esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Resource Efficiency Metrics
#### 3.2.3 Execution Time (Inference Latency)  
The Raspberry Pi 4 board executes the non-quantized LiteRT DNN with a mean latency of less than 1.1 ms, comparable to the performance of the GPU-based development platform running the TensorFlow DNN. While the standard deviation of latency is negligible on both platforms, it is approximately six times higher on the GPU-based platform, likely due to task overhead from the Windows OS.  
When the non-quantized LiteRT DNN is deployed on the STM32F7 microcontroller, the process for analysis of 10 s ECG rhythm takes over 4.8 s. This inference latency exceeds the data collection period by nearly 50%, limiting real-time applications. However, after LiteRT DNN quantization, this latency is significantly reduced to approximately 615 ms for both IntQ and Full-IntQ models. The small standard deviation of less than 0.5 ms indicates that the analysis process inside CNN is stable and unaffected by variations in the input signal or the rhythm being analyzed. This stability is crucial for ensuring a reliable real-time decision-making process without unexpected delays, under real-life conditions involving diverse rhythm variations and artifacts.
## 3 Results and Discussion
### The Influence of Solution Temperature on NOx Removal Efficiency
Figure 8: The influence of gas temperature and KCL solution concentration on NO/NOx removal efficiency: (**a**) The SED changes with input voltage at 0.001 mol/L; (**b**) The SED changes with input voltage at 0.1 mol/L; (**c**) NO removal efficiency at 0.001 mol/L; (**d**) NO removal efficiency at 0.1 mol/L; (**e**) NOx removal efficiency at 0.001 mol/L, and (**f**) NOx removal efficiency at 0.1 mol/L. in Figure 8f. It is found, by comparison, that the difference in NOx removal efficiency between different temperatures becomes very limited, which further shows that when the conductivity reaches a certain value, the effect of solution temperature on the NOx removal reaction is rapidly reduced. More importantly, the results show the effectiveness and significant advantages of the KCL solution as the ground electrode in NOx removal. When the KCL solution with a concentration of 0.1 mol/L is used as the ground electrode, the NOx removal efficiency can reach up to 86% at the energy density of 3.0 kJ/L.
## 1 Introduction  
State departments of transportation (DOTs) in the United States use high-density polyethylene (HDPE) above-ground storage tanks (ASTs) to store deicing solutions for use in roadway maintenance in winter. HDPE ASTs can also be used to store sodium hypochlorite, sulfuric acid, ferric chloride, ferric sulfate, alums, polymers, biodiesel, fertilizers, phosphoric acid, sodium hypochlorite, sulfuric acid, urea, brine, agricultural chemicals, industrial chemicals, and many other chemicals used in wastewater treatment and industrial processes. HDPE ASTs are typically designed to contain from 7600 L to 56,800 L (2000 gallons to 15,000 gallons) of liquid, and the tanks are either single- or double-walled.  
Although HDPE has unique properties and advantages compared to other materials, such as cost efficiency, corrosion resistance, a resistance to leaching, outstanding tensile strength, a large strength-to-density ratio, high-impact resistance, a high melting point, low maintenance requirements, and the ability to last in different types of environments, HDPE does have a relatively poor weathering resistance (e.g., poor resistance to ultraviolet radiation) and is susceptible to stress cracking [1, 2]. When HDPE is utilized in high-capacity ASTs, the disadvantages of the material become crucial. Sudden, catastrophic AST failures endanger people and property and cause environmental, ecological, and economic losses [2, 3, 4, 5, 6]. To ensure functionality and integrity, these ASTs are routinely inspected to identify signs of aging and damage in an effort to prevent catastrophic failures. Relatively few studies have been performed on non-destructive testing (NDT)for the inspection of HDPE ASTs (e.g., [7; 8; 9]). Previous laboratory-based NDT studies by the authors showed that infrared thermography (IRT) and ultrasonic testing (UT) techniques were helpful in detecting and measuring the defects located in a simulated HDPE tank wall [8; 9]. These studies also showed that the HDPE sample size and thickness, exterior tank surface temperature, interior liquid temperature, ambient temperature, and relative humidity all affect defect detection and the threshold of the defect size that can be detected.  
