esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Security and Usability Challenges in System Authentication  
It is not easy to balance between security and usability in authentication systems. Increasing the security level of a design often leads to an increase in the system complexity that could negatively affect the user experience [37, 38]. For instance, while strong authentication systems employing multi-factor approaches provide strong resistance against both spoofing and adversarial attacks, they induce user frustration due to the increase in time complexity or repeated queries for user verification [19]. Similarly, when environmental factors, e.g., lighting or humidity, disrupt sensor detection sensitivity, highly sensitive biometric systems may not function properly, leading to the production of a wrong output [39, 40, 41].  
It is worth noting that repeatedly tracking the biometric features of users and precisely extracting valuable features, such as touch pressure, swipe dynamics, and trajectory patterns, significantly increase the system's robustness [42, 43]. However, the computations of real-time processing will be highly increased, and this renders the system unsuitable for seamless use. Interestingly, decision-making techniques, e.g., TOPSIS, can be utilized to select only valuable features, and this, in turn, ensures that the system remains user-friendly and secure and decreases the computational system penalty [44, 45]. Although the use of TOPSIS introduces an additional decision-making step, it serves as a valuable dimensionality reduction technique that simplifies the learning process. By selecting only the most relevant features, TOPSIS contributes to reduced computational load and enhances real-time performance, making the system suitable for practical deployment on resource-constrained devices such as smartphones.  
The goal of this work is to develop a lightweight and robust authentication framework that is dynamically adapted to the user environments and conditions in real-world applications [38]. Even though significant progress has been achieved in the field of biometric authentication, current existing techniques are still subjected to cybersecurity attacks and are not robust to real-world conditions. To address the aforementioned shortcomings, in this paper, different ML algorithms are integrated with TOPSIS and behavioral biometrics to get a secure, resilient, and lightweight prominent solution for smartphone authentication.
## 5 Conclusions  
This paper addresses the inter-satellite data-transmission requirements for space-based gravitational wave detection, analyzing key aspects of the inter-satellite laser communication system, including PRN code selection, signal scheme design and mathematical modeling of signal transmission. In PRN code selection, six orthogonal PRN codes with excellent autocorrelation and cross-correlation properties were chosen, satisfying the requirements of the gravitational wave-detection mission. For the signal scheme, the sampling rate was set to 100 MHz, the PRN code rate to 1 Mbps and the data code rate to 62.5 kbps, resulting in a total code rate of approximately 976.6 Hz. This design ensures the inter-satellite communication system meets the mission's data rate requirements while providing good communication performance. A simulation model of inter-satellite laser communication was established using Simulink to validate the system design. The simulation results confirmed the proper implementation of spreading, phase modulation, noise addition, phase demodulation and despreading functionalities. Additionally, based on the TianQin constellation, the BER analysis demonstrated that the simulation model achieves a data-transmission rate of 62.5 kbps with a BER better than \(10^{-6}\) when the modulation index exceeds \(3.4\times 10^{-3}\), fulfilling the inter-satellite laser communication requirements for space gravitational wave detection. Future research will focus on refining the BER model, optimizing simulation results and improving data flow efficiency to enhance the performance of inter-satellite laser communication systems for space gravitational wave-detection missions.  
Conceptualization, Z.Y.; methodology, Z.Y.; software, H.L. (Haoqian Liang); validation, H.L. (Haoqian Liang); formal analysis, H.L. (Hongling Ling); investigation, H.L. (Haoqian Liang); resources, K.L.; data curation, H.L. (Haoqian Liang); writing--original draft preparation, H.L. (Haoqian Liang); writing--review and editing, Z.Y.; visualization, H.L. (Haoqian Liang); supervision, K.L.; project administration, Z.Y.; funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number: 2023 YFC2205501.  
