esa-sceva/satcom-chunk-collection huggingface dataset:
# Making a Case for Hybrid GFRP-Steel Reinforcement System in Concrete Beams: An Overview
## 3 Analytical Models for Hybrid GFRP-Steel Reinforced Beams
### Models Considering Physical Effects: Fire Modelling
High fire resistance and low cost are significant advantages of using steel rebars in concrete structures [148], considering its temperature threshold is set to between 300 \({}^{\circ}\)C to 500 \({}^{\circ}\)C and its thermal expansion coefficient of \(11.7\times 10^{-6}/^{\circ}\)C [149; 150]. In comparison, GFRP bars start to lose their flexural and bond strengths at temperatures above 120 \({}^{\circ}\)C. However, the complete thermal degradation of the polymer matrix can stand up to 350 \({}^{\circ}\)C. GFRP's load-bearing capability is therefore restricted to much lower temperatures compared to that of steel [151; 152]. Furthermore, GFRP exhibit a non-uniform and highly variable coefficient of thermal expansion (8-33 \(\times\) 10\({}^{-6}/^{\circ}\)C) depending on the direction. Along the longitudinal direction, the GFRP coefficient of thermal expansion is in the range 8-10.0 \(\times\) 10\({}^{-6}/^{\circ}\)C [116] and is similar in magnitude to that of steel. Finite element (FE) and experimental research have been performed to examine the fire behaviour of GFRP RC beams, but direct experimentation has been rather limited. The outcomes from these studies (summarised in Table 4), suggest that the concrete cover thickness has a significant role in the fire-resisting period of the elements. According to experimental tests, a minimum concrete cover of 65 mm is recommended to meet the standard required for a fireproofing of 90 min and impart adequate protection to the GFRP reinforcement. Such a thick cover is not economical and may deter to use of GFRP. Additionally, fire retardant coating can provide a potential resistance for fibres from elevated temperatures [153; 154]. A recent study conducted by [PERSON] et al. [155] concluded that the fire-retard coating helps to preserve the mechanical properties of the GFRP bars in the range of 350-600 \({}^{\circ}\)C, and even increases the tensile strength retention rate by 20-30%. A significant decrease on the performance of fire-retardant coating was only noted when the temperature exceeded 600 \({}^{\circ}\)C.  
Moreover, the fire safety building regulations impose that the minimum period of fire resistance should be 90 min [156]. Meanwhile, most standard procedures of concrete structure fireproofing recommend a cover thickness that should provide a fire resistance of 90 min with exposure to a peak temperature of 350 \({}^{\circ}\)C. GFRP reinforcement would therefore be adequate for most categories of buildings and structures. Figure 8: Thermogravimetric pattern of cementitious binder.
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 4 Results
### Analysis of UAV Velocity Stability  
When a UAV operates in airspace, it experiences vibrations due to various factors such as ambient wind and aircraft dynamics. Even if the UAV is set to operate at a constant velocity, there may still be differences between the actual motion velocity and the preset velocity. To ensure the credibility of velocity reference values, the experiment verified the stability of the UAV state at different preset velocities in approximately the same environment. From Figure 10, it is evident that when the UAV operates at a velocity greater than 5 m/s, the velocity standard deviation is consistently greater than 1 m/s, and the deviation between the actual average velocity and the preset velocity is within 1 m/s. Throughout the whole route, the overall velocity variation shows a pattern of acceleration-steady-deceleration. Excluding non-steady states during the acceleration and deceleration of the UAV, the deviation between the actual average velocity and the preset velocity is less than 0.1 m/s. The standard deviation increases with the higher preset velocity of the UAV. When the preset velocity is 10 m/s, the velocity standard deviation is approximately 0.5 m/s. By eliminating velocity reference values during non-steady states, it is possible to significantly reduce the system bias that unavoidably exists in the UAV due to internal and external environmental factors. It provides a reliable data guarantee for conducting radar validation on UAVs in motion.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 4 Proposed SLAM Classification Based on UWSNs
### New Classification Based on UWSNs
#### 4.2.2 UWSN-Integrated SLAM Systems  
UWSN-integrated SLAM systems enhance performance by incorporating external data and communication capabilities through UWSNs. These systems improve localization accuracy and enrich generated maps by leveraging networked sensors and communication. For example, acoustic beacons and distributed sensor nodes can provide additional environmental data, enabling more robust and accurate SLAM solutions [48].
