esa-sceva/satcom-chunk-collection huggingface dataset:
# Phytochemical Analysis and Amoebicidal Evaluation of Different Agave Species
## 3 Results and Discussion
### Agave Extracts Induce Morphological Alterations in E. Inistolytica Prophozoites  
The effect of the Agave extracts on trophozoite morphology was evaluated using bright-field microscopy. All concentrations of the Agave extracts stimulated a wide variety of morphological alterations compared to the control trophozoites. No differences were found in the morphology of untreated and DMSO-treated trophozoites; the cells showed a distinctive pleomorphic form and the presence of pseudopods (Figure 5A,B). After Agave exposure, there was a prevalence of detached trophozoites with a rounded form; there were parasites with apparent protuberances in the membrane, and cell lysis and DAPI staining revealed apparent changes in the diameter of nuclei (Figure 5). With _A. tequilana_ and _A. angusifolia_, more than 60% of cells showed damage; ruptured trophozoites and cell debris were observed (Figure 5C,D). With _A. maximilana_, 80% of the parasite population presented a granular surface, and an accumulation of filopodial protrusions prevailed (Figure 4G,H). Phytochemical screening indicated a greater presence of flavonoids and steroids in _A. tequilana_, _A. angusifolia_, and _A. rhodacantha_ (Table 2), which could be responsible for the drastic morphological alterations observed due to these extracts. Several studies support the amabeicidal activity of flavonoids, and (\(-\))-epicatechin and kaempfer have been related to growth inhibition, decreased cell viability, and nuclear and morphological alterations in _E. histolytica_ trophozoites. Damage to cytoskeletal structures with changes in essential mechanisms such as adhesion, cytolysis, and nuclear alterations followed by programmed cell death are the principal findings [67; 68]. In addition, it has been demonstrated that the motility of _E. histolytica_ depends on the sustained instability of the intracellular hydrostatic pressure that drives the cyclic generation and healing of membrane blebs [69]. Furthermore, actin filaments constitute the physical backbone of membrane protrusions [70]. The prevalence of the filopodial protrusions that _A. maximiliana_ caused in trophozoites could be related to alterations in intracellular hydrostatic pressure and the actin cytoskeleton. Our results suggest that the trophozoites' deaths occurred principally due to a loss of membrane integrity, changes within the nucleus, and cytoskeletal alterations; biochemical studies are necessary to obtain evidence of programmed cell death in _Entamoeba histolytica_ due to Agave extracts.  
Figure 5: _Cont._
# Deformations of FRP-Concrete Composite Beam: Experiment and Numerical Analysis
## 5 Conclusions  
Verification of the performance of the simplified tension-stiffening approach to represent the deformation behaviour of a glass fibre-reinforced polymer (GFRP)-concrete element is the focus of this research. Two series of specimens with the same geometry of concrete cross-section and different reinforcement types were considered--two nominally identical beams composed each set of samples. The geometry of the GFRP-concrete beam was chosen to simplify numerical simulations. This specimen is only a prototype of structural systems. The beam specimens reinforced with steel bars were used to determine the tension-stiffening model of the concrete that further was applied for the modelling of the GFRP-concrete beams. A nonlinear finite element (FE) model was developed for that purpose. The following conclusions were made.  
* The tension-stiffening approach was not capable of predicting the deformation response of GFRP-concrete elements. The adequate application of this approach is limited to the FE models with reinforcement simulated as embedded truss elements. Contrarily, the tension-softening effect is predominant for the modelling of FRP-concrete specimens.
* A detail FE model is capable of representing deformation behaviour and failure mechanisms of GFRP-concrete composite elements. However, engineering applications of such an approach can be problematic because of the existing hardware limitations.
* A simplified model is proposed to solve the above problem. The model assumes a perfectly elastic model of the compressive concrete; the contribution of the tensile concrete and adhesive are neglected. The predictions are in a satisfactory agreement with the experimental results. The prediction error, corresponding to the service load, is approximately equal to 2%.  
Conceptualization V.G. and A.S.; methodology V.G.; software A.S.; validation I.M., A.R.; investigation, I.M. and A.S.; resources V.G.; data curation A.R. and A.S.; writing--original draft preparation A.S., A.R.; writing--review and editing V.G.; visualization A.S.; supervision V.G.; project administration I.M.  
