esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Future Work  
Despite the benefits of the proposed model in the routing process, a few challenging issues that need to be addressed are discussed here. The proposed model uses a random distribution of nodes in the network, which poses some demerits such as uneven node distribution, uneven coverage, limited range, resource consumption, and complex network management. In addition, in a connection-based mesh topology, every node acts as a relay or router, transmitting data between other nodes in the network. This type of topology  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Delay (ms)** & & & & & & \\ \hline
**Pause Time** & **HMSFO-EARS** & **NSSROP** & **PRRP** & **PDORP** & **DSR** & **LEACH** \\ \hline
1 & 1.47 & 4.20 & 5.85 & 9.84 & 26.20 & 21.72 \\ \hline
2 & 1.67 & 4.20 & 6.53 & 9.75 & 26.01 & 22.40 \\ \hline
3 & 2.15 & 4.20 & 6.92 & 10.82 & 24.93 & 22.50 \\ \hline
4 & 1.86 & 4.59 & 6.34 & 10.72 & 24.93 & 23.77 \\ \hline
5 & 1.47 & 3.81 & 4.98 & 11.21 & 24.06 & 24.16 \\ \hline
6 & 1.76 & 3.42 & 5.27 & 11.60 & 22.89 & 25.52 \\ \hline
7 & 1.28 & 3.71 & 5.17 & 12.47 & 22.89 & 25.52 \\ \hline
8 & 1.28 & 2.74 & 4.68 & 12.47 & 22.70 & 25.42 \\ \hline
9 & 1.97 & 3.03 & 4.59 & 12.47 & 23.86 & 25.03 \\ \hline
10 & 2.15 & 3.81 & 4.88 & 11.99 & 22.99 & 25.23 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Delay analysis of HMSFO-EARS system with existing approaches under different pause times.  
Figure 6: Delay analysis of HMSFO-EARS system under different pause times.  
can be used to create a highly redundant and fault-tolerant network, as data can be routed through multiple paths in the network if one path fails. As the number of nodes in the network increases, the number of connections and routing tables required can become overwhelming. It makes it complex to scale the network, particularly if new nodes need to be added frequently. On the other hand, in many cases, the connections in a mesh topology are dynamic and can vary depending upon node movement, topology changes, or routing demands. Dynamic connections in a mesh network can provide greater flexibility and adaptability, allowing the network to reconfigure itself in response to changing conditions. However, this can also increase the complexity of the network, as nodes must constantly monitor and update their connections and routing tables. In DSR, the source node of a data packet determines the complete route of the packet and includes this information in the packet header. While DSR can provide an efficient and flexible way to route data in the networks, it can also result in increased energy consumption at the source node. This is because the source node must actively participate in the routing process by selecting the appropriate route and including this information in the packet header. This can require significant processing and communication resources, which can increase energy consumption and reduce the battery life of the source node.
## 4 ME-SSA Receiver Design
### Preamble Searcher
#### 4.2.2 Non-Coherent PS  
In the LEO (or MEO) case, coherent PS algorithms are not suitable for the following two reasons.  
Firstly, an excessive Doppler shift leads to a large number of frequency hypotheses, considering the coherent integration time that is equivalent to the preamble duration. For instance, considering a LEO satellite at 500 km altitude and satellite visibility at a low elevation angle (e.g., about 10\({}^{\circ}\)), the maximum Doppler shift is about 36 kHz at 1.5 GHz carrier frequency. Hence, for the MASSIVE waveform, the number of frequency hypothesis can range between 1858 and 29,729, depending on the spreading factor. The search space could be reduced by limiting the lower limit of the elevation angle. For instance, for elevation angle values above 55\({}^{\circ}\), the maximum Doppler is about 20 kHz, that reduces the number of hypotheses; however, the size of the frequency hypothesis space would still be high.  
Secondly, an excessive Doppler rate \(\alpha\), for example up to about 400 Hz/s for a 1.5 gHz carrier frequency and 500 km altitude LEO satellite, would degrade the performance of the coherent PS algorithm. The unknown value of the Doppler rate at the receiver causes a considerable phase rotation among the symbols involved in the coherent integration. Depending on the ratio between coherent integration time and Doppler rate, such rotation can yield a destructive sum and a consequent loss of performance, analogous to the effect of the correlation loss.  
Figure 5: Coherent PS.  
