esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Energy Use for Space Heating

Table 1: Total energy gains from the surrounding building into the test rooms when \(\overline{T}_{e}<\) 19.0 \({}^{\circ}\)C.  
Figure 12: Temperature in test rooms (A and B) and in the surrounding building. Reom B used 350.9 + 88.4 = 439.3 kWh  
Energy use per day for the rooms as well as average outdoor temperature is shown in Figure 13.  
If the facade of room B had instead been insulated with 100 mm EPS or mineral wool insulation, the energy use would undoubtedly have further improved, with better energy efficiency than the solution with hemp-lime. However, this would have also resulted in a facade with changed hygrothermal properties. Unlike hemp-lime, modern materials such as EPS and mineral wool are not diffusion-open, and therefore do not allow moisture flows through the wall. In time, this could have led to moisture damage to a wall renovated with these materials. The differences in energy use between rooms A and B seem to be greater at higher temperatures (see Table 2). In the table, the outdoor temperatures were chosen at approximately 5 \({}^{\circ}\)C intervals, starting at a daily mean temperature of approximately 15 \({}^{\circ}\)C on 26 September 2017. On this day, neither of the rooms required energy for heating. However, with a daily mean temperature of 9.7 \({}^{\circ}\)C on 5 October 2017, room B (0.7 kWh/day) only required 49% of the energy use for heating required by room A (1.4 kWh/day). At lower temperatures, the difference between the solutions is not as pronounced. On the 28 February 2018 with a daily mean temperature of \(-\)9.1 \({}^{\circ}\)C, room B (4.1 kWh/day) required 75% of the energy use for heating required by room A (5.5 kWh/day). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Date** & **Mean Outdoor Temperature [\({}^{\circ}\)C]** & **Room A [kWh/day]** & **Room B [kWh/day]** \\ \hline
2017-09-26 & 15.2 & 0.0 & 0.0 \\
2017-10-05 & 9.7 & 1.4 & 0.7 \\
2017-11-18 & 5.3 & 2.9 & 1.7 \\
2018-01-13 & 0.2 & 3.9 & 2.6 \\
2018-02-06 & \(-\)5.2 & 4.6 & 3.2 \\
2018-02-28 & \(-\)9.1 & 5.5 & 4.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Energy use in kWh/day per room at different mean outdoor daily temperatures. Figure 13: Energy use per day for room A (lime wall) and B (hemp-lime wall) as well as average daily outdoor temperature, from 15 September 2017 to 15 May 2018.
## 2 Background and Related Work on Routing in WMN-Based NAN  
WMN-based NAN refers to a number of household smart meters that establish a cooperative connection of the IEEE 802.11 standard network and are fully meshed or able to send and forward household traffic across multiple intermediate smart meters to a destination with the aid of routing protocols. Although the IEEE 802.15.4g wireless standard has made outstanding progress in NAN, plans to extend its capabilities to the area of NAN are being explored. However, there are still various problems, one of which is its limited data rates, which will not provide the required bandwidth capacity for NAN. This section discusses IEEE 802.11 standards that can operate in NAN based on WMN and surveys the routing protocols that have been modified for data transfer in the NAN.
## 5 Results and Discussion
### Feedback Collection  
The feedback generation process significantly impacts precision and accuracy as mentioned in Figure 9 by continuously refining the system based on learner input. Over 10 iterations, precision improved from 0.68 to 0.90, demonstrating the system's ability to reduce irrelevant or incorrect predictions by focusing on learner-specific needs and  
Figure 8: Content generation performance metrics.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Iteration** & **Precision** & **Recall** & **F1-Score** \\ \hline
6 & 0.82 & 0.86 & 0.84 \\
7 & 0.84 & 0.87 & 0.85 \\
8 & 0.86 & 0.88 & 0.87 \\
9 & 0.88 & 0.88 & 0.88 \\
10 & 0.90 & 0.89 & 0.90 \\ \hline \hline \end{tabular} Precision—improved consistently from 0.70 to 0.90, indicating a reduction in irrelevant or incorrect predictions. Recall—increased from 0.80 to 0.89, reflecting better identification of relevant data points across iterations. F1-score—gradually rose from 0.74 to 0.90, showcasing a balanced improvement in both precision and recall, reflecting overall system refinement.  