Figure 18: Bit error rate of the simulation system under different modulation indices and the corresponding fitted curve.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## Appendix B Expectation of Effective Area \(\overline{A}(R)\)  
In this appendix, we derive the expectation (Equation (5)) of effective area. The expectation, \(\overline{A}(R)\), of the effective area of a node can be obtained by averaging the effective area \(A(r,R)\) over the entire sensor field, which can be represented in a polar coordinate system:  
\[\overline{A}(R)=\frac{1}{\pi L^{2}}\int_{0}^{2\pi}\int_{0}^{L}A(r,R)\,r\, \mathrm{d}r\,\mathrm{d}\theta.\] (A16)  
Applying Equation (4) to Equation (A16), we have  
\[\overline{A}(R) = \frac{1}{\pi L^{2}}\int_{0}^{2\pi}\int_{0}^{L}A(r,R)\,r\, \mathrm{To calculate Equation (A17), we need to have the corresponding indefinite integrals. For the first integration out of the three integrals in Equation (A17), we have  
\[\int r\cos^{-1}\left(\frac{r^{2}+R^{2}-L^{2}}{2 rR}\right)\,\mathrm{d}r = \frac{1}{2}r^{2}\cos^{-1}\left(\frac{r^{2}+R^{2}-L^{2}}{2 rR}\right)\] (A18) \[-\,\frac{1}{4}\sqrt{4r^{2}L^{2}-(r^{2}-R^{2}+L^{2})^{2}}\] \[-\,\frac{L^{2}}{2}\tan^{-1}\left(\frac{R^{2}+L^{2}-r^{2}}{\sqrt{4 r^{2}L^{2}-(r^{2}-R^{2}+L^{2})^{2}}}\right).\]  
For the second integration in Equation (A17), we can now obtain the indefinite integration by doing the substitutions \(R\to L\) and \(L\to R\) on Equation (A18). For the third integration in Equation (A17), we have  
\[\int r\sqrt{4r^{2}L^{2}-(r^{2}-R^{2}+L^{2})^{2}}\,\mathrm{d}r = \frac{(r^{2}-R^{2}-L^{2})}{4}\sqrt{4r^{2}L^{2}-(r^{2}-R^{2}+L^{2 })^{2}}\] (A19) \[-\,R^{2}L^{2}\tan^{-1}\left(\frac{R^{2}+L^{2}-r^{2}}{\sqrt{4r^{2} L^{2}-(r^{2}-R^{2}+L^{2})^{2}}}\right).\]  
Finally, we obtain the expectation of effective area \(\overline{A}(R)\) by applying the indefinite integrals in Equations (A18) and (A19) to the definite integrals in Equation (A17) followed by straightforward manipulating for simplifications:  
\[\overline{A}(R)=2R^{2}\cos^{-1}\left(\frac{R}{2L}\right)+L^{2}\cos^{-1}\left( 1-\frac{R^{2}}{2L^{2}}\right)-\frac{(2L^{2}R+R^{3})\sqrt{4L^{2}-R^{2}}}{4L^{2 }}.\] (A20)
## 3 CPN Models
### Basic Models
#### 3.1.5 Event receive  
Generally, the events in the nesC language represent responses to the execution of a command. For example, when an application executes the command _start_ to enable a radio, it discovers if the radio is turned on through the event _startDone_. Another example is when the application collects a temperature. It executes the command _read_ and receives a value through the event _readDone_ when the temperature is collected. Additionally, there is an event, named _receive_, that depends on the occurrence of an external event. The event _receive_ is triggered when a new message arrives in the sensor node. Unlike the previous events, this event has a probability associated that indicates the chance of its occurrence. Figure 9 presents the CPN model (called module _receive_) of this event.  
The module _receive_ has a transition, named _check_, to decides if it receives a message by generating a random value between 0 and 1 following the uniform distribution. If the generated value is less than or equal to the probability this event occurs, the token moves from place _in_ to _receive_. Otherwise, token moves to _out_. Places _in_ and _out_ are input and output ports, respectively (see Section 2.1.1). The  
Figure 8: CPN models of the command _call_.  
transition _body_ models the command(s) executed in this event. The CPN model of the others events have the places (_in_ and _out_) and the transition _body_.