To mitigate the above-mentioned issues, one solution is to resort to a non-coherent DPDI approach [14] as depicted in Figure 6. This approach reduces the coherent integration time \(T_{\text{coh}}\) by a factor of \(N_{c}\) compared to the coherent PS solution. For smaller values of \(T_{\text{coh}}\), the number of frequency hypotheses will also be reduced, hence making the solution more computationally manageable. Furthermore, the PS becomes robust to the worst-case Doppler rate, since the phase rotation across the symbols in a coherent integration time can be reduced to less than \(2^{\circ}\).
## 3 Results
### Comparison with Different Univariate Models  
Table 2 demonstrates the comparison between the four models using univariate inputs for each site. A comparison of the overall predictive effectiveness of the four univariate models for the 12 sites is shown in Figures 6 and 7. Figures 6 and 7 illustrate that the TF-BiLSTM-attention model for the 12 sites has the smallest mean value of RMSE and the mean value of MAE, and the mean value of R\({}^{2}\) is the largest. This indicates that the TF-BiLSTM-attention model performs the best among the four models. The difference of the forecast effect between the TF-BiLSTM model and the TF-BiLSTM-attention model is very small. The difference of the forecast effect between the BiLSTM model and the BiLSTM-attention model is also very small. However, incorporating the attention mechanism renders an improvement over the model lacking this feature, which indicates that in the case of univariate input, the attention mechanism does not improve the prediction effect of the model significantly. Finally, the prediction effect of the TF-BiLSTM model is significantly better than that of the BiLSTM model, which indicates that adding frequency information to the model can improve the prediction effect of the model. Meanwhile, it can be found from Table 2 that the same model has different prediction effects for different sites. Taking the univariate TF-BiLSTM-attention model, which has the best performance in general, as an example, site S12 has the worst performance, site S3 has the smallest value of RMSE, site S7 has the smallest value of MAE, site S10 has the largest value of R\({}^{2}\), and site S6 has the smallest value of MAPE.
# Seismic Assessment of Historical Masonry Buildings at Different Scale Levels: A Review
## 2 Large-Scale Approaches
### Empirical Methods
#### 2.1.2 Fragility Curves Methods  
The fragility curve methods overcome the DPM's limit, represented by the discretization of damage states and the following need for lots of data that are not always available. A continuous approach is adopted to characterise the structure's damage for any earthquake intensity, evaluating the probability of exceeding a certain damage state ds according to increasing values of a seismic intensity-related parameter (e.g., spectral displacement Sd) [58]. The final output is a fragility--or vulnerability--curve correlating the mean damage grade with the earthquake intensity. As for DPM, the fragility curves method has limited applicability since the curves should be used in the same region where they are defined and based on a consistent database. The curves depend on the building features, e.g., construction techniques, types of manpower, materials, etc., and a relevant amount of data is needed to properly correlate observed damages with expected ones [59].  
The fragility curve method initially experienced some difficulties in matching the continuous nature of the method with the discrete nature of the macroseismic intensity measurements of that time [10]. [PERSON], [PERSON] [22] elaborated the Parameterless Scale of Intensity (PSI), which is able to derive vulnerability functions starting from the damage observation using the MSK scale. The PSI was also used by [PERSON] [24] to analyse areas stricken by the Irpinia earthquake, correlating PSI and PGA through empirical functions and focusing on the flat scale. Other examples applied at the Italian level were proposed by [PERSON], [PERSON] [23] and [PERSON], [PERSON] [25]. The first defined fragility curves as a function of PGA, Arias Intensity and effective peak acceleration [27], while the latter processed observational damage probability matrices to obtain lognormal typological fragility curves relating, for five damage states, the damage probability to the mean PGA referred to the building's municipality.  
PSI's employment did not find a wide application in the field of historical clusters, since based on empirical data, a large sample size was assumed [46].  
Fragility curves can even be defined with reference to spectral acceleration or spectral displacement instead of PGA or macroseismic intensity [28; 29; 30]. If spectral displacement is used, relationships can be achieved between the frequency content of the ground motion and the fundamental period of vibration of the building stock.  
Since fragility curves should be determined and used with reference to the same region, over the years they have been developed with reference to different places and building typologies: to cite a few of them, [PERSON], [PERSON] [26], [PERSON], [PERSON] [31], [PERSON], [PERSON] [32], [PERSON], [PERSON] [33] and [PERSON], [PERSON] [11]. Nevertheless,as for DPM methods, their use for analysing buildings as variable as the historical ones should be carefully evaluated and preceded by a specific study in the area of interest.