The European Social Fund funded this research and APC within the framework of the project \"Development of Competences of Scientists, other Researchers and Students through Practical Research Activities\" (Project No 09.3.3-LMT-K-712).  
Figure 11: Analytical results of the curvature prediction of the beam specimen _B2-GFRP_.  
**Acknowledgments:** The funding provided by the European Social Fund is sincerely acknowledged.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Results and Discussion
### Influence of Bolt Diameter on Fatigue Strength  
In existing fatigue performance research of M20 twisted-shear high-strength bolts [24; 25], the material (ML20 MnTiB) and strength grade (10.9) used in this test are exactly the same as the bolts tested, and the test loading stress ratio \(R\) is equal to 0.1. The value range of the largest stress is 350-550 MPa. The value range of the smallest stress is 35-55 MPa, with both showing a gradual decreasing trend. However, the fatigue life becomes longer and longer. The maximum value is 1,021,264 N/time; the minimum is 28,103 N/time. The results of normal amplitude fatigue tests in the literature are listed in Table 15.  
In order to compare the constant fatigue performance of M24 and M20 twisted-shear high-strength bolts under full pretension, the double-log S-N curve is made in the same coordinate system according to the fatigue test data in Tables 5-8 and in Table 15, as is shown in Figure 12, and the logarithmic equation for the corresponding part of the oblique line of the S-N curve is developed as the third equation in Table 16.  
The constant amplitude fatigue S-N curve equations of the 8.8-grade M24 high-strength large hexagonal head bolt, the 10.9-grade M24 high-strength large hexagonal head bolt, and M20 torsion-shear bolt were obtained by regression fitting, as shown below. As can be seen above, the following conclusions can be drawn:  
1. Under full pretension, the fatigue test data of the M20 twisted-shear high-strength bolt have higher values than those of the M24 bolt.
2. Under the guaranteed probability of 97.72%, the stress amplitude of the M20 twisted-shear high-strength bolt corresponding to 2 million fatigue life is about 278.0 MPa, which is greater than the stress amplitude, the value of which is 181.0 MPa for the M24 bolt. It was indicated that under the same stress amplitude the M20 twisted-shear high-strength bolt has a longer fatigue life than the M24 twisted-shear high-strength bolt.
3. The fatigue strength will decrease when the bolt diameter increases. The reason is that, due to the influence trend that the bolt diameter has on the fatigue strength, when the diameter of the bolt increases, the concentration of the stress between the thread and the bolt will be exacerbated; meanwhile, some defects or impurities will occur during the process of heat treatment.
# BIM-Based Management System for Off-Site Construction Projects
## 2 Literature Review  
### OSC Project Management  
OSC has been assigned different terms over the years: offsite manufacture or production [30], prebuilt construction [39], prefabricated building [40], and pre-assembly [41]. Despite subtle differences among these concepts, they are consistent with the definition of OSC [42; 43; 44]. In general, OSC is aimed at completing a building by producing the components of a mass or structure in a controlled location offsite, transporting these entities to the site, and completing the assembly onsite [45; 46; 47]. The following methods are also categorized as OSC because the proportion of offsite production is higher than that of the onsite construction: modular construction [48], precast construction [49], industrialized building [50], modern methods of construction [51], and systems building [41].  
Although construction is conducted onsite in OSC projects, the finished products are transported by road from the factory to the site in vehicles. This feature shortens the project period by allowing work that is typically performed sequentially in a single space to be performed simultaneously in multiple areas [52]. In addition, offsite production with specialized systems minimizes the temporary facilities required onsite and the overhead costs. Moreover, the direct costs decrease owing to the standardized production [20]. In addition, uniform production following confirmative drawings and line processes results in improved quality [53]. Finally, in the OSC framework, the resources do not move to the fixed products, and instead, the products move to the fixed resources, which eliminates dangerous activities, especially in terms of scaffolding [23]. Furthermore, simplified onsite work lowers the environmental burden associated with the construction industry by minimizing the waste and maximizing the energy efficiency [19; 54].  
