esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Power Consumption Evaluation of the Three DKF Algorithms: Scenario 1
#### 4.1.1 Results from Simulations  
We conducted the simulations on the two-node linear WSN presented in Section 3.2. For each of the selected DKF algorithms that were implemented on the two-node linear WSN, we ran the simulation for a period of 1 day (86,400 s). Figure 5 depicts the energy profile of sensor node S2 for all the three selected DKF implementations.  
From Figure 5, it is clear that EDKF has the lowest energy consumption, while ICF consumes more battery energy compared to the other DKF algorithms. We also observed that the energy consumption of ICF and SGG-ICF were close. This is because both of them were involved in the same number of consensus/gossip iterations. From the results displayed in Figure 5, we see at time t = 80,000 s, the battery energy of sensor node S2 has not crossed the 1.8 \(\times\) 10\({}^{4}\) J level for the EDKF implementation, whereas the battery energy of S2 had crossed the 1.8 \(\times\) 10\({}^{4}\) J level at time t = 10,000 s for both ICF and SGG-ICF. Summarily, the results revealed that for a period of 1 day (t = 86,400 s), sensor node S2 had exhausted 5.7%, 46.1%, and 47.1% of its total battery energy in the case of EDKF, SGG-ICF, and ICF implementations, respectively. Thus, we see that the energy consumption of SGG-ICF and ICF are over 8 times greater than the energy consumption of EDKF. The results revealed that EDKF is a more energy-efficient solution and will provide a longer WWPM lifetime when compared to ICF and SGG-ICF. Table 4 summarizes the battery energy usage of the sensor node for all three DKF implementations over a simulation period of 1 day.  
From the simulations, it shows that the maximum energy consumption for all three DKF implementations is 9000 J. Thus, the 3.7 V 2000 mAh Li-Po battery, which stores approximately 26,640 J of energy, is capable of supplying the nodes for more than a day of physical experimentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Battery Energy (J)**} \\ \cline{2-5}  & **Time (min)** & **0** & **1440** & **Battery Energy Usage (\%)** \\ \hline \multirow{3}{*}{**DKF Algorithm**} & EDKF & 19,160 & 18,069 & 5.7 \\ \cline{2-5}  & ICF & 19,160 & 10,133 & 47.1 \\ \cline{1-1} \cline{2-5}  & SGG-ICF & 19,160 & 10,321 & 46.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Battery energy consumption from simulations.  
Figure 5: Energy profile of the selected DKF algorithms over a simulation period of 1 day.  
The next sub-sub-section presents the results of the physical experiments that measured the power consumption of the selected DKF algorithms and a comparison of the results with those obtained from simulations.
# Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study
## 3 SDN-Based Cloud Resource Management in Network Performance Improvement
### QoS-Aware Resources Management  
While some algorithms consider priority as main challenge, others concentrate more on establishing QoS aware algorithms, such as the quality of service aware virilization-enabled routing (QVR) algorithm [16]. It is proposed for SDN environment where isolated slices of physical infrastructure are created, and each slice is dedicated for several tenants. It aims to dynamically support flow allocation and provision end-to-end QoS constraints fulfillment. Performance evaluation shows that QVR capable of reducing number of shared links while accomplishing lowest link congestion rates compared to baselines. In addition to that, EQVMP [17] is the VM placement algorithm, which aims to preserve low power consumptions and QoS-aware method. It combines three techniques to achieve desirable results. The input consists of VM resource demands, topology matrix and VM traffic. After that, EQVMP will reduce number of hops by maintain same number of VMs in multiple groups to reduce cost of traffic load. Then, the VMs are to be sorted in decreasing order based on total resources needed, then mapping VMs to suitable servers take place by selecting a VM, which is best fit to a specific server. In case there is not an ON-server able to accommodate the VM demands, then a new server will be woken up. Finally, load balancing feature in SDN controller is utilized to prevent link congestion by selecting substituted links in case of overutilization. EQVMP achieves nearly optimal results in power savings, however, average delay is high. Nevertheless, it succeeds on achieving outstanding throughput compared to other baselines and this is the reason this algorithm is located under network performance section.