# Real-Time Model Predictive Control of Human Bodyweight Based on Energy Intake
## 3 Results and Discussion
### Simulation Model Predictive Control
No information on the ratio of carbohydrates, lipids and proteins with respect to the total energy intake is provided. In addition to the total energy intake, further optimization of the MPC algorithm can include suggestions on the ratio of carbohydrates, lipids and proteins based on the recommended dietary allowances. An important factor influencing the dietary behavior is the degree of saturation. Food with a high energy density usually ensures a low degree of satiety. Therefore, food with a low energy density is usually recommended, as it provides a longer period of satiety [52]. In [12], it is shown that combining current methods to deal with bodyweight changes with mHealth applications may enhance the performance of the first ones. Therefore, on the one hand, the current applications may benefit from including such a MPC as developed in this work. By providing more individual accurate estimations of energy intake and bodyweight change levels, it is expected to enhance the engagement to the mHealth technology and the adhesion to its suggestions, improving weight management [33]. On the other hand, if further development of apps and wearables enhance the characterization of the energy intake, physical activity and other related physiological variables, the MPC developed in this work can be extended to manage more accurately the metabolization and energy flux balances processes. In such a situation, a comparison with the existing mechanistic models would allow extracting more biological and physical information while keeping the real-time and automatic properties of the controller. To sum up, it can be seen that such a MPC system may help people to manage and control their bodyweight in real time. Taking advantage of the new possibilities offered by wearables and smartphones, an individual can gather real-time information about their energy intake, bodyweight and activity. Then, mHealth applications may exploit such compact and accurate MPC algorithms to manage body weight change taking advantage of the data gathered. This enables a target bodyweight or bodyweight reference curve to be defined and suggestions for the energy intake needed to achieve this goal to be made. Besides, the MPC system may be a useful tool for dietitians as well. They can develop theoretical bodyweight curves for losing weight using biological and medical information and simulate the energy intake suggested to follow this curve at individual patient level as close as possible. Moreover, at certain points in the treatment, this theoretical curve can be adapted according to the performance of the patient and medical considerations. Thus, the MPC system can be used not only to simulate an energy intake pattern according to a predefined bodyweight curve, but also to monitor the impact of the strategy in individual patients and adapt the strategy accordingly throughout the bodyweight change process.
## 1 Introduction
### Paper Layout  
Figure 1 presents a flowchart depicting the layout of the paper. In Section 2, small-scale laboratory testing and its limitations in replicating full-scale physics are delved into, to address the challenges associated with wall-bounded studies, highlighting the need for alternative approaches. Section 3 then introduces the concept of open-jet testing, emphasizing its potential advantages in replicating real-world conditions. Building upon this foundation,Section 4 offers a detailed exploration of large-scale open-jet testing applied to low-rise buildings, providing insight into methodologies and findings. Shifting focus, Section 5 examines the resilience of roof-mounted solar panels under high wind loads, covering wind effects, structural integrity, and aerodynamic testing outcomes. Section 6 extends the discussion to large-scale open-jet testing for cladding design in high-rise buildings, elucidating the objectives and scale effects of the study. In Section 7, a discussion of results is engaged, categorizing findings based on building type and integration with solar panels. In Section 8, potential avenues for future research are explored, including harmonizing solar panels with architectural aesthetics and advancements in building-integrated photovoltaics (BIPV). Section 9 outlines a vision for open-jet testing, encompassing various areas of future research and application, from the wind performance of different infrastructure and aerodynamic systems to educational initiatives. Finally, in Section 10, the key findings and implications of the paper are synthesized, emphasizing their broader impact on resilience and sustainability.  
Figure 1: Flowchart illustrating the structural organization of the paper, outlining the sequential exploration of small-scale wind testing challenges (Section 2), the introduction of open-jet testing concepts (Section 3), detailed investigation of large-scale open-jet testing on low-rise buildings (Section 4), examination of roof-mounted solar panel resilience (Section 5), exploration of large-scale open-jet testing for cladding design in high-rise buildings (Section 6), discussion of categorized results (Section 7), exploration of future research avenues (Sections 8 and 9), and synthesis of key findings and implications (Section 10).