However, one limitation is the lack of guidelines for NDT inspections of HDPE ASTs, despite guidelines existing for steel tanks (e.g., Steel Tank Institute's STI SP00l, American Petroleum Institute's API Standard 653). Therefore, one goal of this study is to investigate the efficiency of using IRT and UT during field inspections of HDPE ASTs in order to transfer the data from previous laboratory studies into practice and, if needed, apply some changes and/or corrections for a more accurate inspection. The other goal of this study is to provide a set of guidelines for state DOTs, manufacturers, and other commercial agencies or industries to follow and complete a more systematic and efficient tank integrity inspection.
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
## 2 Construction of Mathematical Models  
### Data Transmission Weight  
The size of a SAR echo image can be expressed as:  
\[F=\frac{G~{}A~{}B}{\rho_{\mathrm{r}}\rho_{\mathrm{a}}} \tag{5}\]  
where \(F\) represents the size of the echo image; \(G\) represents the compression ratio, which is determined by applying the high-compression JPEG-2000 method with a compressibility coefficient of \(G=0.01\); \(A\) indicates the imaging coverage area; \(B\) indicates bits per pixel of imaging figure; \(\rho_{\mathrm{r}}\) represents the range resolution; and \(\rho_{\mathrm{a}}\) signifies the azimuth resolution.  
The data transmission capacity can be expressed as:  
\[\mathrm{C_{s}}=\Delta B\sum_{k=1}^{K}\log_{2}(1+\mathrm{SNR_{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{      }}}}}}}}}}}}}}}}) \tag{6}\]  
where \(\mathrm{C_{s}}\) represents the transmission rate of the data signal and \(\mathrm{SNR_{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{ \mathrm{   \mathrm{    \mathrm{    }}}}}}}}}}}}}}}}\) denotes the carrier-to-noise ratio of the \(k\)-th subcarrier signal. To simplify, the subcarrier bandwidth is given by \(\Delta B=\Delta f_{\mathrm{c}}=\Delta f_{\mathrm{r}}=B_{\mathrm{w}}/K\). The bandwidth of each subcarrier is higher in FBMC signals due to aliasing.  
The data transmission weight of the integrated system can be expressed as:  
\[\kappa=\frac{R_{\mathrm{b}}T}{\Delta B}=\frac{F}{\Delta B} \tag{7}\]  
where \(\kappa\) represents the average data transmitted by each subcarrier during the synthetic aperture time and \(T\) denotes the synthetic aperture time.
## 4 Results
### Field Measurements  
Figure 9 shows the KPI test along the Guardo-Mataporquera section according to the network and radio KPIs shown in Tables 2 and 3, respectively. The green and orange colours show the areas where all these KPIs are fulfilled. The former includes locations with a margin greater than 50% of the requirements, whereas the latter does not necessarily provide this margin. In the figure, the yellow colour shows the locations where the connection to the MNO is alive (see Table 3 KPIs) with any network parameters not  
Figure 8: MNO network performance (latency and jitter) according to SLA agreement and a regular user.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{2}{c}{**LTE (4G)**} & \multicolumn{2}{c}{**5G NSA**} \\ \hline
**Parameter** & **Radio** & **Latency** & **Radio** & **Latency** \\ \hline RSRP & \(-109\) dBm & 300 ms & \(-120\) dBm & 150 ms \\ RSRQ & \(-12\) dB & 300 ms & No minimum value obtained \\ SINR & 10 dB & 220 ms & No minimum value obtained \\ \hline \hline \end{tabular}
\end{table}
Table 3: Radio KPIs. 4G/5G radio threshold values to guarantee availability 99% of the time for a latency lower than 500 ms.  
fulfilled (degraded condition case). Finally, the red areas show no connection at all to the MNOs.  
These measurements only show LTE developments since, in the selected area, there is no 5G deployment at the moment. This is the case with most of the low railway density traffic currently, showing more realistic results. Moreover, as shown in Section 4.2.1, the worst-case scenario is for 4G networks, as 5G allows more restrictive radio parameters.  
The results show a good performance along the whole line except for a short area in a remote area. Focusing on the stations, the KPIs' success rate increases to be included in the green areas for the four stations. This means that these field measures validate the hypothesis of implementing the proposed solution along this railway line as they fulfil the network and radio KPIs, presenting a 50% additional performance of all these parameters at the stations. Moreover, it is also a relevant result that the MNO covers most of the railway section.
