction resulted in the lower shear viscosity in porous media and hence produced the lower oil recovery. Lower oil recovery from SW+4S concludes that brine salinity significantly decreases in situ shear viscosity compared to hardness of the brine. Furthermore, pressure profiles of Figure 13 support the results of oil recovery factors. Added amount of sodium sulfate increased the polymer sensitivity to mechanical degradation in the flow lines, which resulted in a significant decrease of polymer shear viscosity entering into the micromodel. Increased degradation values and decreased polymer shear viscosity resulted in the lower pressure responses in the micromodel for SW+4S-PF. This lower pressure response directly influenced the oil mobilization and resulted in lower oil recovery for the SW+4S-PF. Figure 14 represents the initial fluid saturations (upper image) and final fluids saturations (lower image) in micromodel for both polymer floods. From the right bottom corners of both images in Figure 14, the main difference in oil recovery can be visually observed. These observations and the overall data previously presented let us assume that salinity has stronger impact compared to hardness (shown by many authors), keeping polymer mechanical degradation in focus, which in-turn is related to lower the shear viscosity, hence sweep efficiency. Synthetic seawater, B1, injection was performed as the base case, and to observe the reproducibility of the oil recovery data, synthetic seawater flooding was repeated in the cleaned model. The main flooding was a straight flow path between the production and injection points, shown in Figure 15, with almost the same oil recovery factors. Oil recovery and pressure data for synthetic seawater flooding is presented in Figure 16. Similar values are observed for both experiments looking at the pressure response and final RFs. Hence, micromodel reproducibility is confirmed within 2% of marginal error. Additional data and procedures can be seen on a previous study presented by the authors [3]. Figure 14: Image processing of oil recovery with oil initialization and after 10 PV injection, for both polymer solutions, at 22 \({}^{\circ}\)C.
# A Novel Crowdsourcing-Assisted 5G Wireless Signal Ranging Technique in MEC Architecture
## 4 Technical Advantages of Our Solution  
This section presents a 5G wireless signal ranging solution based on mobile edge computing (MEC) and crowdsourcing technology, aiming to overcome the challenges in efficiency, real-time performance, and adaptability faced by traditional signal ranging methods. By introducing 5G millimeter-wave technology, offloading computation tasks to MEC nodes, and utilizing crowdsourced data collection, we have achieved the following three major innovations:
# Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features
## 5 Experimental Results and Analysis
### Multimodal Results Analysis  
As shown in Table 4, multimodal models that integrate both textual and audio features show a clear performance improvement over traditional unimodal methods.  
This is because traditional unimodal models, such as text-based approaches like TextCNN and BERT, although capable of capturing syntactic and semantic structures effectively, often struggle to identify the hidden sarcastic cues embedded in audio signals.  
On the other hand, audio-only models like VGGISH can perceive tonal variations, but the lack of contextual information limits their ability to achieve strong results.  
In contrast, the excellent performance of our proposed MMCSNN model across all the evaluation metrics demonstrates that the key to sarcasm detection lies in cross-modal reasoning--specifically the analysis of semantic and acoustic shifts across modalities.  
To address this, we introduce the concept of audio attributes, improving the model's ability to detect tonal changes related to sarcasm and further enhancing the integration of multimodal features.  
In addition, the use of a dynamic multi-scale convolutional layer allows the model to perform deeper analysis under varying receptive fields, enabling it to detect more complex and globally distributed hidden cues.  
To further verify the model's performance, a confusion matrix is plotted based on the results on the test set, as shown in Figure 6. The model achieves balanced classification across both the sarcastic and non-sarcastic categories, with relatively low false positive and false negative rates. This result highlights the model's ability to handle subtle sarcasm cues while maintaining robustness against misclassifications.
## 3 Experimental Results and Analysis
### Indoor and Outdoor Loop Datasets
Finally, after walking and running, the tester went back to the start point by stairs (see subfigure **H**). Figure 13: Estimated trajectories of the different frameworks on the outdoor dataset. Subfigures **A–G** show different environments in this experiment. The yellow arrow in subfigure A refers to the start-end point, and the red arrows in subfigures **B–G** refer to moving directions. In this experiment, the test path included approximately 349 m (see subfigure **E**) of a straight line, and the tester walked through three parking lots (see subfigures **B**, **D** and **F**) and over several cast-iron well lids (see subfigure **C**).