\end{table}
Table 17: _Cont._preferences. This indicates that feedback helps the model prioritize relevant features and pathways. Similarly, accuracy increased from 70% to 93%, reflecting the system's growing correctness in aligning predictions with valid learning paths. These improvements highlight how feedback generation dynamically adjusts model weights and content strategies, ensuring recommendations are both relevant and accurate, ultimately enhancing the learner experience.
## 2 Proposed Method  
### Define  
In order to facilitate better reader understanding, this section gives the definition of unsupervised domain adaptation as follows: we are given a source domain \(D_{s}=\{(x_{1}^{s},y_{1}^{s}),\cdots,(x_{n_{s}}^{s},y_{n_{s}}^{s}),\}\), containing \(n_{s}\) labeled samples (\(y_{i}^{s}\in\mathbb{R}^{c}\) is the one-hot vector of the label \(x_{i}^{s}\) and \(\mathbb{C}\) is the fault in the dataset number of categories), and a target domain \(D_{t}=\{x_{1}^{t},\cdots,x_{n_{t}}^{t}\}\), containing \(n_{t}\) unlabeled samples \(x_{i}^{t}\), where \(D_{s}\) and \(D_{t}\) obey the marginal probability distribution and \(P\) and \(Q\), respectively. We assume that \(D_{s}\) and \(D_{t}\) share the feature and label space \((X^{s}=X^{t},Y^{s}=Y^{t})\) and the marginal distribution \((P(x^{s})\
eq Q(x^{t}))\) and the conditional distribution \((P(y^{s}\mid x^{s})\
eq Q(y^{t}\mid x^{t}))\) are different between \(D_{s}\) and \(D_{t}\). The goal of unsupervised domain adaptation in this article is to learn a mapping \((P(y^{s}\mid x^{s})\
eq Q(y^{t}\mid x^{t}))\), which aligns the discrepancies in the distributions in different domains, so that the target loss \(L^{t}(f)=\mathbb{E}_{(x^{t},y^{t})\sim Q}[f(x^{t})\
eq y^{t}]\) can be bounded by the combined error of the source loss \(L^{s}(f)=\mathbb{E}_{(x^{s},y^{s})\sim P}[f(x^{s})\
eq y^{s}]\), distribution discrepancies \(L(P,Q)\), and ideal assumptions.  
The end-to-end architecture proposed in this article for RASAN-W is depicted in Figure 3. Subsequent sections will offer a thorough analysis of every component of the methodology. Moreover, the mathematical theory underlying each step of the methodology will be elucidated.  
Figure 2: Our method utilizes the category information of domain samples to align the global and local feature distributions, resulting in better interclass discrimination and intraclass compactness of sample features, reducing the differences in sample distribution between domains.
## 4 Empirical Analysis
### Analysis Overview
Service suitability consists of service performances/effects, competitiveness versus existing services, and market demand growth. Service feasibility consists of economic efficiency, field applicability, and operational sustainability. Figure 2 shows the final hierarchical structure reflecting these categories and criteria across classes.
## 6 Fault Detection and Classification Case Studies  
Cases are considered for study to validate the algorithm for identification and to classify the faults of a different nature, which include faults at different locations, the variable fault impedance, and the variable incidence angle of fault.
## 4 Results  
In order to identify suitable integration locations for the P2H element and to evaluate the potentials in cost reduction as well as the losses in round trip efficiency, simulation studies based on the concepts mentioned in Figure 1 are performed. For this purpose, the component models described in Section 3 are connected according to the conceptual schemes. Inside the calculation procedure, an iterative adaption of the mass flow rates is conducted to achieve constant turbine power and to fulfil the criterion of balanced air mass inside the cavern.  