## 2 Literature Review
### Connected Vehicles (CV)
#### 2.2.1 General Description  
Past achievements in the automotive industry focused on protecting the driver in case of an accident. Safety systems such as the airbag, seat belts and later on, anti-lock braking system (ABS) and electronic stability control (ESC) were introduced in cars for driver safety and protection [27]. However, recent developments show that the focus has shifted towards accident prevention: \"Whereas most developments in transportation safety over the past 50 years were designed to protect passengers in the event of a crash, V2I [vehicle to infrastructure] and V2V [vehicle to vehicle] systems are being designed to help motorists avoid the accident altogether.\" [28]. There are numerous projects that contribute to inter-vehicle communication (IVC) research and development [29].  
In above mentioned communication technologies which make an automobile connected, V2I and V2V are among most advanced ones in today's world. V2I means that the transport infrastructure sends valuable information to vehicles regarding traffic and road conditions, collision danger, speed limitations, traffic jams at intersections, etc. and recommends certain actions to vehicles situated in traffic. The communication between two or more vehicles, V2V, aims at the interaction and collaboration between them. The information that is interchanged is based on the on-site traffic aspects. Vehicles located within a certain distance form a network, communicate with each other, and together, make decisions to enable better traffic management [30]. Further, [PERSON] et al. [31] also expresses that communication between vehicles and mobile devices is becoming progressively important for our lives. Moreover, intelligent transport systems (ITS) classify V2X into road safety, traffic management, infotainment V2X services, and autonomous driving platforms. Road safety applications help mitigate road accidents and lower driving fatality numbers by detecting mobile fixed obstacles and avoiding collisions, and by using emergency electronic brake lights. It also provides weather information and accident notifications [32].
## 4 Security Analysis and Simulation Verification
### Formal Simulation Verification of Protocol Security
#### 4.3.3 Interpretation and Result Evaluation  
This study employs the ProVerif tool for formal simulation-based verification. The protocol workflow is first modeled in ProVerif's formal language, with defined security properties and verification objectives: (1) confidentiality of key against adversarial access, and (2) reliability and effectiveness of mutual authentication. Through entity modeling and simulation of protocol participants A and B, the analysis demonstrates that adversaries cannot circumvent authentication via identity spoofing or message manipulation, nor derive valid session keys from compromised data. These findings complement theoretical security analyses, confirm the protocol's resilience in practical attack scenarios, and establish a robust technical foundation for engineering implementation.
## 4 Impacts of HTST and UHT on Food Quality
### Basic Physicochemical Characteristics
#### 4.2.3 Titratable Acidity  
Researchers have mostly determined that there is no significant change in titratable acidity (TA) after UHT preservation [22; 25; 36; 38; 41; 45; 53], as well as during storage [25; 35; 53; 54]. [PERSON] et al. [38], despite showing no significant change in the TA of cloudy ginger juice immediately after preservation, indicated a significant decrease in TA during a 91-day storage period. However, different reports can be found in the literature. [PERSON] et al. [37], in pomegranate juice, observed a significant decrease in TA after UHT preservation. [PERSON] et al. [32] observed an increase in TA of almost 7%, using the same parameters as [PERSON] et al. [37]--110 \({}^{\circ}\)C/8.6 s. [PERSON] et al. [17] observed a significant 12.9% increase in acidity after UHT preservation of black carrot juice compared to fresh. [PERSON] et al. [45] observed an increase in acidity within 16 weeks of preservation and [PERSON] et al. [41] within 20 days.  
The HTST treatment of peach juice (72 \({}^{\circ}\)C/15 s) did not result in a significant change in TA compared to raw juice, immediately after preservation, as well as during a 28-day storage period [31]. Other researchers [5; 56; 32; 49] also observed no significant effects of HTST treatment on the acidity of fruit and vegetable products. In contrast, the acidity of orange juice decreased significantly by 7.6% and 8.4% as a result of heating at 90 \({}^{\circ}\)C for 10 and 20 s, while an increase in TA was noted over 180 days. A different observation was made by [PERSON] et al. [46], whereby TA increased by 10.2% in blended juice after the process.