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Aspects with Influence in PLC Systems Design  
The different aspects and characteristics of the grid impose constrains in the way PLC systems may be designed and implemented to the limit of their possibilities. The next sections cover the most important ones to be considered when one or more PLC technologies need to be chosen for a Smart Grid roll-out.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **High density residential area** & **Low density residential area** \\ \hline Type of SS & Underground or on ground & \\  & level inside a building & House-type or over a pole \\ Transformers per SS & 2 & 1 \\ Average number of customers per SS & 250–320 & 100 (10–200) \\ LV feeders per transformer & 6–8 & 6–8 \\ Average length of the LV lines (m) & 150 & 300 (100–800) \\ Type of LV line & Underground & Overhead \\ Customers per metering room & 10–25 & 1–4 \\ \hline \end{tabular}
\end{table}
Table 1: Typical data for electricity grids in Europe. SS: Secondary Substation; and LV: Low Voltage.
###### Abstract  
Solar induced fluorescence (SIF) is an ecological variable of interest to remote sensing retrievals, as it is directly related to vegetation composition and condition. FIREFLY (fluorescence imaging of red and far-red light yield) is a high performance spectrometer for estimating SIF. FIREFLY was flown in conjunction with NASA Goddard's lidar, hyperspectral, and thermal (G-LiHT) instrument package in 2017, as a technology demonstration for airborne retrievals of SIF. Attributes of FIREFLY relevant to SIF retrieval, including detector response and linearity; full-width at half maximum (FWHM); stray light; dark current; and shot noise were characterized with a combination of observations from [PERSON]'s laser for absolute measurement of radiance calibration facility; an integrating sphere; controlled acquisitions of known targets; in-flight acquisitions; and forward modelling. FWHM, stray light, and dark current were found to be of acceptable magnitude, and characterized to within acceptable limits for SIF retrieval. FIREFLY observations were found to represent oxygen absorption features, along with a large number of solar absorption features. Shot noise was acceptable for direct SIF retrievals at native resolution, but indirect SIF retrievals from absorption features would require spatial aggregation, or repeated observations of targets.  
fluorescence; spectroscopy; remote sensing 2020
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
###### Abstract  
In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time.  
fog computing; task scheduling; meta-heuristic algorithm Article
## 2 Literature Review
### Recognizing the Limitations of Generative AI in Education  
Although generative AI offers transformative potential for education by creating dynamic, learner-specific content, it also presents complexities and challenges that educators and institutions must address.  
1. Ensuring the Credibility of AI-Generated Content  
A key factor in the adoption of any educational technology is the precision of its output. Generative AI, while capable of producing diverse content, is not foolproof. Ensuring the reliability and validity of AI-generated content is crucial. For example, if a generative model proposes a solution to a maths problem, how can we verify its correctness and appropriateness for the educational context? [41]. Or, as in the case of research [42] where training data are generated to recognize mathematical formulas that have been handwritten. Another example would be the study [43] in which a model can solve university-level maths problems, explaining solutions with the ability to generate new questions.
2. Navigating ethical minefields: ownership, authorship, and accountability  
Generating content through AI raises significant ethical questions. Who owns AI-generated content, AI developers, users, or AI itself? The concept of authorship, which is essential for academic integrity, becomes blurred when content is produced by algorithms. Furthermore, determining the accountability for misinformation or biases present in AIgenerated content requires clear guidelines [44; 45]. Therefore, as underlined in article [46], to ethically use these technologies, it is important that the principles are concretized in a set of concrete practices for each organization.  
Charting the Path Forward: Collaborative Frameworks and Ethical Guidelines  
The relatively new presence of generative AI in education requires a comprehensive and collaborative approach to developing guidelines for its responsible use. Engaging a wide range of stakeholders, including educators, students, policymakers, and technology experts, can lead to the creation of a robust ethical framework. This framework should include the following:  
Clear definitions of roles and responsibilities for all parties involved;  
Strategies to ensure the quality, relevance, and originality of AI-generated content;  
Protections for intellectual property rights and academic integrity [47].  
Learning from the Vanguard: Best Practices in AI-Driven Education  
As generative AI gains traction in education, we are witnessing examples of its successful integration into platforms and curricula. Analyzing these early implementations can offer valuable information. Whether it is an online course that uses artificial intelligence for personalized study plans or a platform that generates study materials through generative models, understanding their strategies and ethical considerations can guide wider adoption [48; 49; 50].  
While the advent of generative AI in education presents exciting possibilities, a thoughtful, ethically grounded approach is essential to navigate the multifaceted challenges it presents.