Considerable research has been performed on OSC. [PERSON], et al. [55] conducted a holistic review of OSC processes and highlighted the active research being performed in the manufacturing and assembly fields. The researchers have also attempted to apply emerging technologies to OSC projects [56; 57]. Notably, OSC projects can be optimized using IoT technologies [58; 59]. Studies have also been performed on the three phases of the OSC project: offsite production [29; 53; 60], transport [61; 62; 63; 64; 65; 66], and onsite installation [67; 68; 69; 70; 71; 72].
## 7 Summary and Conclusions  
The LIBS instrument module developed for the DLR LRU2 rover demonstrated successful performance during the field test at Mt. Etna, Sicily, obtaining suitable in-situ LIBS data of the geological targets. The ARCHES LIBS module, a first prototype, underwent minimal alterations after the indoor test at DLR-RM, including replacing a spacer with a baffle and changing the cover. To the best of our knowledge, it represents the first arm-deployed LIBS instrument on a rover.  
As part of a sustainability-focused project, the ARCHES LIBS module is currently undergoing upgrades and adaptations for soil analysis in an agricultural context. The enhancements include the implementation of a camera for context images, a more compact design, and the integration of additional sensors, such as a microphone, to improve data analysis. Furthermore, in an upgraded version, efforts will be made to extend the wavelength range, covering additional elements of interest.  
Conceptualization, S.S., S.F., E.D., A.W., A.B. and H.-W.H.; methodology, S.S., F.S., E.D., S.F., P.L., A.F.P. and B.V.; software, F.S. and S.F.; validation, S.S., F.S., E.D., S.F. and P.L.; formal analysis, S.S. and F.S.; investigation, S.S., F.S., P.L., A.F.P., R.S. and B.V.; resources, A.W., A.B. and H.-W.H.; data curation, S.S. and F.S.; writing-original draft preparation, S.S., F.S., E.D., S.F., P.B.H., P.L., A.F.P., R.S. and B.V. writing--review and editing, S.S., F.S., S.F., R.S., A.W., A.B. and H.-W.H.; visualization, S.S., F.S., E.D., S.F., P.L. and A.F.P.; supervision, S.S.; project administration, S.S. and A.W.; funding acquisition, A.W., A.B. and H.-W.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Helmholtz Association in the strategic future topic ARCHES ZT-0033.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data can be made available upon request. All data obtained during the ARCHES space demo mission will be made available to the public.  
**Acknowledgments:** We gratefully acknowledge support from DLR and colleagues for the preparation and successful implementation of the space demonstration mission at Mt. Etna, Sicily.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 6 Benchmarks
### Secure Batch Division  
In comparing our secure batch division protocol, we fix three parties and focus on two settings--one consisting of a small batch of divisions (\(N=2\)) and one consisting of a large batch of division (\(N=128\)), which corresponds to the required number of divisions for the [PERSON]'s \(t\)-test and \(\chi^{2}\)-test, respectively. We then linearly extrapolate the runtime and communication overhead of running the SMPC approach for a batch of 16384 divisions, as this is not practical to test.  
Assuming that we have a set of numerators \(a_{1},a_{2},\ldots,a_{N}\) and a set of denominators \(b_{1},b_{2},\ldots,b_{N}\) that are either encrypted (for our MHE protocol) or secret shared (for the SMPC method), the task is to compute either encrypted or secret shared versions of \(c_{1},c_{2},\ldots,c_{N}\) s.t. \(c_{i}\approx\frac{a_{i}}{b_{i}}\). In order to demonstrate the robustness and relative error of our protocol, \(a_{i},b_{i}\) are additionally randomly chosen from two ranges--a small positive range \([1\times 10^{-4},1]\) and a large positive range \([1,1\times 10^{4}]\). We compare three different metrics averaged across five runs of each computation--communication overhead (MB), runtime (s), and relative error (%)--and present the results in Tables 1-3, respectively. As the communication overhead and runtimes are similar in both ranges, only the results for the large range is presented.  
We can see that while the SMPC method requires less communication and is \(10\times\) faster when the number of divisions is low, even when the number of divisions is moderate, it requires \(30\times\) more communication than our MHE method. This communication overhead makes a difference even in the LAN setting, thus making our MHE method faster for a moderate number of divisions. At scale, when considering the full supported batch of divisions in MHE, we can clearly see that the SMPC method requires 235 GB of data to be transferred between the parties, thus making it impractical even in a LAN setting. The full amortized runtime cost of running secure batch division using our MHE method is \(4.60\times 10^{-4}\) s/division compared to the SMPC method's \(7.52\times 10^{-2}\) s/division, which is a more than a \(100\times\) improvement in runtime.  
