esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### GPP-ET Relationship Based on \(\text{WUE}_{\text{dt}}\) and Coupling Strength \(r\)  
Daily \(\text{WUE}_{\text{dt}}\) and the coupling correlation \(r\) of hourly ET and GPP both can reflect the C-W coupling but from different perspectives. As shown in Figure 2, the daily variation of \(\text{WUE}_{\text{dt}}\) and \(r\) differs greatly. For example, with similar WUEs at the beginning of July, the C-W coupling strength can range from moderately coupled (around 0.5) to highly coupled (around 0.9). Based on the definition of these two terms, we further explored the relationship between GPP and ET at both a daily and hourly scale from \(\text{WUE}_{\text{dt}}\) and \(r\), respectively.  
Figure 9 shows the relationship between daily GPP and ET for summer no-rain days based on different values of \(\text{WUE}_{\text{dt}}\). The slope of the GPP-ET relationship can be regarded as a reflection of the ecosystem WUE [16]. Based on all available data, the slope of GPP-ET was larger in the broadleaved forest (3.19 g \(\text{CO}_{2}\)/kg \(\text{H}_{2}\)O) than in the coniferous forest (2.0 g \(\text{CO}_{2}\)/kg \(\text{H}_{2}\)O), but this does not mean that \(\text{WUE}_{\text{dt}}\) is larger in the broadleaved forest. Actually, in the coniferous forest at Qianyanzhou, the small slope of all available data is due to its large day-to-day fluctuation in the GPP-ET relationship. This may be the result of larger variations in environmental conditions at this site (Figure 1). The slope in the coniferous forest site can be up to 16.47 g \(\text{CO}_{2}\)/kg \(\text{H}_{2}\)O. Under certain meteorological conditions (small SR, Ta, and VPD), a large GPP can be generated at a low cost of water. For the broadleaved forest, the difference in the slopes among different values of \(\text{WUE}_{\text{dt}}\) is small. \(\text{WUE}_{\text{dt}}\) reflects the relationship between GPP and ET on a daily scale. Daily GPP and ET are strongly coupled when \(\text{WUE}_{\text{dt}}\) is at a comparable level.  
On an hourly scale, GPP and ET are not strongly coupled all the time at both forest sites, as shown in Figures 10 and 11. Overall, the R\({}^{2}\) is 0.33 for broadleaved forest and only 0.19 for coniferous forest. In contrast to the daily WUE, C-W coupling can depict the  
Figure 9: The daily GPP and ET relationship in the broadleaved forest at Dinghushan (**a**) and in the coniferous forest at Qianyanzhou (**b**) with different values of \(\text{WUE}_{\text{dt}}\) in colors. The slope of each regression line is listed in different colors. All values are on summer no-rain days over 2003–2005.  
relationship between GPP and ET on a sub-daily scale well. For both broadleaved and needleleaf forests, with high C-W coupling strength, sub-daily variations in GPP and ET are highly synchronized, while with a small C-W coupling strength, hourly GPP and ET are distributed randomly.
# Positively Charged Lipid as Potential Tool to Influence the Fate of Ethosomes
## 3 Results and Discussion
### In Vivo Human Skin Tolerability Test  
The in vivo tolerability of the newly realized systems was tested, monitoring the variation of erythema index values before and following the treatment with ethosomal formulations (\(\Delta\)E.I). The obtained values were compared with those recorded following the administration of conventional ethosomes or the administration of saline solution (0.9% NaCl), used as control. The study was performed on six healthy human volunteers over 48 h and the results are reported as the mean of the values recorded \(\pm\) standard deviation.  
As can be seen in Figure 5, six hours after the administration of the samples (both conventional ethosomes and cationic formulations), a slight increase in \(\Delta\)E.I. values was recorded. Although the increase was slight, it was significant (\(p<0.05\)) if compared to values recorded for the administration of saline solution. This increase in E.I. value was probably related to the permeating ability of ethosomal systems through skin and was consistent with data reported in another study [16]. In particular, the amount of ethanol, which remained unchanged between conventional and cationic ethosomes, probably caused a temporary and reversible modification of the main skin barrier, leading to increasing values of E.I. in the first 6 h. After 24 h values of erythema index were in line with that recorded from saline solution, used as control, meaning that the erythema resolved. Actually, the initial increase of erythema index values due to a reorganization of the skin barrier structure represents an attractive property as this effect can accompany the permeation of any delivered drug through skin layers.  