## 5 Theoretical Case Study, Results, and Discussion
### Result and Discussion
Reducing maintenance costs, minimizing downtime, improving safety, and increasing overall efficiency are the listed advantages of TBM digital twins. Figure 13:0 Regression analysis based on the actual and predicted TBM values: (**a1**) comparative results of models for the prediction of penetration rate; (**a2**) comparative results of models for the prediction of the advance rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**PR**} & \multicolumn{3}{c}{**AR**} \\ \cline{2-7}  & **R\({}^{2}\)** & **RMSE** & **VAF** & **R\({}^{2}\)** & **RMSE** & **VAF** \\ \hline SVR & 0.960705 & 0.043628 & 0.960705 & 0.961474 & 0.017447 & 0.961782 \\ ANN & 0.969098 & 0.034308 & 0.969098 & 0.973454 & 0.012022 & 0.973659 \\ SVR-ANN & 0.9694 & 0.033973 & 0.969402 & 0.973602 & 0.011955 & 0.973843 \\ ANN-lbfgs & 0.969098 & 0.034308 & 0.969098 & 0.973454 & 0.012022 & 0.973659 \\ ANN-sgd & 0.953988 & 0.051084 & 0.954006 & 0.94675 & 0.024115 & 0.946851 \\ ANN-ADAM & 0.943473 & 0.062759 & 0.943655 & 0.939158 & 0.027553 & 0.939315 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance indices values for proposed algorithms.
## 3 Methods
### Electrode Attachment  
In this study, we improved the data collection process by capturing EMG signals from three extrinsic and one intrinsic muscle activity. In contrast, the previous experiment only captured two extrinsic forearm muscle activities [8]. The attachment locations of the electrodes were the extensor digitorum muscle (Channel 1), flexor digitorum muscle (Channel 2), extensor pollicis brevis muscle (Channel 3), and flexor pollicis brevis muscle (Channel 4). Channels 1-3 corresponded to extrinsic muscles, and channel 4 corresponded to intrinsic muscle [17]. The utilization of intrinsic muscle must be considered due to its potential to provide a prominent EMG signal of a specific hand finger. The muscle of channel 4 was selected due to its high relative importance in providing an EMG signal of pinch-related movement [18]. The locations of muscle attachment are shown in Figure 1.  
Prior to attaching the electrodes, an alcohol swab was applied to cleanse the skin. The electrodes were arranged so that the inter-electrode distance of an EMG channel was approximately 20 mm. Additionally, the electrodes were attached in a parallel orientation with respect to the muscle. These settings were utilized to obtain a good-quality EMG signal and minimize the occurrence of cross-talk [19]. The positions of the employed muscles were not adjacent to each other, avoiding cross-talk interaction between the employed muscles,as shown in Figure 1. In this study, the doctor guided the attachment of the electrode to find the best position concerning the target muscles. Hence, a good-quality EMG signal could be obtained. The subtle cross-talk interaction of the adjacent muscles with the employed muscle might have still occurred due to a muscle coordination event. However, given the anatomical positioning and functional roles of the employed muscles, this condition may be deemed negligible, as there is unlikely a cross-talk interaction among them [17; 18].
## 4 Experimental Results
### Comparison with DMKMSVM-2, CNN, RBF-SVM and NB  
In this subsection, the performance of DRKMSVM on short text classification is compared with that of DMKMSVM-2, CNN, RBF-SVM and NB, where DRKMSVM and CNN adopt word vectors as input, while RBF-SVM and NB use TF-IDF as input. The bidirectional structure is employed in DRKMSVM. To make a quantitative evaluation, we adopt 4 commonly employed valuation indexes in text classification: Accuracy [42], Precision [43], Recall [44] and F1-score [45]. The experimental results are shown in Table 7, with bold numbers representing the best results. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & \(\gamma\) & \(C\) \\ \hline MR & 0.1 & 8 \\ CR & 0.1 & 8 \\ MPQA & 0.1 & 8 \\ Subj & 0.01 & 64 \\ TREC & 0.01 & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Setting of parameters of RBF-SVM. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Number of hidden notes & 100 \\ Learning rate & 0.001 \\ Mini-batch & 64 \\ Dropout & 0.5 \\ C & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyper-parameter setting of DMKMSVM-2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyper-Parameter** & **Value** \\ \hline Number of hidden notes & 100 \\ Learning rate & 0.001 \\ Mini-batch & 64 \\ Dropout & 0.5 \\ C & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter settings of convolutional neural network (CNN). From Table 7 we can see that although for the dataset CR and Subj, DRKMSVM is not always the best. The overall performance of DRKMSVM is superior to the other four methods with the highest average value of four metrics on the five datasets. The detailed comparisons in terms of the four indexes are as follows:  
(1) In terms of the accuracy and the classification precision, we can see that for the dataset CR, the NB method achieves the best results, and for the dataset Subj, DMKMSVM-2 exceeds DRKMSVM slightly. However, for the other datasets MR, MPQA and TREC, DRKMSVM obtain the best results comparing with other four methods. (2) In the light of the recall rate, for the dataset Subj, DRKMSVM is slightly lower than that of DMKMSVM-2. However, DRKMSVM achieves the best results on the other four datasets (MR, CR, MPQA and TREC), and it is much higher than that of DMKMSVM-2. (3) In the light of \(F_{1}-\)score, for the dataset CR and Subj, DRKMSVM is lower than that of DMKMSVM-2. However, DRKMSVM achieves the best results on the other three datasets (MR, MPQA and TREC). Based on the above analysis, it can be claimed that DRKMSVM has better classification performance than DMKMSVM-2, CNN, SVM and NB in most cases. The comparison results reveal that employing recurrent neural network to extract context feature mapping in text is conductive to enhancing the performance of short text classification.