On the basis of a set of central specifications listed in Table 1, variation studies are conducted related to thermal storage energy efficiencies (\(\eta_{\text{LIP-TES}}\), \(\eta_{\text{HP-TES}}\)), electrical heating power (P\({}_{\text{P2H}}\)), and low-pressure stage (p\({}_{\text{LP}}\)). Here, pressure losses are neglected for TES and heat exchangers but regarded for the cavern because of the high underground piping length. Ambient conditions (10 \({}^{\circ}\)C, 1 bar) are set for the air entering the investigated systems.  
The basic specification parameters inside the system simulations include the turbine power and the charging-discharging duration. In terms of operation durations, a nocturnal, low-price 6 h charging period is chosen, followed by a morning, high-price 6 h discharging period, with low share of renewable photovoltaic energies and high consumption needs. In order to investigate two-stage large-scale A-CAES configurations with underground caverns as mechanical energy storage systems, high turbine outlet powers of 65 MW and a maximum high pressure of 65 bar are selected. Comparable specifications, as summarized in Table 1, can be found in conventional CAES [8; 9] as well as in A-CAES power plants [45].  
Additionally, further parameters are required regarding the thermal storage systems. Here, mineral oil (Therminol VP-1) is considered as liquid medium for the high-pressure thermal energy storage, limited by maximum operation temperature of 400 \({}^{\circ}\)C. For the low-pressure thermal energy storage system, a ceramic packed bed with a particle diameter of 0.05 m, a void fraction of 40%, and a bed diameter of 10 m is selected. With these sets of specifications and the thermal models described in Section 3, the storage masses and the volumes are calculated, as well as the HP-TES heat exchanger size.  
In the following, simulation studies are conducted in order to identify cost reduction potentials and losses in round trip efficiency. Firstly, results are presented on the basis of the reference concept in order to evaluate the impact of thermal storage efficiencies on total round trip efficiency. Subsequently, the influence of the electrical heating power is investigated depending on its locations and the pressure level in the first stage, for the extended and alternative concepts. Finally, a comparison of the investigated concepts is presented.
## 3 Results and Discussion  
### Synthesis of AgNPs and UV Characterisation  
The reduction of AgNO\({}_{3}\) in leaf and stem extracts after 3 h of incubation at 80 \({}^{\circ}\)C was visually evident by color changes (brownish-yellow) in the reaction mixture (Figure 1). These changes in color indicate the production of AgNPs [7; 59] due to the reduction of Ag\({}^{+}\) to Ag\({}^{0}\) by numerous biomolecules that are present in the plant extract [60]. Our results showed that the brown color intensity increased with the duration of the incubation period. However, after 3 h, there were no visible color changes in various solutions. This may be due the consumption of the Ag\({}^{+}\) ions in the solutions and the excitation of the surface plasmon resonance (SPR) effect [68]. The optical phenomenon observed in metal NPs is a distinctive effect that can be readily observed under UV-vis light, originating from the surface plasmon oscillation of free electrons [69]. AgNPs synthesized from plant-based extracts exhibit strong changes in color after incubation [70]. An intense brown color was also observed in aqueous leaf extracts from _B. prionitis_[10].  
The highest percentage yield of the synthesized AgNPs from _B. albostellata_ was obtained from the powdered stems solution (0.50%), followed by 0.32% from the methanolic leaf AgNPs (Table 1). The lowest percentage yield was obtained from NPs from the fresh stem solution (0.10%). Overall, this suggests that the percentage yield of phytocompounds in _B. albostellata_ were greater in the leaf extract than the stem extract (Figure 1). NP yield can be affected by several factors, such as the type of material used, temperature, incubation time, amount of phytocompounds and metals present in the extracts used for the synthesis [16; 71]. AgNPs obtained from the various extracts were characterized by UV-vis spectroscopy (Figure 2) and major peaks were distributed along the absorption band of the SPR [68]. Characteristic absorption peaks were observed at 416 nm (methanol leaves), 402 nm (methanol stems), 400 nm (fresh leaves), 398 nm (fresh stems), 450 nm (powdered leaves), and 438 nm (powdered stems), respectively. In previous literature, AgNPs have exhibited a maximum absorption in the range of 400-450 nm [72; 73]. Plasmon bands are extended in Figure 2 with an absorption tail at longer wavelengths, which may be associated with the distribution and size of NPs [17]. [PERSON] et al. [2] have suggested broad plasmon bands found in a specific spectrophotometric range that may be linked to the occurrence of different metabolites in the synthesized AgNPs from the plant extract. AgNPs produced from the leaf and stem solutions had a maximum absorbance of 4.00 a.u. (Figure 2). AgNPs synthesized from _B. longiflora_ and _B. cristata_ leaf extracts displayed a maximum absorption at 443 nm and 449 nm, respectively [60], while synthesis using aqueous leaf extracts of _B. prionitis_, produced a maximum absorption at 420 nm [10].