# Fluoride and Pineal Gland
## 1 Introduction  
The effect of fluoride on the human body is characterized by a very narrow margin of safety, which means that even relatively low concentrations may cause various adverse or even toxic effects [1; 2; 3; 4; 5]. The risk naturally increases with the intensity and duration of the exposure, with long-term exposure resulting in chronic poisoning [6; 7]. One of the defense mechanisms protecting the body against the effects of fluoride toxicity seems to be its deposition in calcified tissues [2]. The most important role is played by hard tissues; bones; and teeth [2; 8; 9; 10], in which fluoride accumulates in the form of fluorohydroxplapatite and fluoroapatite, replacing hydroxyl ions in the hydroxylapatite structure [11; 12]. These processes may occur at any point in life, starting as early as in the prenatal period [13; 14; 15], and their effects are observed even in the skeletons and dentition of archaeological excavations from the times when exposure to fluorine compounds was incomparably lower to modern times [16; 17; 18]. Significantly, the deposition of fluoride in hard tissues may have its own adverse effects. The symptoms of excessive fluoride accumulation in bones and teeth are known and well documented, classified as skeletal fluorosis and dental fluorosis, respectively [19; 20; 21; 22; 23; 24]. In addition to deposition in hard tissues, fluoride may also be found in calcification areas in soft tissues such as the aorta [25; 26; 27; 28; 29], coronary arteries [30; 31], placenta [32; 33; 34; 35; 36; 37; 38; 39; 40; 41], tendons [42; 43; 44], or cartilage [42; 45; 46]. In these cases, however, this accumulation may not be classified as a defense mechanism triggered by an excessive exposure to fluoride. Unlike in hard tissues, calcium accumulation in soft tissues is never a physiological phenomenon and almost always leads to some undesirable effects, e.g., complications in pregnancy [47; 48]. This indicates that the saturation of soft tissues with fluoride is a natural consequence of their calcification. On the other hand, fluoride itself may stimulate the formation of calcification foci in the soft tissues [27; 49], which suggests that fluoride accumulation is the primary phenomenon in calcification. Yet, regardless of the exact mechanisms, concentration of fluoride in the bloodstream, and thus the risk of adverse effects in the body, is reduced as fluoride accumulates in the soft tissues. Obviously, the exceptions to this are the fluoride-accumulating soft tissues; for example, extensive deposits of calcium fluoride in the placenta may impair blood flow through this organ and thus impair fetal nutrition [32; 33; 50; 51].  
One of the most interesting soft tissues able to accumulate fluoride is the pineal gland [1; 52; 53; 54; 55]. However, while knowledge of the calcification of this organ dates back to the 17 th century [56], the first reports on its accumulation of fluoride appeared only in the mid-1990s [54].
# Increasing the Bioactive Compound Content of Olive Oil by Acidification of Olive Paste
## 1 Introduction
erties, making it valuable in addressing inflammatory diseases and hypertension [26; 27]. Due to the multiple health benefits of these secoiridoids, numerous studies have attempted to isolate and concentrate oleacein and oleocanthal from olive oil using various extraction methods [28; 29; 30; 31]. The aim of this study is to produce olive oils with a high content of bioactive phenolic compounds. Olive oils obtained through this process can be marketed as functional foods, offering enhanced health benefits due to the bioactive properties of these phenolic compounds. To achieve this, food-grade organic acids--citric, ascorbic, and acetic acids--will be used to modify enzymatic activity, directing metabolic pathways toward maximizing the content of the desired oleuppein/ligstroside derivatives. Additionally, this study will determine which acid and its optimal concentration yield the best results in terms of extraction efficiency, volatile compound profile, antioxidant capacity, individual secoiridoid content, total secoiridoid concentration, and overall phenolic content. Ascorbic acid, also known as vitamin C, is a water-soluble organic compound that is insoluble in fats and oils. It is classified as a food additive (E300) and exhibits antioxidant properties. Due to its high antioxidant capacity, vitamin C is commonly used in aqueous solutions, oil-in-water emulsions, and oleogels; however, it is not soluble in refined oils or oils devoid of moisture [32; 33; 34]. Citric acid is an organic compound predominantly found in citrus fruits and is responsible for their sour taste. It is water-soluble but insoluble in oils and fats. Although its antioxidant capacity is lower than that of \(\alpha\)-tocopherol, it promotes the synthesis of antioxidant compounds in the body [35]. When added to olive paste, it has been shown to increase the phenolic compound content of the resulting oils by up to 36% [36]. It is classified as a food additive (E330) and is commonly used as an acidulant and flavoring agent. Acetic acid is an organic compound formed through the oxidation of ethanol and is responsible for the characteristic sour taste of vinegar. It is soluble in water but only sparingly soluble in oils and fats. Unlike ascorbic and citric acids, acetic acid does not exhibit direct antioxidant properties. It is classified as food additive E260 and is primarily used as an acidulant and preservative.
