esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm for Fast Multiplication of Kaluza Numbers
## 1 Introduction  
The permanent development of the theory and practice of data processing, as well as the need to solve increasingly complex problems of computational intelligence, inspire the use of complex and advanced mathematical methods and formalisms to represent and process big multidimensional data arrays. A convenient formalism for representing big data arrays is the high-dimensional number system. For a long time, high-dimensional number systems have been used in physics and mathematics for modeling complex systems and physical phenomena. Today, hypercomplex numbers [1] are also used in various fields of data processing, including digital signal and image processing, machine graphics, telecommunications, and cryptography [2; 3; 4; 5; 6; 7; 8; 9; 10]. However, their use in brain-inspired computation and neural networks has been largely limited due to the lack of comprehensive and all-inclusive information processing and deep learning techniques. Although there has been a number of research articles addressing the use of quaternions and octonions, higher-dimensional numbers remain a largely open problem [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Recently, new articles appeared in open access that presented a sedenion-based neural network [23; 24]. The expediency of using numerical systems of higher dimensions was also noted. Thus, the object of our research was hypercomplex-valued convolutional neural networks using 32-dimensional Kaluza numbers.  
In advanced hypercomplex-valued convolutional neural networks, multiplying hypercomplex numbers is the most time-consuming arithmetic operation. The reason for this is that the addition of \(N\)-dimensional hypercomplex numbers requires \(N\) real additions, while the multiplication of these numbers already requires \(N(N-1)\) real additions and \(N^{2}\) real multiplication. It is easy to see that the increasing of dimensions of hypercomplex numbers increases the computational complexity of the multiplication. Therefore, reducing the computational complexity of the multiplication of hypercomplex numbers is an important scientific and engineering problem. The original algorithm for computing the product of Kaluza numbers was described in [25], but we found a more efficient solution. The purpose of this article is to present our new solution.
## 3 Results and Discussion
### Breakthrough Experiments under Humid Conditions  
The summarized results given in Table 7 reveal some interesting findings with regard to the performance of the two tested materials. For the AC_VS_600 adsorbent, the total specific CO\({}_{2}\) uptake was found to be quite close to that obtained under dry conditions (in spite of the lower CO\({}_{2}\) partial pressure in the wet feeding gas), whereas a higher drop in this performance indicator was observed for the AC_WS_500_P adsorbent. However, a certain degree of variability in \(q_{CO2}\) among the 10 cycles was observed for both materials. This variability, which was also evident from the CO\({}_{2}\) breakthrough curves shown in Figure 6, could be explained by the extremely slower kinetics of water vapor adsorption on biomass-derived porous carbons in comparison with that of CO\({}_{2}\)[17]. Figure 5: CO\({}_{2}\) breakthrough curves for five consecutive cycles (adsorption temperature = 50 \({}^{\circ}\)C) from a dry binary mixture of N\({}_{2}\) and CO\({}_{2}\) (with a CO\({}_{2}\) composition of 13.75–14.25 vol. %) using the AC_VS_600 (**a**) and AC_WS_500_P (**b**) materials. As already pointed out by [PERSON] et al., (2018), the amount of water adsorbed during the first cycles could progressively increase, leading to a gradual decrease in the adsorption sites available for CO\({}_{2}\). This was especially evident during the two first cycles for both tested adsorbents, as reported in Table 7. Nevertheless, after a few number of cycles, the CO\({}_{2}\) uptake seemed to stabilize (for the AC_WS_500_P adsorbent) or even increase up to a closer value to that obtained for the first cycle (for the AC_VS_600 adsorbent). These differences observed in the performance of the two adsorbents could also be related to the effectiveness of the desorption step as well as the transient period to reach water equilibrium. In the case of the AC_VS_600 material, its more hierarchical pore size distribution could facilitate a relatively fast desorption of CO\({}_{2}\) at 50 \({}^{\circ}\)C and mild vacuum conditions, despite the fact that a certain volume of micro- and mesopores could be filled by water. The fact that the CO\({}_{2}\) uptake fluctuated over the ten cycles could also indicate that further cycles are required to saturate the bed with water. For the AC_WS_500_P adsorbent, however, the observed lower CO\({}_{2}\) uptake could be due to a higher impact of the water adsorption on its surface, which could result in a critical reduction in the volume of mesopores and wide micropores available for CO\({}_{2}\) diffusion during the desorption step. Nonetheless, the almost steady CO\({}_{2}\) adsorption capacity observed for the last cycles also indicated that the relatively short residence time used here during the adsorption steps was appropriate to avoid a gradual accumulation of water within the bed.