# Using Fuzzy Control for Feed Rate Scheduling of Computer Numerical Control Machine Tools
## 2 Materials and Methods
### Fuzzy Controller for Dynamic Feed Rate Scheduling
#### 2.7.1 Fuzzy Rules  
A. Fuzzification and membership function  
Some common fuzzy membership functions include many different types, such as triangles, trapezoids, and Gaussians. In the past literature, it is pointed out that the use of the Gaussian membership function can obtain better experimental results. Compared with the triangular membership function, the use of the Gaussian membership function requires more computing resources and time. Therefore, in this study, we adopted the triangular membership function to define the input and output membership functions. The first input is the curvature membership function where ZO is the minimum curvature, VS is the very small curvature, S is the small curvature, M is the medium curvature, B is the large curvature, and VB is the maximum curvature. The other input is curvature variation where ZO is the very small change, L is a small change, M is a medium change, H is a large change, and VH is an extremely large change. For the output feed rate membership function, VLF is extremely slow speed, LF is slow speed, MF is medium speed, HF is high speed, VHF is very high speed, VVHF is extremely high speed, and TF is the highest speed. Figure 5 shows the membership functions of the curvature, curvature variation, and feed rate.  
Figure 4: Architecture of fuzzy controller for dynamic feed rate scheduling.  
When inputting the amount of curvature variation, it is first normalized to the interval \([0,1]\) based on the maximum and minimum values. After many experiments and adjustments, if the curvature variation remains greater than 3, the curvature variation is represented by the maximum value of 3 and is then normalized. The curvature does not need to be normalized and the output is the feed rate percentage. The current feed rate can be obtained by multiplying by the output with the maximum feed rate, and the user is able to adjust the maximum feed rate to meet the machining requirements.
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 1 Introduction
### Related Work
#### 1.2.2 Syscall-Based Attacks and Countermeasures  
Most studies monitor system calls by utilizing a combination of static and dynamic analyses. Dynamic analysis involves extracting system calls from an executable during its real-time execution. While effective, this technique has a significant disadvantage: it cannot identify all system calls because not all execution paths are covered during runtime, as it only captures the system calls made during the specific execution scenarios that are observed. To address this limitation, static analysis is employed. Static analysis examines the entire codebase without executing it, ensuring that all potential execution paths are covered. This comprehensive approach can identify all possible system calls within the program. However, static analysis comes with its own drawback: it tends to generate false positives, identifying system calls that might not actually be executed in a real-world scenario. Despite this, the combination of both static and dynamic analyses provides a more thorough and accurate tracking of system calls than either method used alone.  