## 3 The Proposed Methodology
### S Component: Scoring Topic Clusters  
At this phase, fastTNT evaluates each topic cluster's predictive power and importance for the classification tasks. This process is achieved using a novel topic-scoring approach called feature importance-based topic scoring (FITS).  
fastNT uses two main data inputs for scoring: the topic clusters (\(TC\)) and the final mean feature weight list (\(FI_{F}\)). In order to score a specific topic cluster (\(TC_{i}\)) comprising a set of words, \(TC_{i}=\{w_{i1},w_{i2},\ldots,w_{i\bar{w}}\}\), where \(b\) is the number of words in the cluster, and \(i\) is the topic cluster number ranging from 1 to \(k\), we first extract from the final mean feature weight list (\(FI_{F}\)) the feature importance values associated with those words exclusively. Let \(FI_{F(TC)}=\{FI_{F(w1)},FI_{F(w2)},\ldots,FI_{F(w\bar{w})}\}\) be the subset of feature weights corresponding to the words in topic cluster \(TC_{i}\). Afterward, we calculate the topic cluster score \(S_{TCi}\) using an aggregation function \(S_{TCi}=\text{Agg}(FI_{F(TC_{i})})\). The aggregation function may be the arithmetic mean, median, geometric mean, or another suitable measure. Figure 6 illustrates this scoring method, visually representing how the scoring process works.  
For this study, the geometric mean is chosen as the topic cluster weighting function (refer to Equation (1)) to dampen the impact of extreme values in the set of feature importance values \(FI_{F(TC)}\). This approach ensures that the topic cluster score is robust and reflects the topic's overall importance in the context of the dataset.  
\[S_{TCi}=\left(\prod\
olimits_{j=1}^{b}FI_{F(w_{\bar{w}_{j}})}\right)^{\frac{1 }{b}}\ldots\ldots. \tag{1}\]  
These topic clusters are then ranked in descending order according to their computed scores.
## 3 Background Theory
### Spectrogram  
A spectrogram is originated by the sequential computations of short-time Fourier transforms (STFT) together with the time domain signal. The shades of colors in the spectrogram are the logarithmic energy representation of discrete Fourier transforms (DFT) windowed on a particular time and frequency of the signal [19]. In the representation of the spectrogram, the time is plotted on the horizontal axis and the frequency on the vertical axis, and the energy intensity is displayed using color representation. Higher energy levels are shown with yellow color saturation and lower energy levels are shown with blue color saturation. The greater the energy bands lying on the base of the frequency axis, the greater the energies at the lower frequencies. Figure 5 illustrates the construction of a spectrogram from a time-domain signal where small windows of the signal are being converted into color bands of the spectrogram. It becomes difficult to mathematically model the acquired AE signals; therefore, derived signal parameters become a good option. Spectrograms have recently been used by [PERSON] et al. [20] to identify six different human activities using the measured Channel State Information (CSI).
## 2 Results and Discussion
### Volatile Components in HAB  
A total of 606 volatile compounds were identified in this study, among which esters were present in the highest number (179), followed by aldehydes and ketones (111), terpenes (82), alcohols (81), sulfides (37), furans (29), nitrogenous compounds (29), acids (23), phenols (18), and lactones (17).