# An Improved Algorithm of Drift Compensation for Olfactory Sensors
## 3 Materials and Methods  
### Maximum Average Discrepancy  
Maximum mean discrepancy (MMD) is a very efficient measure of the distance between two distributions. We mainly use it to compare the difference of target domain data and source domain data, and then find a new domain by minimizing MMD. The maximum mean discrepancy is based on the idea that we need to identify a function that takes distinct assumptions about two different distributions. By looking for a continuous function \(\mathcal{F}\) in the sample space, finding the mean of the function values of samples with different distributions on \(\mathcal{F}\), and by taking the difference of the two means, the average deviation of the two distributions corresponding to \(\mathcal{F}\) can be obtained. Find an \(\mathcal{F}\) such that the deviation has a maximum value, and the MMD is obtained. Finally, MMD is taken as the test statistic to judge whether the two distributions are the same. If this value is small enough, the two distributions are considered the same. At the same time, this value is also used to judge the similarity between the two distributions. In transfer learning, this \(\mathcal{F}\) is generally used as the RBF kernel function, and MMD can be expressed by Equation (1) [29].  
\[MMD^{2}(\mathcal{F},p,q):=\underset{f\in\mathcal{F}}{sup}E_{x\sim p}\|[f(x)]-E _{y\sim q}[f(y)]\| \tag{1}\]  
where \(\mathcal{F}\) is the desired function, \(x\), \(y\) is the sample of two random variables, \(p\) is the distribution of \(x\), and q is the distribution of \(y\). If and only if \(p=q\), \(MMD^{2}(\mathcal{F},p,q)=0\).  
For the unsupervised domain adaptation problem, two different domains are considered: the source domain \(S\) and the target domain \(T\), whose probability distributions are \(P_{S}\) and \(P_{T}\), respectively. The source domain data \(X_{S}=[x_{1},x_{2},\ldots,x_{S|}]\) and the source domain label \(Y_{S}=[y_{1},y_{2},\ldots,y_{S|}]\) and the unlabeled target domain data \(X_{T}=\left[x_{1},x_{2},\ldots,x_{T|}\right]\), where \(N_{S}\) and \(N_{T}\) are the number of samples in the source domain and the number of samples in the target domain, respectively. Generally speaking, the probability distributions \(P_{S}\) and \(P_{T}\) are different. The Euclidean distance between the source domain and the target domain after a specific function \(\varphi(\cdot)\) is mapped to the reproducing kernel Hilbert space (RKHS), as shown in Equation (2):  
\[MMD^{2}(X_{S},X_{T})=\|\frac{1}{N_{S}}\sum_{i=1}^{N_{S}}\varphi(x_{Si})-\frac{ 1}{N_{T}}\sum_{j=1}^{N_{T}}\varphi(x_{Tj})\|_{\mathcal{H}}^{2} \tag{2}\]
## 6 Results and Discussion
### Comparing Energy Efficiency  
We also assess the proposed multipath system's energy efficiency (bits-per-Joule). For tractability, we utilize two RF chains and two devices to provide a fair comparison. To standardize the energy efficiency figures, we use 802.11 ad MU-MIMO as a baseline. Figure 9 depicts the mean normalized energy efficiency. When compared to 802.11 ad, HEKATON, Multipath and BUSH achieve superior energy efficiency. This is because of their low power consumption, and all of them employed phased array to enhance energy efficiency. We observe in Figure 9 that Multipath outperforms BUSH, and BUSH outperforms HEKATON in terms of efficiency. This is because an optimum technique should execute multiple RF chains simultaneously while servicing one device. Multipath always employs the best strategy (two or more RFs where feasible); BUSH utilizes two RFs in some low SNR circumstances; however, HEKATON does not use this strategy.
## 4 Numerical Simulation
### Numerical Model and Boundary Conditions  
The section K51 + 590 to K51 + 640 of the Gudou Mountain Tunnel, replicating field geological conditions and dimensions, serves as the test section. The numerical model is depicted in Figure 10.  