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes
### Cell-Laden Commercial Skin Templates
#### 3.1.5 Dermo-Epidermal Skin Equivalents (Composite Graft)  
Composite allografts containing both major skin layers (dermis and epidermis) thus resembling closely the natural human skin tissue structure and function [24]. Composite grafts that include allogenic dermis with autografted epidermal keratinocytes permit rapid re-epithelialization with a non-immunogenic cellular epidermis. One significant benefit of the composite grafts is their one-step application procedure in comparison with dermal substitutes [59; 60]. There are many bioengineered commercial composite skin grafts.  
Alloskin (AlloSource, Centennial, CO, USA) is a composite allograft made from cadaveric tissue. This product is indicated for different acute and chronic wounds [59; 25]. As Alloskin is applied only as temporary coverage, accordingly, no cellular DNA from the product was found in the wound 14-21 days post-implantation [49].  
OrCel (Ortec International, Inc., New York, NY, USA) is another composite allograft synthesized by culturing allogeneic neonatal keratinocytes and fibroblasts in type I bovine collagen porous sponge with nonporous sides. In this substitute, fibroblasts are cultured in a bovine type I collagen sponge that has a non-porous collagen-gel coating. Subsequently, keratinocytes are added on top to create an epidermal layer [61]. This bilayered substitute can produce an array of growth factors such as VEGF, PDGF, fibroblast growth factor-1, keratinocyte growth factor-1, and transforming growth factor-\(\alpha\), and other cytokines, which are all favorable for host cell proliferation, migration, and wound healing [61].  
Moreover, OrCel is an FDA-approved substitute for reconstructions in hand release surgery in epidermolysis bullosa patients and at graft donor sites, and its safety and efficacy have been evaluated in some clinical studies [56]. In a particular study of [PERSON] et al. [61], OrCel graft was employed to treat split-thickness donor sites in severely burned patients. Their findings demonstrated that OrCel was more effective in facilitating rapid wound closure of split-thickness skin donor sites with reduced scarring as compared to Biobrane-L (UDL Laboratories, Inc., Rockford, IL, USA) used as a standard dressing. Accordingly, OrCel can be used as a promising treatment option that promotes and accelerates skin healing and reduce scarring.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 5 Numerical Results
### Energy Consumption  
Figure 6 depicts the performance evaluation of SASC architecture with other solutions in terms of energy consumption under a varying number of nodes. Based on numerical results, it is observed that SASC decreases energy consumption by an average of 19% as compared to other solutions. On the other hand, the experimental results in Figure 7 demonstrate the improvement of energy consumption between SASC architecture and the existing solution. And the numerical analysis shows that SASC improves energy consumption by an average of 45% under varying CBR data traffics. The existing solutions cause additional overheads due to frequent route re-discoveries under the presence of malicious nodes and lead to unnecessary energy consumption. Moreover, the cluster heads are rotated on a fixed interval with considering the network status, such mechanisms deplete energy consumption between sensors in an unbalanced manner. The design of SASC architecture focuses on consistent and energy-efficient mechanisms for data forwarding and reducing needless energy consumption over the network field. Moreover, SASC helps to balance a load of energy consumption on the network nodes while forwarding the data on secure and authentic routing paths in the presence of potential security attacks. Additionally, the private-public keys are generated only once by each node and keep the public keys on the publicly accessible directory for global sharing, which greatly reduces the energy consumption in the process of keys management with nominal computational overheads. Moreover, to forward data packets from the cluster heads towards the BS, the proposed architecture exploits a light-weight XOR function between the data and key to produce a MAC or signature, which requires a nominal computational overhead.  
Figure 5: Packet drop ratio in varying CBR data traffic.