# A Review of Blockchain Technology Applications in Ambient Assisted Living
## 3 Results  
After identifying and selecting the relevant papers using the defined criteria, we have conducted a qualitative analysis to identify blockchain applications and use cases in ambient assisted living systems and the associated challenges and limitations. Most of the literature on blockchain application in healthcare focuses on the health aspects, such as the management of electronic health records, and only a few relevant papers were found on addressing aspects of patient care at home using ambient assisted living systems. Nevertheless, most of the identified papers are very recent, mostly beyond 2020, showing that blockchain technology usage in ambient assisted living is a fast-emerging field of research that will gain a lot of attention in the near future.  
We have organized the reviewed papers on the basis of the most important aspects of ambient assisted living reported in the literature to which blockchain may bring significant improvements: (1) monitoring, timely follow-up, and intervention of patients or older adults living at home using IoT devices; (2) decentralized data storage to avoid single point of failure, data manipulation issues, and mistrust; and (3) privacy and security aspects of cross-continuum of care.
## 3 Damage Characteristics
### Microscale Damage
#### 3.2.2 Microscale Chemical Damage  
An analysis of the XRD and XRF patterns was conducted on sandy red clay samples, both before and after wet-dry cycling, reveals that the primary mineral components are quartz, kaolinite, illite, and hematite. As Figure 5 shows, the quartz content exceeds 50%. Clay minerals, predominantly kaolinite and illite, account for over 40% of the sample composition. The remaining minerals, primarily hematite, comprise less than 10% of the sample. No emergence of new diffraction peaks is observed in the XRD patterns throughout the wet-dry cycling process, indicating that wet-dry cycling does not induce the formation of new mineral components.  
Wet-dry cycles lead to a reduction in the proportions of kaolinite and illite present in the samples, whereas the proportion of quartz increases, leaving the proportions of other minerals predominantly unaltered. This suggests a pronounced hydration-weakening effect on the clay minerals, resulting in their continual depletion upon interaction with water, causing their content to diminish from 43% to 40%. The rate of mineral depletion in the samples reveals that illite (12.5%) undergoes a more significant reduction compared to kaolinite (3.7%), indicating that despite kaolinite's greater abundance in sandy red clay relative to illite, illite undergoes a more pronounced hydration weakening during water-rock interactions. Quartz and hematite demonstrate robust resistance to hydrolysis, consequently exhibiting negligible alterations in their content throughout the wet-dry cycling process. However, the depletion of clay minerals leads to an increase in the proportion of quartz, causing its content to escalate from 51% to 54%. Other minerals, predominantly hematite, remain predominantly unaltered in proportion owing to their low content.  
Figure 4: SEM images: (**a**) \(N=0\); (**b**) \(N=5\).
## 2 Numerical Model Development and Experimental Validation  
In this section, a numerical model for generating LW data from SLJs subjected to weak adhesion is developed. The model is validated, using machine learning techniques, by comparing the simulation results with those from experimental tests with real SLJs. A diagram of the approach can be seen in Figure 1. The experimental part is shown on the left-hand side, comprising SLJ fabrication, with distinct levels of weak adhesion, and LWs and strain-stress data acquisition. These data are correlated to correctly match LWs to levels of weak adhesion. The FEM simulation model of the SLJs is represented on the right-hand side of the diagram. At the bottom of the chart, a machine learning algorithm is used, which takes numerical and experimental data and validates the FEM model.  
Figure 1: Diagram of the proposed approach for FEM model development and validation.
## 3 Results and Discussion  
### Mechanism Model  
#### 3.1.1 Response of H+ to Toluene Mineralization  
According to [PERSON] et al., the nitrification process comprises a two-step reaction during microbially mediated toluene mineralization. The first product of nitrate reduction is nitrite (NO2\({}^{-}\)). Further reduction of nitrite proceeds to nitrogen gas (N2) [40, 41].  
