esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology  
In order to reach the best results, the experimental work was divided into two parts. The first part aims to find the optimal ratio to replace cement with lime in masonry mortar. To achieve this goal, five mortar mixtures (1:2.75) were prepared, containing 10% silica fume as an additive with replacement ratios of 0, 15, 25, 35, and 50% of lime as a partial replacement of cement. A total of 45 cubes (50 \(\times\) 50 mm) for compressive strength were tested at 7, 28, and 90 days according to ASTM C109/C109M [26]; 15 prisms  
Figure 1: XRD lime analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physical Properties** & **Result** & **Test Procedure Specification** \\ \hline Specific gravity & 2.41 & ASTM C188 [23] \\ Water absorption (\%) & 12.3 & ASTM C1585 [24] \\ Bulk density (kg/m\({}^{3}\)) & 1433 & ASTM C188 [23] \\ Fineness (\%) & 92.2 & ASTM C430 [25] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical properties of granite.  
(40 \(\times\) 40 \(\times\) 160 mm) for flexural strength at age 28 days according to ASTM C348 [27]; and 10 cylinders (100 \(\times\) 200 mm) for splitting strength at age 28 days according to ASTM C496/C496M [28]. Dry density and water absorption tests were carried out according to ASTM C642 [29]. The mix containing 25% lime as a partial replacement of cement was chosen as an economical mix as well as meeting the structural requirements of cement-lime mortar to be used as a masonry mortar.  
The second part included ten mixes: 90 cubes (50 \(\times\) 50 mm) according to ASTM C109/C109M, 30 prisms (40 \(\times\) 40 \(\times\) 160 mm) according to ASTM C348, and 30 cylinders (100 \(\times\) 200 mm) according to ASTM C496/C496M containing 25% lime as a partial replacement of cement, as well as different percentages of granite powder (10, 20, 30, 40, 50, 60, 70, 80, 90, and 100%) as a partial replacement for fine aggregate. The ideal combination was identified based on the findings of the physical and mechanical characteristics, featuring the most effective ratio of granite powder as a partial replacement for fine aggregate. Table 4 represents the mixing proportions of cement-lime mortar.  
Curing and testing parameters: Following the mixing process, all samples were enclosed in a plastic bag for a duration of 48 h. specimens were removed from molds and immersed in water at a temperature of 23 \(\pm\) 2 \({}^{\circ}\)C until the testing commenced. Testing occurred at 7, 28, and 90 days. This process aims to assess the mechanical and physical characteristics of mortar mixes to enhance the durability and performance of masonry mortar, using lime and granite powder as sustainable ingredients.
## 4 Results and Discussion
### Loaded Swelling Deformation  
#### 4.2.1 Effect of Initial Dry Density  
The relationships between the final swelling strains and initial dry densities of MB specimens under different vertical loads are shown in Figure 9. Under identical vertical pressure conditions, the final swelling strain of MB specimens increased with the initial dry density [42]. The greater is the vertical load, the smaller is the increase in swelling strain change with increasing initial dry density. The linear best-fit relationship between the swelling strain and initial dry density of MB specimens can be expressed by Equation (8):  
\[\varepsilon_{P}=a\rho_{d}-b \tag{8}\]  
Figure 8: Relationship between final unloaded swelling strain and corresponding initial dry density of MB specimens.  
where \(\varepsilon_{P}\) is the load swelling strain of an MB specimen under load \(P\); \(\rho_{d}\) is the initial dry density of an MB specimen (g/cm\({}^{3}\)); and \(a\) and \(b\) are linear fitting parameters related to the vertical pressure on the MB specimen (Table 5).