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 6 Supporting Technological Advances
### Three-Dimensional Cell Cultures  
Three-dimensional (3D) cell cultures are one of the key technologies that form the basis of whole organ engineering. Cell morphology can greatly affect the cell's functions, and 2D cultures can alter a cell's morphology from its native shape by a great extent. Cells extracted from tissue and placed in a 2D culture become flat, can fail to divide, and lose their differentiated phenotype [167]. Cells that have lost their native morphology and function after transfer to a 2D culture have been shown to regain their native phenotype after placement in a 3D culture gel, further emphasizing the importance of a dimensionally biomimetic cell scaffold [168]. This is because when compared to 2D cell cultures, 3D culture environments are more similar to in vivo environments for most tissues and are thus able to produce more physiologically similar cell activity for most cell types [169]. One of the seminal works in developing 3D cell culture environments and in demonstrating the merit of using ECM molecules as the scaffold material of choice was work by [PERSON] examining the effect of 3D gel substrates on gene expression of cultured cells [170]. Her work, along with most early 3D cell culture methods, utilized hydrogels with more basic compositions, such as collagen gels. However, as 3D cell culture scaffolds were further developed, more structurally and compositionally advanced scaffolds were generated, such as engineering scaffolds to have built-in porosity to assist in cell infiltration and adhesion [11]. Three-dimensional cell cultures showed great promise in being able to better replicate in vivo functions than 2D cultures, allowing for more accurate in vitro cell biology studies and disease modelling, with this ability leading to 3D cell culture use in tissue engineering [11].
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Battery-Based Supply Techniques
#### 2.2.3 Laser Beam Inflight Recharging
Furthermore, experiments described in [53] have demonstrated the feasibility of this technique, achieving flight times of over 12 h for a quadcopter. Wireless recharging offers the potential for continuous operation and extended flight durations, addressing limitations associated with battery capacity and swapping cycles. However, further research and development efforts are necessary to optimize the technology and overcome challenges related to power efficiency, safety, and scalability. While the laser beam inflight recharging approach offers potential benefits, it also introduces certain constraints and considerations that need to be taken into account. One such constraint is the need for UAVs to operate at reduced heights and within a limited area to maintain a reliable power transfer from the laser transmitter. This limitation is influenced by regulations, such as those imposed by the FAA, which set a maximum altitude of 400 feet for small UAVs weighing less than 26 kg. Compliance with these regulations is crucial for safe and legal operations. Another consideration is that each UAV utilizing laser beam inflight recharging requires its own dedicated laser transmitter. This can pose limitations on the number of UAVs that can be deployed in a given area, as multiple transmitters would be needed to support a larger fleet. Additionally, the individual transmitters can significantly increase the overall cost of implementing the laser beam inflight recharging system [34]. Therefore, the scalability and economic feasibility of this approach should be carefully evaluated before widespread adoption. It is important to conduct a comprehensive assessment of the operational and regulatory aspects, as well as the economic implications, when considering the integration of laser beam inflight recharging for UAVs. This evaluation should involve a careful balance between the benefits of extended flight time and the potential limitations and costs associated with this technology. By addressing these considerations, researchers and industry professionals can make informed decisions regarding the practical implementation of laser beam inflight recharging in UAV operations.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 1 Introduction
#### Notations  
In the rest of the paper, we present the vectors and matrices in bold. We use \((.)^{*}\), \((.)^{H}\), and \((.)^{-1}\) to denote conjugate, conjugate transpose, and inverse of the argument, respectively. \(I_{K}\) stands for the \(K\times K\) identity matrix. We use \(\ln(.)\), \(\log_{2}(.)\), and \(\lim_{x\rightarrow\infty}f(x)\) to show the natural logarithm function, logarithm function with base 2, and limit of function \(f(x)\) when its argument tends to infinity.
## 5 Results and Discussion
### Fault Prediction Phase
#### 5.1.4 Operational Parameter Model Prediction  
The selected model is used to predict the operating parameters of the machine tool, and the results are shown in Figure 18. According to the prediction results, the selected prediction model can accurately predict the output of the operating parameters of the machine tool and prepare for further fault diagnosis. At the same time, the prediction model derived from a single data item can still have a good prediction effect when processing the other six data items, which further explains the excellent generalization ability of the MLGRU-Attention prediction model.  
Table 7 shows the network structure of the neural network model used for prediction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output Shape** & **Parameters** \\ \hline GRU1 & (Samples, 24, 50) & 7950 \\ GRU2 & (Samples, 50) & 15,300 \\ Attention & (Samples, 50) & 7500 \\ dense & (Samples, 1) & 51 \\ \hline Total & & 30,801 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Architecture of the MLGRU model with attention layer.  
Figure 18: Prediction results of (**G1**) FR, (**G2**) SM, (**G3**) SL, (**G4**) SS, (**G5**) AF, (**G6**) AS, (**G7**) FS.