Nitrification first step (nitrate reduction):  
\[\text{C}_{7}\text{H}_{8}+18\text{NO}_{3}{}^{-}=18\text{NO}_{2}{}^{-}+7\text{CO} _{2}+4\text{H}_{2}\text{O} \tag{12}\]  
Nitrification second step (nitrite reduction):  
\[\text{C}_{7}\text{H}_{8}+12\text{NO}_{2}{}^{-}+12\text{H}^{+}=6\text{N}_{2}+7 \text{CO}_{2}+10\text{H}_{2}\text{O} \tag{13}\]  
According to [PERSON] et al., in the two-step nitrification process, nitrite reduction produces hydrogen ions, whereas nitrate reduction does not [42]. During the complete mineralization process of toluene, the generated end products include gaseous carbon dioxide and water [43]. When gaseous carbon dioxide dissolves in water, it reacts and forms carbonic acid. The dissociation of hydrogen ions from the carbonic acid provides bicarbonate, and subsequently, a carbonate ion leads to an increase in H+ (Figure 1).  
Under natural practical conditions, nitrate usually degrades directly into nitrogen gas (Equation (2)). Toluene mineralization consumes H+, and the aqueous pH increases during the reduction process. The end products of mineralization are still carbon dioxide and water, which dissolve into the solution and eventually react with water to form stable carbonate compounds. Due to the complexity of the reactions, changes in the concentration of hydrogen ions in the solution are complicated. Based on the ionization fractions, the ratio of the solute components in the aqueous carbonate system will vary with H+. As the pH of the solution increases, the concentration of CO3\({}^{2-}\) also increases, particularly in an alkaline solution. When the saturation index (SI) reaches a certain value, some of the calcium carbonate in the solution precipitates out.
## 2 Research Methodology
### Problem Solutions  
Researchers have recently focused on solving the aforementioned issues. Different protocols to quickly deploy the adaptive video streaming segments onto surrogate servers are proposed. The WebDAV protocol has been selected as an optimal protocol to replicate the video contents. This is due to its ability to efficiently transfer small segments of DASH [13]. Moreover, Quick UDP Internet Connections (QUIC) is employed as the principal transport protocol for low-latency streaming protocols in IP multicast topologies. It makes use of mABR streaming and HTTP/3 server push methods to boost scalability and minimize the number of open streaming sessions, thus conserving bandwidth [14]. Although the redirection mechanism has been considered by [26], they aimed to provide an algorithm based on the qualitative and quantitative characteristics of different sources for selecting the most suitable alternative edge server. PROMETHEE is one of the most extensively utilized strategies in this respect. A transparent redirection mechanism for CDN surrogate servers is proposed in [27], where the TCP session handoff mechanism is considered as a transparent redirection.
## 3 Results
### Microfacies Analysis
#### 3.2.9. MF9: Lithoclastic Wackestone/Floatstone  
The matrix is predominantly composed of micrite, within which densely packed lithoclasts (floats) are embedded. Lithoclasts (floats) constitute over 60% of the rock volume, as evidenced by sample S104. Pressure solution stylolites, marked by arrows, are concentrated around the lithoclasts (floats) and are embedded within the micritic matrix. The fabric exhibits sutured laminated to sutured packed textures, indicative of significant compaction and diagenetic processes. This microfacies is observed in the upper part of the Chuangde Formation in the Zhangguo section in Gyangze.  
The presence of lithoclastic wackestone/floatstone, characterized by high lithoclast content and pressure solution features, suggests a depositional environment influenced by gravitational transport and reworking of carbonate material. This is consistent with a deep-water slope setting, where sediment gravity flows, such as debris flows or turbidity currents, are common. The sutured fabric further supports a depositional environment subjected to significant overburden pressure, typical of slope or basinal settings. These features collectively reinforce the interpretation of a deep-water slope depositional environment for this microfacies.  