The model was initially created and meshed in Midas; then, it was converted to FLAC\({}^{\text{3D}}\) using the MIDAS TO FLAC\({}^{\text{3D}}\) tool. The numerical model comprises 54,180 hybrid grid cells and 45,626 nodes. The model dimensions are 130 m \(\times\) 50 m \(\times\) 130 m, and the fault width is 25 m. In the model, blue represents the fault, green and red represent the strata, and purple and yellow represent the lining structures of the left and right tunnels. The mesh size for the tunnel and nearby soil layers is 1 to 1.5 m, while the mesh size for strata farther from the tunnel is 3 to 4 m. The geotechnical body is simulated using the Mohr-Coulomb criterion, while the lining structure is modeled using a solid  
Figure 10: Numerical model.  
element elastic model. The lining structure in the numerical simulation uses C30 concrete. According to relevant standard [27], its elastic modulus is 30 GPa, Poisson's ratio is 0.2, and the density is 2.5 \(\times\) 10\({}^{3}\) kg/m\({}^{3}\). The surrounding rock adopts the same physical and mechanical parameters as those at the site, as shown in Table 1.  
After establishing the numerical model, the bottom and side walls of the model are set as displacement boundaries, where no displacement components occur. However, the inner and outer surfaces of the lining structure are set as free surfaces, allowing deformation under the action of surrounding rock and water pressure. The bottom and side walls of the model are also impermeable boundaries, with no fluid exchange nodes with the external environment on these boundaries. Then, water level lines are set at different heights on the top of the model. The initial ground stress and initial water pressure distribution under the combined action of self-weight and water pressure are calculated when the tunnel is not excavated. When the initial ground stress and initial water pressure distribution reach equilibrium, the rock mass in the tunnel is set as empty units to simulate excavation. Simultaneously, the lining structure is assigned an elastic model to simulate the construction of the lining structure. Finally, the entire seepage process of the model is achieved by setting the pore water pressure on the outer surface of the lining structure to zero.  
In the tunnel fluid-solid coupling simulation, the fluid calculation mode involving pore water pressure uses the seepage mode, and the seepage model for the elements is selected as the isotropic seepage model. In the seepage mode, a fully coupled fluid-solid calculation is performed. Changes in pore water pressure cause mechanical deformation, while volumetric strain also induces changes in pore water pressure.
# Investigation on Self-Admitted Technical Debt in Open-Source Blockchain Projects
## 2 Background
### SATD Detection Strategies and Techniques  
Detecting the SATD essentially requires parsing the comments and classifying them based on the text content left by the developers. This operation can be performed manually and consists of associating each comment with a label that determines its type (for example, a specific category of SATD or the absence of SATD). However, manual classification is a time-consuming and skilled operation that is actually the basis for the creation of datasets used in classifier training. According to recent literature, two main techniques of Machine Learning are currently used for automatic SATD detection: Natural Language Processing (NLP)-based and Neural Network (NN)-based.  
NLP techniques can be used to automatically identify the SATD within code comments to speed up the SATD identification process, making it more efficient and accurate. NLP-based classifiers use NLP features and a training dataset of labeled samples to build up classification rules [24]. In ref. [11], the authors use a maximum entropy classifier (Stanford Classifier) to classify the comments of the code, on the basis of a dataset they created manually through the attribution of labels that represent the types of SATD they considered. Through the use of NLP, authors obtain significantly more accurate results than searches based on keywords or phrases. To improve the F1 score of training results, the authors in ref. [25] use N-gram inverse document frequency (IDF) as a feature for the classification of requirements and design SATD. In refs. [5; 22], authors adopt text-mining, and, in particular, use feature selection to improve performance in SATD detection.  