## 1 Introduction
### Comparison of Imaging Conditions  
To illustrate how the proposed imaging condition in Equation (13) attenuates large angle reflections, we performed simple experiments on the prestack impulse response. The synthetic data were generated by a two-reflector layered model. Figure 2a shows the impulse response using \(I_{dt}\) for a single trace at an offset of 4000 m, from which we can see the image of two reflection events, as well as the backscattered events. The imaging condition \(I_{\mathrm{dt}}+I_{\
abla}\) attenuated backscattered events, as shown in Figure 2b. By using the proposed imaging condition in Equation (13), Figure 2c only preserved the small-angle reflections. Figure 2d-f show the corresponding imaging comparison for a single trace at an offset of 0 m (one source and one receiver, both at 0 m on the surface). Using our proposed small-angle imaging condition, Figure 2f preserved all the zero-angle reflections available in Figure 2d, while attenuating the backscattered energy, which is mostly notable at location 0 m.  
Figure 3 compares the imaging results of the single trace at zero offset using Equations (13) and (14). From the comparison, we can see that the factor \(1/\omega^{2}\) in Equation (14) boosts the low-frequency components in Figure 3b.  
Figure 1: Workflow of the proposed method.
## 3 Results  
Table 1 shows the comparison between HIIT with and without cognitive load in the perception of physical, cognitive, and emotional efforts and behavioral data (speed and heart rate). The physical effort was significantly higher during the HIIT with cognitive load than during the HIIT without cognitive load (\(p\) = 0.001), although no significant differences in external physical load (heart rate and speed) were observed between sessions. Cognitive efforts were also significantly higher during the HIIT with cognitive load (\(p\) = 0.015). Table 2 shows the comparison between pre- and post-training on physical, cognitive, and emotional efforts (obtained from the Hooper and Mackinnon wellness questionnaire and the VAS for mental fatigue) and the RMSSD for HIIT with and without cognitive load. A significant increase in the subjective feelings of general fatigue (\(p\) < 0.001) and a significant decrease in RMSSD (\(p\) < 0.001) were observed in both protocols without differences between them. Additionally, a significant increase in subjective feelings of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **HIIT without Cognitive Load** & **HIIT with Cognitive Load** & \(p\) \\ \hline \multicolumn{4}{c}{Questionnaire to quantify the mental load in sports} \\ \hline Physical effort (a.u.) & 3.78 \(\pm\) 2.39 & 6.00 \(\pm\)1.90 & 0.001 \({}^{\star}\) \\ Cognitive effort (a.u.) & 4.56 \(\pm\) 2.17 & 5.43 \(\pm\) 1.88 & 0.015 \({}^{\star}\) \\ Emotional effort (a.u.) & 6.38 \(\pm\) 1.95 & 6.84 \(\pm\) 2.14 & 0.164 \\ \hline \multicolumn{4}{c}{Behavioral data} \\ \hline Mean HR (bpm) & 160.70 \(\pm\) 13.06 & 159.86 \(\pm\) 11.67 & 0.64 \(\dagger\) \\ Maximum HR (bpm) & 176.90 \(\pm\) 11.31 & 176.88 \(\pm\) 9.19 & 0.99 \(\dagger\) \\ Mean speed (Km/h) & 9.86 \(\pm\) 1.68 & 9.94 \(\pm\) 1.72 & 0.65 \\ Maximum speed (Km/h) & 14.42 \(\pm\) 1.69 & 14.60 \(\pm\) 1.64 & 0.42 \(\dagger\) \\ \hline \multicolumn{4}{c}{\({}^{\star}\): \(p\) < 0.05; \(t\): Due to distribution requirements, the \(p\)-value was obtained from a paired sample \(t\)-test. \(p\)-values reported in the table were adjusted for Bonferroni corrections for multiple comparisons. HR—Heart Rate; HIIT—High-intensity interval training a.u.—arbitrary units; bpm—beats per minute.} \\ \end{tabular}
\end{table} 
Table 1: Analysis of physical and mental load indicators during training sessions. Comparison between different protocols.