Based on the microfacies characteristics (e.g., planktic foraminiferal packstone and grainstone) and microfossil assemblages (e.g., calcimicrobes, abundant planktic foraminiferal assemblages, silicinicrobes, and monotonous to paucispecific radiolarian assemblages), the planktic foraminiferal packstone and grainstone are interpreted to have been deposited in outer shelf to upper slope environments, proximal to the shelf margin shoal complex. In contrast, the radiolarian cherts are inferred to have accumulated in deep-water, oceanic basin environments situated below the carbonate compensation depth (CCD). This distinction highlights the contrasting depositional settings associated with these microfacies, reflecting the transition from shallower shelf-slope systems to deeper pelagic basins.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions  
While the reviewed literature demonstrates significant progress in ETC-based traffic sensing technologies, several methodological and practical challenges emerge from this comprehensive analysis. The comparison tables presented in this survey reveal that no single approach excels in all scenarios, highlighting the need for context-specific technology selection and hybrid methodologies that can adapt to varying traffic conditions and application requirements.  
Although ETC data-based urban traffic state sensing and analysis technologies have achieved significant progress in both theoretical research and practical applications, they still face numerous technical challenges and application bottlenecks in the process of developing toward larger-scale, deeper-level, and broader-scope applications. These challenges involve not only breakthrough requirements at the technical level but also concern the coordinated development of industrial ecosystems and comprehensive policy environment support. Meanwhile, with the continuous emergence of new technologies and evolving urban traffic demands, this field is experiencing unprecedented development opportunities and innovation space.
# Analysis of Motion Characteristics of Bionic Morphing Wing Based on Sarrus Linkages
## 2 Materials and Methods
### Bionic Morphing Wing Kinematic Model  
The morphing wing structure designed in this paper has a degree of freedom of 1, so only one power source is needed to drive it. As can be seen above, the driving object selected in this paper is the inner wing, which means that the rotation angle of the inner wing becomes the input variable of the mechanism.  
The center of the left and right wings was taken as the origin of coordinates for analysis. In order to simplify the kinematic analysis of the morphing wing, the motion analysis diagram shown in Figure 7 was made. In the figure, the distance between the fuselage-connecting platform AF and the origin coordinates is \(\Delta\). The lengths of the inner wing (AB) and middle wing (BC) are respectively a and b, and the included angles between them and axis X are, respectively, \(\alpha_{1}\) and \(\alpha_{2}\). The lengths of the outer frame and the fuselage connecting platform are both c.  
According to the design requirements, \(a=b\), \(\alpha_{1}=\alpha_{2}\), the rotational angular velocity, and angular acceleration of the inner wing and the middle wing are the same. The following is the analysis of the motion characteristics of the outer wing. In Figure 5, the distance between the midpoints \(O_{1}\) and \(O_{2}\) of AF and CD is \(\left[\Delta+2a\text{cos}\alpha_{1}\right]\). As can be seen from the figure, their coordinates are, respectively:  
\[O_{1}=\begin{bmatrix}x_{O_{1}}&y_{O_{1}}&z_{O_{1}}\end{bmatrix}^{T}=\begin{bmatrix} \Delta&0&0\end{bmatrix}^{T} \tag{4}\]  
\[O_{2}=\begin{bmatrix}x_{O_{2}}&y_{O_{2}}&z_{O_{2}}\end{bmatrix}^{T}=\begin{bmatrix} \Delta+2a\text{cos}\alpha_{1}&0&0\end{bmatrix}^{T} \tag{5}\]  
Figure 6: Diagrammatic sketch of morphing wing: (**a**) morphing wing expanded state; (**b**) morphing wing folded state.  
Figure 7: Sketch of morphing wing motion analysis.  
As mentioned above, the morphing wing structure has only one translational degree of freedom along the X-axis, so only one variable is needed to express the position equation of any bar, which can be obtained from the above equation:  
\[x_{O_{2}}=\Delta+2a\text{cos}\alpha_{1} \tag{6}\]  
\[\alpha_{1}\leq 90^{\circ}.\]  
By taking the first derivative of both sides of the above equation with respect to time \(t\), the velocity expression of the outer frame can be obtained:  
\[\dot{x}_{O_{2}}=-2a\dot{\alpha}_{1}\text{sin}\alpha_{1} \tag{7}\]  
Continue to take the first derivative of both sides of the above equation with respect to time \(t\), and the expression of the acceleration of the outer frame can be obtained  
\[\ddot{x}_{O_{2}}=-2a\ddot{\alpha}_{1}\text{sin}\alpha_{1}-2a\dot{\alpha}_{1} ^{2}\text{cos}\alpha_{1} \tag{8}\]  
The above procedure completes the position, velocity, and acceleration analysis of the morphing wing.