In the CIF, only the deterministic responses at the given sample points are required within the Chebyshev polynomial function, rather than any modification to the deterministic solution procedure. It not only boasts ease of implementation but also exhibits remarkable efficiency. Some literature exists regarding the applications of the CIF in engineering structural dynamic characteristics. [PERSON] et al. present an univariate Chebyshev polynomial method (UCM) for the interval bounds estimation of structures with bounded yet uncertain parameters, and a surrogate model composed of explicit univariate Chebyshev functions is established through the UCM [18]. [PERSON] et al. establish a nonlinear mathematical model of a rotor-shaft-bearing system of a hydroelectric generating unit under the action of interval uncertain parameters based on the Chebyshev polynomial and interval analysis theory, and they analyze the uncertain response characteristics of the system under single parameters, multiple parameters, and shock excitation [19]. [PERSON] et al. propose the Chebyshev convex method (CCM) to calculate the dynamic response of the Jeffcott rotor system with multiple uncertain parameters [20]. Currently, the research on the dynamic responses of arches with uncertain parameters in the literature is largely focused on the circular arch, meaning that the curvatures of the arches being analyzed are constant. The analysis of the dynamic response of variable curvature arch structures with uncertain parameters is scarce in the literature. Generally, the variable curvature arches predominate in practical engineering, thus it is necessary to identify a convenient and efficient model to obtain the dynamic responses of generally shaped arches with uncertain parameters. In this paper, a non-invasive polynomial surrogate model based on the Chebyshev approximation theory is employed to handle the interval dynamic response of constant and variable curvature arches with uncertain parameters under external excitation. First, the motion equations of the generally shaped arch are constructed, and the differential quadrature (DQ) method is employed to simplify these equations. Secondly, the deterministic dynamic response is computed via the Newmark-\(\beta\) method. On this basis, the uncertain dynamic response is derived using the polynomial surrogate method, and the discrepancies of the response interval among different rise span ratios are compared. This approach is straightforward and convenient, without any complicating theory or learning mechanism. The remainder of this paper is organized as follows. The model of the variable curvature arch is described, the equations of motion are established, the DQ method is introduced to simplify the equations and the Newmark-\(\beta\) method is employed to obtain the deterministic dynamic responses in Section 2. Based on the previous derivations, the Chebyshev orthogonal polynomial approximation is used to obtain the interval solution of free vibration characteristics in Section 3. Some numerical examples are presented and several discussions are raised in Section 4. In Section 5, the conclusions are summarized.
## 6 Performance Evaluation
### Simulation Results
#### 6.4.3 Sensor Network Density Test (Third Simulated Scenario)  
In the third scenario, we evaluated the impact of increasing the network density from 100 to 1000 in step of 100 nodes with 40 nodes designated as the source communicating nodes. The results of the simulation are illustrated in Figure 6a-e. As can be observed in the figures, increasing the network traffics and density causes too much traffic oscillations in both SSR and SHR, and decreases the success rate in the network, particularly between 100 to 300 node network sizes. However, at higher network density, the success rate of the delivered packets becomes stable, with SHR showing slightly higher packet success rate compared with SSR. Our ASAART protocol maintains higher packet success rates at different network densities. In addition, the time required to converge to minimal routing path is minimal, which makes the end-to-end delay lower than for SHR and SSR. At the higher network density, SHR spends too much time on topology maintenance, which causes its performance to reduce drastically compared with the ASAART technique. Despite the higher packet success rate and lower end-to-end delay exhibited by ASAART, it also uses minimum energy and reduced MAC layer packet delivery compared with SHR and SSR protocols. In Figure 6e, we can observe that PER for ASAART protocol is lower with a network of size between 200 to 1000 nodes compared with the SSR and SHR protocols. The SHR protocol competes against SSR by showing a slightly higher packet delivery ratio at higher network densities particularly between 400 to 1000 nodes.  
Figure 5: Sensor network density test (second simulated scenario) with source node = 20: (**a**) Average packet delivery rate (success rate); (**b**) End-to-end delay; (**c**) Energy consumption; (**d**) Transmitted MAC layer packets; (**e**) Packets error rate.  
**Figure 6**.: _Cont._It is interesting to observe that when the network is scaled from 400 to 1000 nodes, PER becomes stable for the ASAART routing protocol. In conclusion, the ASAART protocol proves to be resilient against errors, and energy consumption and offers better routing performance compared with the SHR and SSR protocols in a scalable and high traffic sensor network.
## 4 Discussion of FE Results  
### Force-Slip Curves  
Generally, the FE modelling strategy herein presented proved to offer relatively good correlation with the selected literature data, both in quantitative and qualitative terms. For few configurations (especially for the TCC joints characterized by high \(\alpha\) values), a major scatter was observed and justified by local damage phenomena that compromised the overall load-bearing performance of the FE assembled components.  
Selected examples are shown in Figure 8 for S#1 specimens and different STS inclinations, while the corresponding test results are derived from [6].  
The typical PO analysis was thus stopped due to convergence issues, in the very late damaged stage. In most of the cases, see Figure 8, this ultimate collapse configuration was achieved for relatively small slip amplitudes (\(s<10\) mm), compared to the imposed displacemen  
Figure 8: Force-slip curves of selected TTC joints with inclined STSs under a standard PO setup (ABAQUS/Explicit, S#1, \(\alpha=\) var) and corresponding experimental results (data from [6]).  