The results obtained from in vivo studies showed suitable levels of cutaneous tolerability and safety of the new realized cationic systems, thus confirming they represent promising systems for topical skin delivery.  
Figure 5: Variation of erythema index (\(\Delta\)EI) over 6, 24 and 48 h of treatment with saline solution, formulation A, formulation D and formulation I. Results are expressed as the mean value of six independent experiments \(\pm\) standard deviation. * \(p\) value < 0.05 compared to saline solution, used as control.
## 3 Case Analysis
### Resilience Measurement  
Inviting the aforementioned five experts to evaluate and obtain the resilience scores for each indicator. Calculated based on the principles described in Formulas (1)-(19) in Section 2.2. The arithmetic mean of the experts' scores is used as \(t_{ij}\) to substitute into the uncertain measurement function, resulting in the single indicator measurement vector \(\mu_{ij}=\left(\mu_{\bar{ij}1},\mu_{\bar{ij}2},\ldots,\mu_{\bar{ij}5}\right)\) composed of each factor's single indicator measurement. \(\mu_{i\bar{jk}(4\times 5)}\) calculates the indicator weights and computes the uncertain measurement vector of \(A_{i}\). UseFormulas (20)-(23) to calculate the index weights and determine the uncertainty measure vector for \(A_{i}\).  
The uncertain measurement vector of A\({}_{1}\)  
\[\begin{split}&\mu_{1}=w_{1}\times(\mu_{1})_{4\times 5}=\left( \begin{array}{c}w_{11}\\ w_{12}\\ w_{13}\\ w_{14}\end{array}\right)^{T}\times\left(\begin{array}{c}\mu_{11}\\ \mu_{12}\\ \mu_{13}\\ \mu_{14}\end{array}\right)\\ &=\left(\begin{array}{c}0.233\\ 0.274\\ 0.28\\ 0.245\end{array}\right)\times\left(\begin{array}{c}0\\ 0\\ 0\\ 0.19\\ 0.01\\ 0\\ 0\\ 0\end{array}\begin{array}{c}0.024\\ 0.08\\ 0.88\\ 0.81\\ 0.20\\ 0.2\end{array}\right)\\ \end{split} \tag{28}\]  
Similarly, we obtain the uncertain measurement vectors and weight vectors for A\({}_{2}\) and A\({}_{3}\), which are filled in Table 3.  
The final calculation of the comprehensive uncertainty vector for target layer A is as follows.  
\[\begin{split}&\mu=w\times(\mu)_{3\times 5}=\left( \begin{array}{c}w_{1}\\ w_{2}\\ w_{3}\end{array}\right)^{T}\times\left(\begin{array}{c}\mu_{1}\\ \mu_{2}\\ \mu_{3}\end{array}\right)\\ &=\left(\begin{array}{c}0.217\\ 0.388\\ 0.395\end{array}\right)\times\left(\begin{array}{c}0.047\\ 0\\ 0.054\end{array}\begin{array}{c}0.201\\ 0.467\\ 0.465\end{array}\begin{array}{c}0.346\\ 0.549\\ 0.481\end{array}\begin{array}{c}0.406\\ 0\\ 0\\ 0\end{array}\right)\end{split} \tag{29}\]  
Set the confidence threshold \(\lambda=0.7\). The probability that the sampled value of the random variable falls within one standard deviation is 68.3%, so \(\lambda\) is generally taken as 0.6 to 0.7 [47], which indicates a high reliability of the evaluation results. According to the confidence determination Formula (24), by substituting the threshold \(\lambda\), we obtain Formula (30).  
\[k_{0}=\underset{k}{\text{min}}\left(\sum_{k=1}^{p}\mu_{ik}\geq 0.7,p=1,2, \ldots,5\right) \tag{30}\]  
Because \(\mu_{1}+\mu_{2}+\mu_{3}=0.032+0.253+0.478=0.763>0.7\), \(k=3\) has moderate resilience.