## 3 Results and Discussion
### PCM-Incorporated Building Model  
The annual energy simulation results for the PCM-incorporated building models are presented in this section Finding the optimal melting point for PCM typically involves investigating the thermal and energy performance of the building after integrating phase change materials with different phase transition temperatures.
## 4 Natural Antioxidants and Their Role in Cosmetics
### Selected Plant Species with Antioxidant Activity
#### 4.11.3 Solanum lycopeneum  
L.  
_Solanum lycopeneum_ L., commonly known as the tomato, is a key horticultural crop in the nightshade family (Solanaceae) and holds significant global value. This species is native to South America, particularly the regions that are now part of Peru and Ecuador. It has been an essential food source for indigenous populations in these areas since pre-Columbian times. Due to its adaptability and nutritional benefits, the tomato has been widely cultivated around the world and is now one of the most important edible plants [210].  
Tomatoes and tomato products are a rich source of compounds with nutritional and antioxidant properties. They contain vitamins, phenolic compounds such as anthocyanins, phenolic acids, flavonoids, and carotenoids, including lycopene. The highest content of lycopene is found in processed and cooked products, e.g., in ketchup, purees, concentrates, soups, sauces, and juices. During heat treatment, lycopene contained in tomatoes is transformed into a form that is much better absorbed from the digestive tract. The cooking process releases some of the lycopene contained in the skin [210; 211; 212; 213].  
Important components of tomatoes are water-soluble vitamins, such as vitamin C, riboflavin, thiamine, vitamin PP, folic acid, pantothenic acid, vitamin H, and fat-soluble vitamin K [212]. Diets rich in tomatoes and tomato products are increasingly recommended in a healthy diet. Additionally, it is suggested that food manufacturers include information on the content of lycopene and its effect on product packaging. During tomato processing, lycopene is mainly obtained from by-products--skins and seeds [214; 215].  
The extract of _S. lycopeneum_ demonstrated potent antioxidant activity, as evidenced by its ABTS-reducing capacity, alongside significant anti-aging effects, particularly through elastase inhibition. A linear correlation was observed between the antioxidant activity and the anti-elastase properties of the tomato extract. This extract contributes to maintaining skin health, largely due to its lycopene content, which plays a crucial role in its strong anti-aging effects [216].  
Tomato extract formulations can significantly enhance skin lightening and hydration, while visibly improving skin tone through their antioxidant mechanisms [217].
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
## 2 Strong Adaptive Kalman Filter (SAKF)  
Strap-down Inertial Navigation System/Global Navigation Satellite System (SINS/GNSS) Integrated Navigation State Model  
For a high-speed UAV system, the state vector of navigation is defined as X\(\in R^{15}\), whose elements include INS error and inertial sensors' error.  
\[\mathbf{X}=\left[\begin{array}{cc}\varphi^{\mathrm{T}}&(\mathbf{\delta}\mathbf{v}^{n})^{ \mathrm{T}}&(\mathbf{\delta}\mathbf{p})^{\mathrm{T}}&(\mathbf{\varepsilon}^{b})^{\mathrm{T }}&(\mathbf{\
abla}^{b})^{\mathrm{T}}\end{array}\right]^{\mathrm{T}} \tag{1}\]  
The navigation system is based on the East-North-Up (ENU) frame, and \(\varphi=\left[\begin{array}{cc}\phi_{E}&\phi_{N}&\phi_{U}\end{array}\right]^ {\mathrm{T}}\) is the error components of attitude; \(\mathbf{\delta}\mathbf{v}^{n}=\left[\begin{array}{cc}\delta v_{E}&\delta v_{N}& \delta v_{U}\end{array}\right]^{\mathrm{T}}\) represents the error components of velocity in the ENU frame; \(\mathbf{\delta}\mathbf{p}=\left[\begin{array}{cc}\delta L&\delta\lambda&\delta h \end{array}\right]^{\mathrm{T}}\) represents the error components of latitude, longitude and height; \(\mathbf{\varepsilon}^{b}\) represents the drift of gyroscope in body frame; \(\mathbf{\
abla}^{b}\) represents the bias of accelerometer in body frame. The superscripts \(b\), \(n\) respectively represent body frame and ENU frame. \(T\) represents the transposition of the matrix.  
The state equation is given as follows,  
\[\dot{\mathbf{X}}=F\mathbf{X}+G\mathbf{W}^{b} \tag{2}\]  
where \(\mathbf{W}^{b}\) represents the measurement noise of gyroscope and accelerometer; \(\mathbf{F}(15\times 15)\) represents the transition matrix; \(G\) represents the noise exciting matrix.  