Sysverify [17] uses an approach that merges static and dynamic analysis to minimize the attack area in cloud docker containers. The static analysis generates a connection path from the system calls to the library API, whereas the dynamic analysis creates a connection path for syscalls resulting from indirect calls or for the syscalls that are seldom used. The combination of the two connection paths will show information about the security level of the syscall's generation. Sitter [18] constructs security filters for kernel modules in Android platforms, which are used to reject syscall patterns that are strange, taking in consideration the order of execution, the arguments, and their length or if the system call is out of use. The Tailor toolkit [19] creates a system call whitelist for the Seccomp filter. The whitelist is created by combining two partial whitelists. The first one is created using three profilers which map the system calls originating either from functions, from the environment, or libraries. The second one is created by tracking system calls coming from assembly instructions or syscall functions, using static analysis. However, a fully automated machine learning-driven framework for detecting cryptomining activities is presented in [20], which utilizes syscall patterns to identify anomalous events in comparison to the normal algorithms used in cryptocurrency operations. Other filtering techniques are SysXCHG [21] which can adapt during execution, Nimos [22] which explores syscall sequences, and Optimus [23] which identifies the syscalls which are essential for containers, increasing their security.
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Metaheuristic System of Classifications
#### 2.6.3 Solution Search Strategy  
The solution search strategy dimension also reveals another vital aspect of the metaheuristics navigation system. It is also related to the way a metaheuristic covers the search space. However, in this case, it is associated with the number of solutions that are optimized in each iteration. Regarding this characteristic, the methods can be split into (1) trajectory-based (single solution) or (2) population-based (set of solutions) [2; 16; 65; 66; 34; 67].  
**Remark 3**.: _The term \"solution search strategy\" is defined in this study because it was pondered as the most appropriate term to arrange the definition for (1) trajectory-based (single solution) or (2) population-based._  
Figure 3: The most common dimensions of metaheuristics classifications found in the literature. * The Solution Manipulation Strategy requires a deeper explanation of how the metaheuristics can be subdivided. ** The source of inspiration has several different views in the literature. *** The same division found in the COP system of classification.  
The trajectory-based methods work by improving a single solution iteratively, moving from one solution to another, forming a trajectory (track). In contrast, population-based metaheuristics operate by improving a set of solutions (population) simultaneously. In other words, some operations are applied in each iteration, and the current population is replaced by a new one (with some improved solutions). A metaheuristic population-based can be further divided into single-population (Section 2.1) or multi-population (Section 2.2). Multi-population is generally used to increase the diversity of the solutions [68] and establish a parallel search through subpopulations that evolve together. Moreover, a multi-population search may require additional mechanisms and methods to manipulate individuals among populations.
# Increasing the Reliability of Flood Embarments with Neural Imaging Method
## 3 Research Problem and Objective  
The motivation behind this study is the fact that the currently used systems for monitoring of the state and performance of the embankment around the investigated tailings pond can only measure the dam parameters at specified points. Despite a large number of measuring points and the use of many  
Figure 3: Distribution of measurement and control equipment around the pond.  
Figure 2: Photographs of earth embankment: (**a**) the earth embankment around the tailings pond, (**b**) the measuring equipment.  
measurement methods, it is impossible to completely rule out the risk of dam failure. Although they store historical data in a comprehensive database, these systems do not predict an upcoming failure in advance so as to allow for taking effective measures to repair the embankment or even to evacuate people from flood risk areas.  
To design a suitable device for solving the above-mentioned problem, a modified electrical impedance (electrical resistance) tomography method was used [4; 19; 20]. The electrical resistance method consists in the analysis of propagation of generated electrical current in a body. The method is based on the determination of changes in electrical impedance (resistance) of the tested soil. The Wenner system was used in the study. With this method, you do not have to spread apart or change positions of the electrodes, which significantly reduces the time of measurements. Prior to measurements, relevant data regarding the spacing geometry, profile lengths and measurement parameters must be entered into the apparatus.  
A typical electrical impedance tomography image has a relatively low resolution. In order to eliminate well-known typical defects of electrical impedance tomography, a model of an intelligent monitoring system for flood embankments was developed. The key element of the model was an electrical impedance tomography device for the image reconstruction of the embankment, inside by means of a properly designed cluster of artificial neural networks. A unique feature of the proposed solution that distinguishes it from other, well known concepts is the fact that it generates a color value of each and every pixel of the output image using a separate neural network. The primary objective was to improve the resolution of tomographic reconstruction images and to increase the system's capability to detect various types of anomalies inside dams and flood embankments.