In terms of relative error, while we can see that the MHE method appears slightly more accurate than the SMPC method, this is most likely due to the larger number of iterations that the Goldschmidt division has to be run for in our MHE method owning to the large range of possible values.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 2 Literature Review
This algorithm incorporated a competition strategy into the standard PSO framework to optimize the global best solution. In [52], a method was presented to address the path-planning problem for UAVs operating in adversarial environments with radar-guided surface-to-air missiles (SAMs) and unknown threats. An improved PSO algorithm is utilized, along with preprocessing steps involving Voronoi diagram generation and the use of the Dijkstra algorithm to obtain an initial path for the multi-swarm PSO algorithm. The multi-swarm PSO algorithm employed multiple swarms with sub-swarms to balance exploration and exploitation. Researchers in [40] proposed a three-dimensional path-planning algorithm for UAV formation using an enhanced PSO approach. The algorithm incorporated a chaos-based logistic map to improve the initial distribution of particles and replaced constant acceleration coefficients and maximum velocity with adaptive linearly varying values, leading to an improved optimal solution. In spite of the improvements associated with the PSO algorithm in the literature, it still encounters the issue of premature convergence and the risk of becoming trapped in local minima when applied to complex problems. To highlight the limitations and strengths of PSO and other meta-heuristic algorithms, such as GA, ABC, CIPSO, and IPSO, we conduct a thorough performance evaluation and consider different metrics. Furthermore, we demonstrate the differences among these algorithms by analyzing the collected data and employing a statistical test, specifically the one-way ANOVA test. This study effectively guides researchers in enhancing the current approach to overcome the challenges related to local optima and slow convergence issues.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 3 The Proposed CSO-Based Algorithm
### The Proposed CSO-Based Approach
#### 3.1.4 Calculating F\({}_{4}\)(r)--The Service Provider's Cost Actor  
[PERSON] and [PERSON] [43] proposed the addition of a fourth cost, namely \(F_{4}(r)\), in the evaluation function trying to construct qualitative route sets from the service provider's scope. This fourth cost depends mainly on each route set's total length. At first, they calculate the total length \(L_{tot}\) of each route set by summing all time distances between nodes belonging to the routes of each route set. \(L_{tot}\) is proportional to fuel consumption, a crucial factor for each service provider. The mathematical formula calculating score \(F_{4}(r)\) is the following equation:  
\[F_{4}(r)=\ \left\{\begin{array}{cc}-\Big{(}\frac{b_{4}}{x_{4m}}+\ \frac{b_{4}}{x_{4m}^{2}}\Big{)}L_{tot}^{2}+\ b_{4}\cdot L_{tot}+K_{4},&if\ F_{4}(r)\ \leq\ K_{4}\\ K_{4},&if\ F_{4}(r)>K_{4}\end{array}\right. \tag{11}\]  
where \(K_{4}\) is a positive parameter determined by the user defining the upper limit of \(F_{4}(r)\), \(-K_{4}/x_{4m}\leq b_{4}\)\(\leq 0\) and \(x_{4m}\) is a limit value for \(L_{tot}\) according to which route sets having \(L_{tot}\) values smaller than the value of \(x_{4m}\) are supposed to be optimal. In these cases, \(F_{4}(r)\) reaches its maximum value.
## 3 Results and Discussion
### Variation of Doppler Signal  
This section presents the data on DFS of ionospheric signals, which were obtained at the Geophysical Observatory, together with the measurements of ULF/VLF electromagnetic fields and gamma radiation background, during the period from 1 February up to 15 March 2024, i.e., both previously and later the 4 March 2024 \(M6.1\) earthquake.  
For the purpose, a hardware and software complex for Doppler measurements was applied, which uses the technique of phase-locked loop (PLL) to estimate the DFS of a larger amplitude beam at multipath signal propagation. A detailed description of the method may be found in [32], and the results of its application were published in [33].  