## 2 System Architecture
### Equipment Used and Specifications  
The methodology behind designing the experiment is based on the following principles for the selection of the sensors: they should be low-cost, low-power, and widely supported sensors. The selection of the communication protocol is based on energy efficiency, and therefore BLE was selected and not ZigBee, which is best used on mesh networking rather than point-to-point communications. More specifically, the sensors were chosen for their simplicity, low power usage, and sufficient accuracy for basic environmental monitoring. The chosen communication protocol (BLE) was selected for its short-range and energy-efficient communication between the ESP32 and Raspberry Pi. The Raspberry Pi supports reliable cloud connectivity with the support of BLE and the 5G network, which offers high bandwidth with low latency, ideal for IoT systems. The data transmission of a 10 s timeframe using an encoded string of T34H46 (temperature and humidity) ensured real-time updates based on ThingSpeak limitations and in parallel we minimized the energy use. Power consumption was measured using a USB power meter and iperf3 based on  
Figure 5: The 5G module used in the experiments.  
realistic load scenarios with solar panel testing for feasibility for sustainability. The MQTT protocol was chosen as a future upgrade for better scalability and network flexibility.  
Tables 1 and 2 demonstrate the essential hardware chosen for the gateway and sensing stack. For each item, they summarize key specifications, such as the processor type and speed, memory, I/O ports, wireless and wired connectivity, power requirements, and, where applicable, peak data-rate figures.
# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 4 Experiments and Results
### Analysis of Experimental Results in Case of Strong Interference  
The simulation effect of adding the controller to control the attitude angle, as well as the position information, in the case of strong interference is shown in Figure 6a-f.  
Figure 5: Experimental results with weak interference.  
Figure 6a-f show the attitude angle and position information results. The three controllers revealed a large difference. In the attitude angle graph, this can be seen very intuitively. Compared with the addition of the weak interference, with the addition of the strong interference, the UAV with the three different controllers is affected. The SMC and the FO-SMC receive a more serious impact in the attitude angle graph, while the FOSM-ADRC proposed in this paper is also affected, but the attitude angle control is relatively stable, and no divergence occurs. In the position diagrams, the comparison of the control effect of the three controllers is more obvious, especially in the Z-axis diagram in Figure 6f, where the larger vibrations of the SMC and the FO-SMC occur in the undispersed stage, but the FOSM-ADRC proposed in this paper is still relatively stable. Overall, the normal SMC loses control at about 24 s and disperses immediately, and although the FO-SMC also loses control at a similar time, it shows some signs of retracing within a short period and exhibits a faster response speed and a stronger anti-interference capability compared to the normal SMC. In addition, the FOSM-ADRC proposed in this paper is still relatively stable in this strong interference situation. Only in the simulation image, there is a little jitter, but it never loses control, and there is no dispersion, so it can be seen that the controller proposed in this paper exhibits strong anti-interference performance.
## 2 Materials and Methods
### lidar Data  
Lidar measurements were conducted with Leosphere WindCube v1 and v2 CWDL lidars, which can be set up to measure at up to twelve arbitrary height levels. The focal points of the lidar systems were set to 140 m. For this study, time series data from 25 sites (for locations see Figure 2), each with twelve consecutive measuring months, were available. The data for each site contains availability values in percent with a frequency of 10 min for multiple measurement heights up to 200 m. Overall, the dataset was comprised of over 5.3 million data points. All sites are situated in Germany at elevations ranging from 61 m to 703 m a.s.l. (see Figure 3 for height distribution). The availability, henceforth called the 10-min availability, is defined as the percentage of values with a CNR value above the threshold of \(-22\) dB for the respective ten minute interval [18; 26].  
Figure 1: Example scene of cloud base height predicted by the HYFOG algorithm for 15 Junuary 2015 06:00 UTC.
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 5 PA-Based Communication and Localization
### PA Communication Challenges  
The nonlinear PA is considered the most efficient method to generate acoustic signals in an underwater environment [68]. However, using the nonlinear PA causes generation of vapor clouds in the underwater environment due to the heating effect. These vapor clouds prohibit further generation of acoustic signals at the same location until the cloud decimates in the water [81]. Such vapor cloud decimation poses a theoretical bound on the speed of generating acoustic signal when nonlinear PA techniques are used and produces an upper bound on the achievable communication bitrate. To evade the vapor cloud, [PERSON] et al. [68] suggest using a scanning laser that focuses the laser beam at different points in a region to avoid possible presence of vapor clouds.  