# Advancing Digital Twin-Based Collision Avoidance:
## 3 Evaluation of Results
### Real-World Data Acquisition  
For practical data gathering, an RTLS module was installed on a forklift in an industrial setting. ICMP packet-based RTT measurements were conducted between the forklift's compute unit and a central control unit connected via a network cable to the 5G core. Figure 6 presents data from one of the test runs, indicating position and RTT. Over 92 min, 5540 data samples were collected at a 1 Hz rate.  
Figure 7 depicts the interdependence between the system's update frequency and the associated delay at the 95 th percentile. This metric provides a critical benchmark for systems requiring adherence to stringent delay thresholds. Here, \(\tau_{\text{edge}}\) is maintained at 10 ms, and the RTT \(\tau_{\text{ttt}}\) exhibits a mean of 10.41 ms with a standard deviation of 4.60 ms. The data from the measurements suggest that while the centralized configuration introduces additional latency through edge processing, strategic optimizations in the software and hardware could substantially mitigate this factor. Moreover, by decoupling the truck's publish rate from the edge server's update frequency, system efficiency could be enhanced, allowing for less frequent yet more strategic position updates. In situations where real-time position updates are not necessary, a more bandwidth-efficient approach could be used by transmitting updates only when collision events occur. This modification could effectively support the centralized architecture in managing larger fleets, scaling up to larger fleets, according to operational requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Component** & **Size** \\ \hline  & IPv6 Header & 40 B \\  & UDP Header & 8 B \\  & CoAP Header & 9 B \\  & Payload (Position, Orientation, Speed, Timestamp) & 24 B \\ \hline \hline \end{tabular}
\end{table}
Table 2: Byte composition of a real-time location system (RTLS) packet.  
Figure 5: Throughput comparison of messages on the 5G channel for centralized and decentralized models with varying R-PDU packet rates and truck numbers. A 1% warning probability and a 2 Mbit/s bandwidth limitation are assumed. The decentralized model is shown to be less feasible for larger fleets.
## 3 System Model and Architecture Design
### Impacts of Standby Ratio on Power Consumption  
Table 2 shows that increasing the standby ratio leads to a high power consumption. To minimize this power consumption, we need to develop a mechanism that ensures that the microcontroller and sensors will operate based on the ratio of 1:10 (active to standby ratio), which means that, in a duration of ten minutes, the microcontroller is active only for one minute and on standby for nine minutes. This means that some sensors are put in standby mode until the required information is obtained to turn them ON, and then they read the data for the scheduled period.  
The following system conditions and microcontroller parameters are to be considered and accounted for in the estimation of the standby current. Firstly, there is an automatic wake up on time intervals: in this condition, the microcontroller offers real-time clocks (RTC) that can run in low-power standby modes, enabling us to wake up the microcontroller automatically at specified time intervals to read the data and transmit the data to the sink node [32; 33]. Secondly, there is a retention of random-access memory in standby conditions. In this second condition, the specification of the microcontroller shows that the content of random-access memory in standby mode allows the microcontrollers to wake up quickly without running a start-up code, which leads to a high consumption of valuable energy [33; 34]. The third condition is interrupting capabilities--the microcon  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Active to** & **\% Time in** & \begin{tabular}{c} **Time active \(\times\) I** \\ **s**athby **\end{tabular} & \begin{tabular}{c} **Time** standby \(\times\)** \\ **s**athby **\end{tabular} & \
\begin{tabular}{c} **Total Charge** \\ **(\muAs)** \\ \end{tabular} &
\begin{tabular}{c} **\% Impact of I** **Standby** \\ **to** **Total Power** \\ \end{tabular} \\ \hline
1:10 & 90\% & 100 & 5 & 105 & 6.54\% \\ \hline
1:100 & 99\% & 100 & 50 & 150 & 33\% \\ \hline
1:1000 & 99.9\% & 100 & 500 & 600 & 83.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The impacts of standby ratio on power consumption [33].  
troller specification sheet shows that it often leaves certain peripherals active in standby mode, enabling the microcontroller to wake up quickly with certain events [34]. The last one is power monitoring--Brown out reset (BOR) and supply voltage supervisor (SVS) are important circuits that monitor the integrity of the microcontroller's power source. Faults and interruptions to the microcontroller power source can impact the reliability of the operation.