Monitoring of DFS variation was carried out on an inclined, 970 km long radio path \"Dushanbe-orzu--Institute of Ionosphere (Almaty)\". For DFS measurement, the radio transmitter (TX) of the Dushanbe-orzu broadcasting radio station (Tajikistan, 37.533\({}^{\circ}\) N, 68.700\({}^{\circ}\) E) operates at a frequency of \(f=7245\) kHz. At such a distance, reflection of radio waves occurs gradually, as a wave passes a sufficiently large section of its path in the ionosphere. In this case, a big reflection area of sounding radio waves is formed, which determines the magnitude of DFS. Selection of the optimal radio frequency was made  
Figure 7: Variation of the dynamic amplitude spectrum of ULF signal in the 0.025–0.066 Hz band before the \(M6.1\) earthquake on 4 March 2024. The vertical line (\(EQ\)) corresponds to arrival of the seismic wave from the earthquake to the IMS-008 sensor.  
in accordance with the BBC Frequencies and Sites Catalog of the British Broadcasting Corporation [42].  
The receiver part (RX) of the Doppler radio installation is located at a research station of the Institute of Ionosphere in Almaty (43.175\({}^{\circ}\) N, 76.953\({}^{\circ}\) E). The location diagram of the 4 March 2024 \(M\)6.1 earthquake epicenter relative to the radio path of the Doppler ionosonde is presented in Figure 8.  
As shown in Figure 8, the Dobrovolsky radius for an \(M\)6.1 earthquake is \(R_{D}=419.7\) km. (The Dobrovolsky radius limits the area of lithospheric deformations at the preparation of an earthquake and depends on its magnitude \(M\), \(R_{D}=10^{0.43\cdot M}\) km [34]). The distance from the epicenter to the sub-ionospheric point in the middle of the radio path (indicated with a red ellipse in Figure 8) equals 452 km. Thus, the sub-ionospheric point was situated outside the Dobrovolsky zone.  
However, as seen in Figure 8, half of the region of radio wave reflection is shifted by 250 km from the median point towards the earthquake epicenter and thus still hits inside the Dobrovolsky circle. Such a disposition allows us to assume that deformation processes in the lithosphere should find a response in the variation of DFS.  
To determine the size of the radio wave reflection region and its height in the ionosphere, a calculation was made of the electron concentration profile and of the propagation trajectory of the sounding radio wave.
## 4 Experimental Results and Discussion
### Investigation of Embedding Dimension  
To explore how the embedding dimension \(d\) affects the performance of the proposed SSRHE method, thirty samples were randomly selected from each class for training, and \(d\) was tuned from 5 to 50 with an interval of 5. Figure 9 shows the classification accuracy of SSRHE and other DR methods with different embedding dimensions on Indian Pines and PaviaU datasets. According to Figure 9, the OAs firstly rapidly rose and then tended to stabilize with an increasing \(d\), because embedding features with high dimension contained more useful information for training, while too large dimension might lead to information saturation. The classification accuracies of the most DR methods were better than the RAW method since these methods can remove the redundant information in HSI. The proposed SSRHE achieved the best classification performance in comparison with other methods on two HSI datasets. The reason is that SSRHE discovered the complex relationships between samples by hypergraph model and fused the useful spatial information for extracting discriminant features for classification.
## 3 Kinematic Analysis of CDHRM  
### Multilevel Mapping Relationships of CDHRM  
According to the mechanical design, the multilevel mapping relationships of CDHRM can be described in Figure 2, which can be divided into cable-joint kinematics and joint-end kinematics, where the relationship between the forward and inverse kinematics is expressed as: driving space--joint space--working space. For cable-joint kinematics, the geometric relationship between the cable length and the joint angle are developed via the established geometric model. Subsequently, for joint-end kinematics, the projection relationship between the joint angles and end-effector's pose is established via the spatial coordinate transformation matrix.  
To establish multilevel mapping relationships of the CDHRM, a geometric model and coordinate system is created as shown in Figure 2(a),b, where the Figure 2(a) represents the geometric model when the joint is in the odd condition, similarly the Figure 2(b) represents the geometric model when the joint is in the even condition. The frames \(l_{i}\) and \(j_{i}\) are created at the center of two tubular structures, and another frame \(U_{i}\) is created at the center of the universal joint.