Another key issue is that the photoacoustic effect is hybrid by nature. While a high energy light signal is used to transmit encoded data from the transmitter side, at the receiver side, an acoustic signal is received. This hybrid nature makes it very difficult to come up with a suitable modulation/demodulation or encoding/decoding scheme. Moreover, the generated acoustic signal in a PA process is highly directional, as we have discussed in Section 3. One needs to factor such directionality to make a successful communication. By controlling the shape of the plasma, the directionality of the acoustic beam may be controlled. [PERSON], et al., [82] have explored the PA effect and studied the shape of the generated underwater acoustic beam. They have found that the directivity of acoustic signal is concentrated on the plane perpendicular to the laser transmission direction.
## 2 Experimental Setup and Procedure
### Uncertainty and Error  
The test status and data collection are affected by various factors, such as human operation errors, instrument errors, fluctuations caused by environmental factors, etc. Therefore, each parameter in the test was measured three times, and the average value was taken as the official one in order to obtain more accurate results. The measurement accuracy of the main parameters involved is shown in Table 4.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model  
The distribution of satellites determines the coverage performance of the constellation [37], and the more uniformly the satellites are scattered across the celestial sphere, the better for rapid coverage over large areas. The most widely used Walker constellation covers all the longitudes uniformly with time but suffers from a significant problem of uneven coverage on the latitude zones. This shortcoming will be even more pronounced when the number of satellites is large.  
Aiming at designing a mega-constellation capable of achieving uniform global coverage of the ground, it is first necessary to define a metric that measures the density of the constellation's sub-satellite points over the latitudinal zones. Furthermore, the Walker constellation can be used as the basic unit of a mega-constellation, taking advantage of its symmetry and uniform coverage of all longitudes. In addition, the latitudinal coverage is restricted by the orbital inclination of the satellites within the constellation. A mixed Walker constellation configuration with multiple inclinations is required to provide balanced coverage of the various latitudinal zones.  
The mega-constellation configuration design in this paper can be summarized as follows: Using the Walker constellation as the basic unit, the density of the sub-satellite points on the latitude zone is adopted as an indicator, and sub-constellations with different orbital inclinations are generated in successive iterations. At the same time, the initial orbital planes of the sub-constellations are evenly spaced on the equator to ensure that the sub-satellite points are sufficiently uniform in the longitudinal direction. We have acquired a preliminary constellation configuration up to this point. Then, the collision-avoidance constraint is established according to the relative position between satellites, and the orbit parameters of some satellites with high collision risk are adjusted. The final constellation configuration is derived.
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 3 Proposed Method SOC Estimation  
### Lithium Battery SOC Estimation Algorithm  
In this paper, the lithium battery pack used in the experiment is composed of four cells. The schematic diagram of the proposed State of Charge (SOC) estimation method for the lithium battery pack using neural networks is shown in Figure 2.  
Power supply and electronic load are used to charge and discharge lithium batteries. The battery pack is composed of four cells, and one cell is consists of a 1300-mAh lithium battery. The battery controller consists of a voltage sensor, current sensor, temperature sensor, microcontroller, and eight-channel relay module. The battery controller is used to measure the voltage, current, and temperature variable data of the battery. Subsequently, the measured data is sent to a personal computer (PC) and then evaluated using neural networks to enable SOC estimation of the lithium battery.
## 2 Materials and Methods
### Determination of Prebiotic Activity  
#### 2.9.1 Microorganisms  
The strains used to evaluate the potential prebiotic activity of the extracts were _Lactobacillus acidophilus_ Ki, _Lactobacillus casei_ 01, _Lactobacillus rhamnosus_ R11, _Bifidobacterium lactis_ BB12, and _Bifidobacterium lactis_ BLC1. _Lacidophilus_ Ki was obtained from CSK (Leeuwarden, The Netherlands); _L. casei_ 01 and _B. lactis_ BB12 were obtained from [PERSON] (Hersholm, Denmark); _L. rhamnosus_ R11 was provided by Lallemand (Montreal, QC, Canada); and _B. lactis_ BLC was obtained from DSM (Moorebank, Sydney, NSW, Australia). The microorganisms were reactivated, and pre-cultures were prepared in de Man-Rogosa-Sharpe (MRS, Biokar Diagnostics, Allonne, France) broth and incubated overnight at 37 \({}^{\circ}\)C. In case of _B. lactis_ BB12 and _B. lactis_ BLC, MRS was supplemented with 0.5 g L\({}^{-1}\) of filter-sterilized L-cysteine-HCl (Fluka, Buchs, Switzerland) to lower the redox potential.