Neural network-based techniques for SATD detection consist of using NN processes for efficient extraction of information from text useful for SATD recognition. The use of Convolutional Neural Networks (CNNs) for feature extraction from texts was initially proposed by [PERSON] [26]. CNNs for SATD detection find application in ref. [27] for creating a trained CNN model that authors use for SATD pattern identification. In refs. [6; 28], the authors adopt an approach based on CNN for text via multitasking learning and focus on the detection of the SATD from different sources, including source code comments, and comments in commits, issues, and pull requests. Neural Networks based on Generative Adversarial Networks are used in ref. [29] to detect different typologies of SATD and,in particular, to solve the imbalance of sampled data (enhancing the features containing few number data). In ref. [30], authors adopt a deep learning approach to recommend how SATD should be removed, which involves the use of CNN for processing vectorized comments and Recurrent Neural Networks for processing the whole source code. Automatically Learning Patterns (Automatically Learning Patterns for Self-Admitted Technical Debt Removal)
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 2 Related Work
### Challenges and Gaps in Existing Research
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Approach** & **Achievements** \\ \hline Enhancement of micro-expression recognition through a lightweight MFE-Net using multi-scale features and attention [29]. & Demonstrated effectiveness of MFE-Net on CASME II and MEGC2019 datasets, improving feature extraction. \\ \hline Integration of GANs with CNNs to enhance micro-expression dataset diversity and classification accuracy [32]. & Outperformed existing CNN models with augmented dataset from CASME II, improving classification accuracy. \\ \hline Development of a multimodal large language model for facial expression and attribute understanding [33]. & Face-LLaVA outperformed existing models on multiple datasets, enhancing performance in facial processing tasks. \\ \hline Improvement of multi-class facial emotion identification using deep learning techniques [34]. & Achieved high accuracy, showing effectiveness of the combined use of EfficientNetB0 and WKELM. \\ \hline Utilization of advanced deep learning techniques for emotion recognition from visual and textual data [30]. & Demonstrated the ability to recognize seven primary human emotions effectively. \\ \hline A systematic review on emotion detection methodologies utilizing deep learning and computer vision [35]. & Identified trends in models used for emotion detection, emphasizing CNNs and their effectiveness. \\ \hline Proposal of a two-level feature fused two-stream network for improved micro-expression recognition [31]. & Achieved UAR exceeding 0.905 on CASME II and significant improvements on other datasets. \\ \hline Development of a method for magnifying subtle facial movements for better detection of micro-expressions [36]. & Introduced a technique that successfully enhanced the detection of micro-expressions in facial videos. \\ \hline Review of advances in facial emotion recognition using neural network models [37]. & Highlighted the dominance of CNN architectures in facial emotion recognition tasks with ongoing challenges. \\ \hline Evaluation of ChatGPT applications in psychiatry and potential for emotion recognition [38]. & Identified the potential for ChatGPT to enhance psychiatric care through emotion detection and automated systems. \\ \hline Development of a real-time approach for sign language recognition using pose estimation [39]. & Demonstrated effectiveness in recognizing signs with the aid of facial and body movements. \\ \hline Emotion detection in Hindi–English code-mixed text using deep learning [40]. & Achieved 83.21\% classification accuracy in detecting emotions in social media texts. \\ \hline Review of multimodal data integration in oncology using deep neural networks [41]. & Emphasized the importance of GNNs and Transformers for improving cancer diagnosis and treatment. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Emotion recognition milestones.
# Fuzzy MCDM Methodology for Analysis of Fibre Laser Cutting Process
## 4 Conclusions  
Considering the complexity of laser cutting technology and difficulties and limitations of traditional MCDM methods, this study proposed a fuzzy MCDM methodology for the analysis of the fibre laser cutting process, assessment of alternative cutting conditions and identification of favourable cutting conditions. Based on the conducted analyses and obtained results, the following conclusions may be drawn:  
* Laser cutting conditions in which a small cutting speed is used, or a combination of high cutting speed and low focus position, are less preferable with respect to meeting the considered criteria and associated significance levels.
* Generally consistent rankings of alternatives were obtained by the applied fuzzy MCDM methods, with highly positive associations.