## 3 Results and Discussion
### Fracture Surface Analysis  
The fracture morphologies of the FeCrAl alloys under different deformation conditions were investigated using FE-SEM to identify the fracture mode. Figure 7 shows the fracture morphologies of the broken specimens at 200 and 300 \({}^{\circ}\)C. The macroscopic fracture surfaces observed at 200, 300, and 400 \({}^{\circ}\)C (Figure 7a,d,g) show some cracks, indicating a typical brittle fracture characteristic. Upon examining enlarged images taken from the center of the corresponding fractured specimens, a mixed ductile-brittle fracture mode became apparent, characterized by the presence of several small dimples, localized cleavage facets, and cracks. This resulted in low ductility within the temperature range of 200-400 \({}^{\circ}\)C.  
At 500 \({}^{\circ}\)C, the fracture surface predominantly displayed a ductile fracture characterized by a significant proportion of dimples, accompanied by only a few microcracks with lengths of approximately 30 \(\upmu\)m, as illustrated in Figure 8.  
As the temperature rose to 600 \({}^{\circ}\)C, the specimens exhibited a complete ductile fracture mode, characterized by a marked reduction in area and the presence of dimples, as depicted  
Figure 8: (**a**–**c**) SEM pictures of the fracture surfaces of specimens at 500 \({}^{\circ}\)C.  
Figure 7: SEM pictures of the fracture surfaces of specimens at (**a**–**c**) 200 \({}^{\circ}\)C, (**d**–**f**) 300 \({}^{\circ}\)C, and (**g**–**i**) 400 \({}^{\circ}\)C.  
in Figure 9a,b. With a further increase in temperature to 700 and 800 \({}^{\circ}\)C, the reduction in area notably diminished, and numerous large, deep dimples became apparent at the center of the fracture surfaces, as illustrated in Figure 9c-f. These results suggest that the FeCrAl alloy specimens tested at 700 and 800 \({}^{\circ}\)C exhibit greater ductility compared to those tested at lower deformation temperatures. This is because the formation of larger and deeper dimples consumes more plastic strain and energy. Furthermore, within the temperature range of 600-800 \({}^{\circ}\)C, no cleavage fracture feature was observed on the fracture surfaces, indicating that the fracture mode transitioned from a mixed ductile-brittle fracture to a purely ductile fracture as the deformation temperature increased. This transition also aligns with the temperature-dependent trends observed in the reduction in area and strain hardening exponent. Specifically, when the temperature reached 600 \({}^{\circ}\)C or above, the reduction in area increased significantly, while the strain hardening exponent decreased markedly, dropping to negative values at temperatures of 700 and 800 \({}^{\circ}\)C. Therefore, a combined analysis of the notable changes in the reduction in area and strain hardening coefficient can aid in determining the shift in fracture mode.
## 5 Conclusions  
In this study, a semi-analytical load distribution model was proposed based on a three-dimensional and linear elastic solution. Different types of tooth profile and longitudinal modifications of cycloid gears were considered in this model to simulate and predict the multi-tooth contact conditions of cycloid drives. Numerical examples of load distribution analysis of cycloid drives using the proposed model were presented and compared to the current Hertz contact stiffness-based model to demonstrate the effectiveness of the predictions of this semi-analytical model. From the analysis results, the following specific observations and conclusions can be drawn:  
1. Load distribution, the number of tooth pairs in contact, and the loaded transmission error are influenced significantly by different types of tooth profile modification having the same radial clearance. NNM tends to yield smaller LTE than PPM, while the contact stress is quite the contrary. Both of them need to be optimized with proper modification parameters for the best performance of the cycloid drive.  