SINS/GNSS integrated system uses loosely coupled closed-loop architecture, and the measurement equation is given as follows,  
\[\mathbf{Z}=\left[\begin{array}{c}\mathbf{v}_{\mathrm{INS}}^{n}-\mathbf{v}_{\mathrm{GNSS }}^{n}\\ \mathbf{p}_{\mathrm{INS}}-\mathbf{p}_{\mathrm{GNSS}}\end{array}\right]=\mathbf{H}\mathbf{X}+ \mathbf{V} \tag{3}\]  
where \(H\) represents measurement matrix; \(\mathbf{V}=\left[\begin{array}{cc}\mathbf{V}_{v}&\mathbf{V}_{p}\end{array}\right]^{ \mathrm{T}}\), and \(\mathbf{V}_{v}\) and \(\mathbf{V}_{p}\) represent the velocity and position's measurement noise of satellite receiver; \(\mathbf{v}_{\mathrm{INS}}^{n}\) and \(\mathbf{v}_{\mathrm{GNSS}}^{n}\) respectively represent the velocity obtained by INS and GNSS; \(\mathbf{p}_{\mathrm{INS}}\) and \(\mathbf{p}_{\mathrm{GNSS}}\) respectively represent the position obtained by INS and GNSS.
## 5 System Simulation and Results Analysis
### Boundary Calculation of the Model  
Based on the formulas derived from Section 4.1, the relationship between the duty cycle (Dc) and output resistance (Rb) in both CCM and DCM can be expressed using an intermediate variable, m, as follows:  
\[\mathrm{D_{C}}=\left\{\frac{-6}{\mathrm{m}}-\frac{1}{6\mathrm{m}}+\frac{1}{3}-1 \frac{\sqrt{3}}{2}\bigg{(}\frac{\mathrm{m}}{3}-\frac{1}{3\mathrm{m}}\bigg{)}\right. \tag{21}\]  
\[\mathrm{m}=\frac{\sqrt[3]{\left(31\sqrt{3}\sqrt{\left(-2\mathrm{R_{b-in}T+27L} \right)+\mathrm{R_{b-in}T-27L}\right)}\mathrm{R_{b-in}T}^{2}}}{\mathrm{R_{b-in }T}} \tag{23}\]  
Boundary points are shown in Figure 11, where the \(x\)-axis represents the duty cycle (D); Rb represents the matched output resistance; and Dc represents the boundary point between CCM and DCM modes.  
Figure 11: Boundary Calculation.  
Figure 10: Model control structure diagram.  
From Figure 11, we can observe that the system operates well in both modes, and the boundary point C is also clearly visible. Then, we adjusted the coupling coefficient parameter, K, and observed the operation of CCM and DCM under different K values.
## 4 Discussion
### Study Limitations  
Our study, exploring the impact of vehicle interior lighting on reading behavior with a focus on a specific demographic (19 participants: 12 females and 7 males, average age 39), faces certain limitations that warrant further discussion. We recognize, as highlighted by the reviewer, potential biases arising from our sample's age and gender composition [37; 38; 39]. The sensitivity to lighting environments may vary across age groups, and differences in visual perception and preferences between genders could influence our findings. Additionally, we did not examine how lighting preferences might shift over the day, missing out on understanding the influence of circadian rhythms and melatonin levels on light intensity and color temperature preferences, which are known to affect alertness and relaxation at different times.  
To address these shortcomings, future studies should aim for a more diverse participant pool and consider the temporal aspects of lighting preferences, enhancing our comprehension of how gender, age and daily timing affect lighting preferences and reading behavior. Incorporating circadian rhythm insights into experimental designs could yield a more nuanced evaluation of vehicle interior lighting's effect on reading.  
Moreover, our study was conducted in a stationary vehicle to prioritize safety and feasibility, serving as an initial investigation into this subject. This approach, however, limits our ability to replicate the dynamic conditions of actual driving, such as movement and changing external lighting, potentially affecting the applicability of our results to real--world driving scenarios. Future research should, therefore, extend to studies in moving vehicles, under diverse lighting and environmental conditions, to fully grasp the implications of interior vehicle lighting on reading behavior. This direction not only promises to refine our understanding but also to guide the development of more effective vehicle lighting designs.