* The conducted analysis, with respect to sensitivity of final ranks to the criteria weights changes, showed a high level of stability of individual solutions, even in cases of significant changes in the weighting coefficients.
* The development and statistical analysis of the mathematical model for the approximation of the fuzzy MCDM decision-making rule revealed statistically significant linear and quadratic effects of the cutting speed.
* The possibility to determine the resulting utility through the contour diagram for arbitrarily chosen input values is very important, considering that drastically different laser cutting conditions may achieve the same result. Moreover, through optimization, one can reveal the most preferable input parameter value combination.
* Unlike classical multi-objective optimization, based on the use of multiple models for each response, the applied approach lacks prediction of individual response values for a given combination of input parameter values, which makes it not very convenient for the selection of a particular solution in the presence of specific requirements and constraints.  
The proposed fuzzy MCDM methodology may be additionally adapted when solving specific problems so as to include limitations and exact specifications that must be met with respect to the considered criteria.  
Conceptualization, M.M., G.P. and M.T.; methodology, G.P., M.M., M.T. and D.M.; software, G.P. and M.M.; validation, M.M. and P.J.; formal analysis, M.T., M.M., G.P., D.M. and P.J.; investigation, M.M., P.J. and M.T.; resources, M.T., M.M., G.P., D.M. and P.J.; data curation, M.T., M.M. and G.P.; writing--original draft preparation, M.T., M.M., G.P., D.M. and P.J.; writing--review and editing, M.T., M.M., G.P., D.M. and P.J.; visualization, M.T., M.M. and G.P.; supervision, M.M. and G.P.; project administration, M.M. and G.P.; funding acquisition, D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Dataset available on request from the authors.  
This research was financially supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia (Contract No. 451-03-137/2025-03/200109).  
The authors declare no conflicts of interest.
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 4 SGXDump Attack Overview
### SGXDump  
As explained in Section 3, the typical role of the ROP payload is to escalate the privilege of the malicious code for running the shell or disabling the security enforcement (e.g., the SELinux or ASLR policy). Since the main task of these ROPs is to make system calls with proper parameters, its payload only needs several gadgets.  
However, the SGXDump ROP payload has a rather complicated task: it extracts the entire enclave's memory including code and data using the probe array's page table entry (PTE). To this end, SGXDump turns enclave memory values into virtual page numbers (VPNs) in the accessed PTE as follows. It reads one byte from enclave memory and binary-shifts the value to the left twelve times, multiplying by 4096 (i.e., the page size). Then, it adds the base address of the probe array to the value and uses it as an index to access the probe array. This access leads to the access bit in the probe array's page table entry (PTE) being set to one. Based on the index of the accessed probe array, the PAM can recover the value that SGXDump read from the enclave memory. To obtain the entire contents of the enclave's memory, SGXDump iterates this process until it reaches the end of the enclave's memory.  
Figure 2: The proposed SGXDump framework.
# Survey of Blockchain-Based Applications for IoT
## 10 Decentralized AI and IoT
### AI-Based Smart Contracts  
Traditional smart contracts in blockchains consist of a set of rules and conditions that trigger actions based on specific events. AI can be integrated into this logic for a more advanced decision-making process. This concept includes an off-chain component for model training. The resulting model is then inserted into the smart contract to make predictions on-chain [75].  
One application of AI-based smart contracts is in Goal-Oriented Semantic Networks in the 6G automotive sector, where blockchain technology enhances security and automation in Connected and Autonomous Vehicles (CAVs). AI-driven smart contracts enable vehicles to interpret real-time sensor data and execute autonomous decisions based on predefined objectives, such as traffic management, emergency response, and collision avoidance. By integrating semantic filtering and deep reinforcement learning (DRL), these contracts ensure that only meaningful, goal-relevant data are processed, significantly reducing communication overhead and improving efficiency. Furthermore, blockchain-based smart contracts enhance trust and data integrity by securing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) transactions, enabling seamless and reliable vehicular operations in the next-generation Intelligent Transport Systems (ITSs) [76].