2. Tooth longitudinal modifications play a positive role in relieving the stress concentration at the two edges of cycloid gears and improving stress distribution along the axial direction, which should be considered seriously in the analysis and design of cycloid drives. Among the three proposed types of TLM, LCM exhibits better stress distribution characteristics, and further research is needed on the determination of modification parameters.  
Figure 14: The peak-to-peak values of loaded transmission error (LTE) and gear ratio (GR) under applied torque values of 50, 150, 250, 350, 450, and 550 Nm.  
3. Applied torque levels were observed to have significant effects on the contact stress, LTE, and GR. The magnitudes of their maximum, peak-to-peak values change proportionally with torque. The contact pattern extends towards the edges of the tooth as the torque increases.  
This proposed model can be applied to determine the limitation of the applied torque to prevent the contact stress exceeding the elastic limit of the gear material. Furthermore, due to the model's better computational efficiency, it is very suitable for parameter sensitivity studies to optimize the design of cycloid drives. In the future, an experimental study will be carried out to validate the proposed model.  
Conceptualization, X.L. and T.Z.; Methodology, X.L.; Validation, X.L. and T.Z.; Analysis, X.L. and L.S.; Writing--Original Draft Preparation X.L. and Y.W.; Writing--Review and Editing, X.L. and Y.W.; Supervision, X.L. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China (Grant No. 2018 YFB1309200), China Postdoctoral Science Foundation (Grant No. 2018M640516), and Basic Research Program of Jiangsu Province (Grant No. BK20190824).  
The authors declare no conflict of interest.
## 6 Key Findings  
### Experimental Results  
After performing the theoretical and practical analyses described this article, several key findings can be highlighted:  
* The read and write operation latency of the proposed decentralized system can be really low, in the same order as similar centralized applications but providing higher levels of cybersecurity. This fact indicates that applications for scenarios where low response times are not critical can benefit from the use of blockchain, since the difference in terms of user experience is negligible.
* On the same conditions, the use of PoA significantly increases TPS with respect to the use of PoW. Therefore, the use of PoA includes benefits in terms of efficiency, energy consumption and speed.
* Setting an appropriate gas limit is essential when it comes to determine the blockchain behavior. As it was observed in the previously described experiments, increasing the gas limit significantly increases TPS, since blocks can include more transactions. However, this requires nodes to have more computational resources to verify these transactions, and it would take longer to propagate blocks through the network (i.e., more overhead would be involved). Therefore, resource-constrained devices would not be able to validate blocks on time, losing the capacity to keep the network synchronized and eventually leading to centralization. According to the results of the experiments presented in this article, both for low and high gas limits, and even with high transaction loads (e.g., 250 transactions per second is considered a high load for the application described in this paper), the system maintained acceptable request rates. Therefore, low gas limits are effective in terms of performance and increase decentralization, which is essential when using consensus protocols such as PoA.  
* With respect to CPU usage and CPU power consumption, the behavior of PoW substantiates the one described in [98], where the plateau of PoW is associated with the constant mining process for securing the blockchain. In any case, it must be noted that the previously described CPU usage and power consumption results are actually unlikely in practice, since the simulated workloads (i.e., a progressive load that ranged progressively from 100 to 1000 transactions) were aimed at testing the performance of the system rather than to recreate a realistic scenario for the application described in this article.
* As for the deployment in SBC devices, its use can be beneficial for the greater proximity to the end users. The experiments performed confirm that the use of SBCs is viable for implementing both PoA and PoW-based blockchain nodes, but it is necessary to consider the limitations of such devices in terms of computational power to make proper use of the system and to not to degrade their performance. Specifically, the experiments showed that, to provide an adequate performance level, devices required at least 1 GB of RAM (combined with at least 1 GB of swap memory) together with a CPU with at least four cores of a relatively powerful ARM processor.