Figure 7: Monitored force contributions for the mechanical analysis and characterization of TTC joints with inclined STSs under a standard PO setup.  
predictions agreeing with Figure 8, the maximum resistance \(F_{max}\) can be conventionally detected as the first condition between the attainment of the (a) actual maximum force or (b) a total force corresponding to a joint slip \(s=15\) mm (if any). The corresponding serviceability stiffness \(K_{ser}\) is then given by [22; 23]:  
\[K_{ser}=\frac{0.4\ F_{max}}{\frac{4}{3}(s_{04}-s_{01})} \tag{4}\]  
with \(s_{04}\) and \(s_{01}\) the measured sliding amplitudes at the 40% and 10% part of the maximum resistance \(F_{max}\).
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 2 Literature Review  
A Chaotic Compressive Sensing (CCS) algorithm was proposed in [10] to solve both problems of energy saving and data security. A combined compression algorithm [4] provided different biomedical signals that showed a significant improvement in the compression ratio with a small maximum error for optimizing the network resource usage over Wireless Sensor Networks (WSN). A lossy compression algorithm based on online dictionaries provided a quantitative assessment for compression, reconstruction and energy consumption of wearable Internet of Things (IoT) was presented in [11].  
Several varieties of EEG compression algorithms have been proposed in literature. [PERSON] et al. [12] proposed a high performance wavelet-based EEG compression while [PERSON] et al. [13] presented an adaptive error modeling scheme. [PERSON] et al. [14] presented an excellent algorithm for EEG signals in high dimensionality. [PERSON] et al. proposed a compression method for multichannel EEG data with improved storage capacity and efficiency in transmission. A lossless and near-lossless algorithm of EEG compression was presented in [15]. The literature proposed two modes of compression for different platform with individual capability. An algorithm for classifying multichannel EEG data through a promising tool called the fuzzy multichannel EEG classifier was proposed in [16]. This supports different EEG signals that were collected at different time instants.  
The previously proposed algorithms were able to attain a high performance in terms of both compression rate and complexity in their VLSI architecture. [PERSON] et al. [17] proposed a VLSI implementation of a mixed bio-signal lossless data compression together with its EEG signal compression. The said work presented a detailed figure of merits for the different compression methodologies.  
On the other hand, several compression techniques were also proposed for electrocardiography (ECG) signals. [PERSON] et al. [18] developed a slope prediction based algorithm while [PERSON] et al. [19] proposed a novel adaptive trending prediction with Huffman coding architecture that was extended by implementing a fuzzy decision technique for a more efficient and precise signal prediction [20]. These 3 previously proposed methods in literature provided a reliable reference for EEG compression in VLSI implementation. Recently, an adaptive resolution control [21] and a fuzzy resolution control [22]techniques for body signals have been proposed. Although these variable resolution control techniques can improve the compression rate efficiently, there is significant information that was lost in the body signal.
# Advanced Noncoherent Detection in Massive MIMO Systems via Digital Beamspace Preprocessing
## 3 Numerical Results
### Influence of Propagation Channel  
In the following, the influence of the propagation channel is analyzed. To this purpose, the dominance of the LOS is varied by means of the LMR and the spatial spread \(\sigma_{\text{s}}\) of the multi-path components is modified. The analysis is restricted to the full-array architecture (observations apply to the sub-array in the same manner). The stronger the LOS component compared to the multi-path components, the narrower are the individual PSPs enhancing the angular user separation for both conventional beamforming and the combination with eigenspace-based processing due to lower inter-user interference (see Figure 7). At the same time, higher channel correlations deteriorate the overall performance. Increasing the spatial spread \(\sigma_{\text{s}}\), the multi-path components become more relevant and cause a broadening of the PSPs in case of conventional beamforming. Consequently, a higher inter-user interference occur and the user separability is worse in these scenarios (see Figure 8). On the contrary, additional eigenspace-based processing is insensitive to higher spatial spreads regarding the user separation and exhibits a performance improvement due to lower channel correlations.  
In summary, noncoherent detection with beamspace preprocessing performs best when channel correlations are low, i.e., multi-path propagation is prevailing and the multi-path components impinge at a large angular spread at the BS. However, in order to achieve best performance in terms of user separability, a dominant LOS channel needs to be present, where the standard deviation of the multi-path components from the nominal LOS angle is preferably small at least for conventional beamforming. This allows to accommodate a large number of users. Ideally, one generated beam or rather receive branch can serve one user. Thus, the upper bound is \(N_{\text{rx}}\) in the full-array and \(N_{\text{rx,sub}}\) in the sub-array case. However, this limit might be reached in pure LOS channels or/and for eigenspace-based beamforming, where the orthogonality of the beams is given as compared to conventional beamforming.