## 2 Background and Related Work
### Educational Mobile Games
The design also requires participants to have expertise in the computer field, which may limit the scope of users when it may be required for knowledge delivery in other groups. [PERSON] et al. [21] designed a game to engage users in an educational game to combat phishing. However, participants found the game difficult because it does not consider cultural differences. The contents used were unfamiliar to most of the participants, and hence it made it difficult for the users to play and acquire the intended knowledge. The limitations of the previous work are summarized in Table 1.  
Currently, despite the multiple studies mentioned above, only a handful of them have begun to investigate the role of younger generations in phishing. However, most studies have not taken into account phishing and cybersecurity awareness due to social-economic and cultural variations among teenagers. Furthermore, just a little research has examined the game's performance in terms of knowledge transfer, and most of the studies above do not include knowledge retention. As a result, the objective of this study was to create a game that addressed knowledge differences between teenagers based on differences in their socioeconomic and cultural backgrounds. The game will be available for free download from the Google Play Store for social reasons. Because Android phones are very inexpensive and accessible to most people, participants in this survey opted to play the game on them (59%). Additionally, the game is structured to take into account the African setting of social culture, including the environment and objects used, based on things that participants are familiar with. To make learning easier for participants, the game was created with African characters and African-themed environments. Most teenagers (99% of males and 94% of females) play video games [28] and are said to spend more than 20 h a week doing so [29]. As a result, it is thought that learning through mobile games would be easier than learning through traditional means such as books, notes, and lectures. Furthermore, due to its advantages in retaining knowledge longer than other traditional techniques, the game can teach phishing and other cybersecurity topics to people of various ages.
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 3 BDS-3 Integrity Failure Model and Effect Analysis Algorithm
### BDS-3 Integrity Risk Factors Analysis
#### 3.3.3 Integrity Risk Factor Analysis  
Based on the system design and IFMEA results, abnormal events are analyzed, including the anomaly mode, the influence mode of anomalies on the SISRE, spatial correlation, and receiver detectability. The BDS-3 integrity risk factor analysis table is obtained, as shown in Table 3.
## 4 Numerical Analysis and Failure Mechanism of a Single-Crack Specim
### Failure Mechanism of a Closed Pre-Crack Concrete Sample  
The mode-I fracture toughness of brittle material is lower than the mode-II fracture toughness. The maximum dimensionless mode-I and mode-II stress intensity factors are defined as \(f_{\rm{6 max}}=K_{\rm{Ie}}/\sigma_{y}\sqrt{\pi a}\) and \(f_{\rm{r\theta max}}=K_{\rm{Ie}}/\sigma_{y}\sqrt{\pi a}\), respectively. When \(f_{\rm{r\theta max}}/f_{\rm{\theta max}}<1\) or \(f_{\rm{r\theta max}}/f_{\rm{\theta max}}<K_{\rm{IIC}}/K_{\rm{IC}}\), the crack will expand according to the mode-I fracture pattern. If \(f_{\rm{r\theta max}}/f_{\rm{\theta max}}>1\) and \(f_{\rm{r\theta max}}/f_{\rm{\theta max}}>K_{\rm{IIC}}/K_{\rm{IC}}\) the fracture will follow a mode-II fracture pattern. For a 0.5 mm wide crack, \(K_{\rm{I}}\) = 0, the dimensionless stress intensity factor is shown in Figure 8, where \(f_{\rm{r\theta max}}/f_{\rm{\theta max}}<1\). Thus, mode-I fracture occurred, and the angle of \(f_{\rm{\theta max}}\) was 70.5\({}^{\circ}\) based on the maximum circumferential stress theory.  
Figure 7: Fracture propagation numerical simulation of single joint-fissured concrete sample where the single crack thickness is 0.5 mm. (**a**) \(\beta\) = 15\({}^{\circ}\); (**b**) \(\beta\) = 30\({}^{\circ}\); (**c**) \(\beta\) = 45\({}^{\circ}\); (**d**) \(\beta\) = 60\({}^{\circ}\); (**e**) \(\beta\) = 75\({}^{\circ}\).  
The maximum circumferential stress at the crack tip is greater than the maximum radial shear stress for specimens 1-5 (Table 5). The crack propagation angles for the specimens obtained from the numerical and experimental results are close to those obtained from the maximum circumferential stress (Table 6).