###### Abstract  
There is increasing evidence that anomalies in the ionosphere could appear a few days before large earthquakes. Many significant successes with using anomalies for predictions have been reported, although they are usually limited, both in space, to a specific geographic area, and in time, to one or a few events. To date, no solution has been presented that consistently yields the location and magnitude of future earthquakes and thus can be used to develop a warning service. The purpose of this research is to improve on the possible use of Global Ionospheric Maps for earthquake prediction. The use of three-dimensional data matrices, having spatiotemporal information to feed a convolutional neural network, is proposed in this contribution. This network was trained on all large earthquakes occurring from the beginning of the year 2011 to the beginning of October 2024 but it is proposed that it be periodically retrained with new data. This network has reached an accuracy of around 60% in the validation data for a division into eight categories of different earthquake magnitudes. Nevertheless, this percentage increases considerably if the classification into neighboring categories is also accepted, something that could be clearly admissible for the purposes of a warning system. The author believes that success in this endeavor has to come from a collaborative effort. For this reason, the training and validation data with three-dimensional matrices (latitude/longitude/time) of total electron content values along with the subsequent earthquake magnitudes are provided in this paper along with the trained network. Researchers are strongly encouraged to improve on the current neural network with or without the inclusion of additional information.  
Keywords:earthquake forecast; total electron content; deep learning; neural networks; warning system +
Footnote †: journal: Applied sciences
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 7 Discussion
### Discussion on American Put Pricing  
#### 7.2.1 Performance  
In this section, we present the performance analysis of various models in the context of American put option pricing, highlighting the superior performance of the KAFIN-based approach. As demonstrated in Table 4, KAFIN consistently outperforms other models in terms of residual statistics, showing the least error across all metrics, including mean, standard deviation (STD), minimum, absolute minimum, maximum, absolute maximum, and mean squared error (MSE). Specifically, the mean residual for KAFIN is 0.3027, with a standard deviation (STD) of 0.3166, indicating that KAFIN's predictions closely align with the expected outcomes and exhibit minimal variability.  
The residuals of KAFIN show the smallest values among all the models, with an absolute minimum of 0.0000, indicating a high level of accuracy. In contrast, the other models, such as FINN REG, FINN FFN, FINN DNN, and FINN MLP, demonstrate significantly larger residuals. For instance, FINN REG has a mean residual of \(-11.4139\) and an STD of 17.4739, indicating a much larger deviation from the ideal predictions. Similarly, the residuals for the other baseline models also show considerable discrepancies, with FINN FFN, FINN DNN, and FINN MLP exhibiting mean residuals of \(-11.7586\), \(-10.8948\), and \(-10.6701\), respectively. These values suggest that these models either fail to generalize effectively from the training data or struggle to capture the complexities of American put option pricing dynamics.  
Furthermore, the maximum residual for KAFIN is 3.6489, which is much smaller than the maximum residuals observed for the baseline models. For example, FINN REG has a maximum residual of 59.84995, while FINN FFN has a similar maximum value of 59.88425. These large residuals imply that these models may be overly sensitive to noise or fail to capture the nuanced relationships in the financial data.  
Despite KAFIN's relatively larger standard deviation compared to some other models, its overall performance is far superior in terms of reducing the magnitude of residuals. The robust performance of KAFIN can be attributed to its architecture, which is specifically designed to capture complex relationships inherent in financial data. Additionally, KAFIN's training methodology likely optimizes the model to minimize errors across various metrics, resulting in a more stable and accurate performance in American put option pricing.  
It can be seen from the results of the residuals that KAFIN stands out as the most reliable model for American put option pricing. Its ability to minimize residual errors and maintain close alignment with the expected results makes it a powerful tool in financial modeling. The superior performance of KAFIN over traditional models underscores its potential for real-world financial applications. Future work may focus on further optimizing KAFIN's architecture and exploring its applicability across a broader range of financial derivatives to enhance its utility.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 2 Literature Review
### The Development Trend of Smart Building  
At present, most cities are faced with high-rise and dense buildings, so it is the most important task to prevent and solve any type of fire situation quickly and effectively, in order to most effectively reduce losses in terms of life and property [9].  
The introduction of new technologies has provided us with many new options to achieve security in smart buildings and smart cities. The intelligent feature is that when such disasters occur, these buildings and cities should be able to implement emergency response measures quickly and holistically. Key technologies and factors that help prevent fires should also be noted. Some of the key technologies that can be used to achieve this ideal include:  
1. Significant growth and easy access to Internet connectivity and bandwidth.
2. Ubiquity of Smart Phones and Tablets along with their inbuilt notification systems.
3. Advancement of wireless technologies, especially for IoT enabled sensors.
4. Economical access to Cloud-based Apps and data storage.
5. Adoption of Computer aided Facility Management (CAFM), Building Information Modeling (BIM) and virtual reality (VR) technologies for efficient operation and management of buildings.