# A Survey of UV Filters Used in Sunscreen Cosmetics
## 3 Discussion
The number of reported cases of allergic contact dermatitis after the use of octocrylene seems to be irrelevant considering the widespread use of it in cosmetic products. Photoconct alteredy to octocrylene may occur in patients with previous photoallergy to topically applied ketoprofen [20, 51]. Both OC and EHMC demonstrate high bioaccumulation rates, passing into breast milk [19]. Ethylhexyl triazone (EHT), was found to be the second most common UV filter in child sunscreens (52.0%) and the fourth in adults (50.0%). It does not show skin penetration [52] and has good photostability [34]. EHT releases free radicals in contact with sunlight [47]. Titanium dioxide (TiO\({}_{2}\)) is an inorganic UV filter present as both nano (22.0%) and micro (28%) forms in child sunscreens. Titanium oxide is considered a more photochemically stable and less skin irritating filter than most organic filters, but it can generate reactive oxygen species (ROS) when exposed to UV radiation, leading to potential adverse effects [53, 54, 55]. Therefore, since 2019, titanium dioxide can only be used as a nanomaterial when coated with inert shells like silica, hydrated silica, alumina, aluminium hydroxide, aluminium stearate, stearic acid, trimethoxyacrylylsilane, dimethicone or simethicone, or with some combinations thereof (Annex VI, Regulation No. 1223/2009). TiO\({}_{2}\) (nano) is not allowed in applications that may lead to exposure by inhalation [12].
###### Abstract  
(1) Robotic walkers have gradually been developed over the last decade, and their use has caused changes in gait. However, detailed gait analyses during robotic walker-assisted walking have not been performed. In this study, we aim to identify the changes in determinism of gait dynamics owing to the intervention of a robotic walker. (2) Eleven healthy subjects participated in walking experiments under normal walking, rollator-assisted walking, and robotic walker-assisted walking conditions. We analyzed the measured trunk acceleration to derive the gait parameters, local scaling exponent (LSE, from correlation sum), and percentage of determinism (%DET, from recurrence plot). (3) The walking speed during rollator-assisted walking was significantly lower than that during robotic walker-assisted walking. Changes in the shape of the LSE along the anterior-posterior direction revealed the influence of the robotic walker at an individual level. The changes in %DET along the anterior-posterior direction were also significantly different between normal walking and robotic walker-assisted walking. (4) The rollator decreased the walking speed in comparison to normal walking. The changed LSE and reduced %DET imply reduced deterministic patterns and disturbance to the gait dynamics. The robotic walker only affects the gait dynamics in the anterior-posterior direction. Furthermore, the burden on the subjects was reduced during robotic walker-assisted walking.  
nonlinear time series analysis; robotic walker-assisted walking; gait analysis; changes in the determinism of gait dynamics
## 2 Principle of Detection
### Residual Turbulent Scintillation Theory
In Equation (1), the first term indicates that the variance in light intensity fluctuations includes aerosol speckle fluctuations and turbulent fluctuations; the second term indicates that the aperture smooths the aerosol speckle fluctuations but includes turbulent fluctuations, and the third term indicates that the aerosol and turbulent fluctuations are smoothed by the aperture, and the total received energy does not fluctuate. Therefore, the atmospheric turbulence fluctuation information along the laser beam propagation path can be obtained by receiving the light intensity fluctuation measurement data, then the atmospheric turbulence profile can be obtained. According to RTS theory, when measuring the atmospheric turbulence profile, the parameters directly related to lidar include the laser incident wavelength, \(\lambda\), telescope aperture, \(D\), telescope focal length, \(F\), telescope aperture, \(d_{0}\), and lidar detection distance, \(L\).
## 5 Experimental Results and Analysis
### Experimental Results
**Effect of historical data on FL-AHNEAP:** We can learn from the terminal load analysis that the user data size will directly affect the time consumed by the FL-AHNEAP method in terminal training. Therefore, it is necessary to analyze the effect of historical data on FL-AHNEAP, e.g., the effect of three month data and one month data on FL-AHNEAP. We select the historical data of one month, three months, and six months to predict the same data of the next month, as shown in Figure 10. It can be seen that the accuracy of  
Figure 8: Cold start prediction of new users. Figure 9: Effect of user data size on FL-AHNEAP. using three month and six month data to predict the next month's data is slightly higher than the accuracy of using one month data, whereas the accuracy of using three month data and six month data is basically the same. It can be inferred that historical data has a certain impact on accuracy, but when the time span of historical data is long, the gap gradually decreases. More specific experiments can be performed to obtain the critical point, by measuring the historical data of the appropriate time period for the prediction accuracy and terminal load.
# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Key Technologies and Architectures
#### 2.3.1 Transformer Architectures and Attention Mechanisms  
At the core of MLLMs are neural networks based on the transformer architecture [6], which are well suited for handling sequential data and learning complex patterns across different modalities. Transformers use self-attention mechanisms to weigh the significance of different parts of the input data, allowing the model to focus on the most relevant information [19]. Extensions of the transformer architecture have been developed to handle multimodal inputs, such as Vision Transformer (ViT) for images [20] and Audio Transformer for audio data [21].  
Attention mechanisms are crucial for MLLMs, enabling them to prioritize certain aspects of input data over others. In multimodal settings, cross-modal attention allows the model to align and fuse information from different modalities [22]. For example, in visual question-answering tasks, the model needs to attend to relevant regions in an image based on a textual question [23].  
Figure 2: The general model architecture of MLLMs.
# Investigations on an Ancient Mortar from Ulpia Traiana Sarmizegetusa Archaeological Site, Romania
## 2 Materials and Methods  
### Materials  
The material to be investigated (Figure 1) came from Ulpia Traiana, an important archaeological complex situated in southwestern modern-day Romania, extracted from the remaining plinth of the central statue in Forum Vetus (Figure 2). Although the size of the aggregates qualifies the material as being a 'concrete' rather than a'mortar', the term'mortar' will be used in this paper to refer to an ancient, lime-based construction material.  
A visual inspection identified 2-3 cm aged gneiss aggregates along with a wide range of other aggregates (up to 10 mm in size but most of them under 1 mm) well connected to the matrix, embedded in a mortar containing a large number of millimeter-sized, relatively uniformly dispersed, CaCO\({}_{3}\)-rich white areas, giving a first confirmation of the age of the material. Cracks and mostly rounded, closed macroscopic pores were also visible.  
Figure 1: Mortar specimens containing CaCO\({}_{3}\) nodules (N), aggregates (A), and pores (P); extracted from below the exposed surface.  
Figure 2: Forum Vetus, Colonia Ulpia Traiana Augusta Dacica Sarmizegetusa; the remains of the statue’s plinth.  
To obtain fresh surfaces in order to eliminate, as much as possible, the influence of weathering, the samples were sliced with an angle grinder. Microscopic images show an almost unaffected surface (only a few small aggregates were extracted, while the larger ones were sliced through), suggesting that a strong bond between aggregates and the binder matrix still exists, even after the mechanical action; also, large carbonation areas can be easily identified (Figure 3). Beyond the surface exposure, the material has a well-connected structure in which there are large areas of carbonation products that have occupied the available pores over time. This shows that the material can provide a datable carbon source through these carbonation products, and that in those areas, this process ended a long time ago.  
No carbonate concretions that could come from aggregates were identified in the analyzed sample, so the S5 calcareous concretion mechanism was discarded.
## 2 Experimental Study
### Load Displacement Response  
The load-displacement curve is a graphical representation of the relationship between the displacement of a structure and the corresponding applied load. It provides a visual depiction of the effects of the load on the structure [30]. In the case of the lateral static loading test, the load-displacement curve for the lateral load is shown in Figure 5. The linear displacement load generated by the lateral actuator starts from 0 mm and increases gradually by 10 mm at each level of loading until reaching 60 mm, at which point the loading is stopped. The loading curve displays nonlinearity, with an overall trend characterized by a steep drop. When the lateral displacement reaches 60 mm, the magnitude of the lateral load is approximately 8 kN. The loading curve can be roughly divided into two stages: the stiffness stage from 0 mm to 5 mm displacement and the plastic strain stage from 5 mm to 60 mm displacement. In the stiffness stage, the lateral displacement of the GPF shows a linear relationship with the lateral load, increasing from 0 mm to around 5 mm, while the lateral load increases from 0 kN to approximately 3.7 kN in a uniform manner. During this stage, the GPF undergoes slight deformation, and the soil in front of the pile slightly bulges, with no significant separation observed between the GPF and the soil. In the plastic strain stage, the displacement load increases from 5 mm to 60 mm, and the lateral load continues to increase but at a reduced rate. In the later stage of loading, the curve reaches a plateau. During this stage, relative sliding is observed between the GPF and the soil, cracks emerge at the rear in the loading direction, and the front end of the soil bulges, with a portion of the GPF being embedded in the soil.  
It is also shown in Figure 6 that at the end of the stiffness stage, the soil surrounding the GPF has reached the plastic yield critical state, while the GPF itself remains in the elastic stage. In the plastic strain stage, the soil enters the plastic yield stage and undergoes deformation, while the GPF continues to deform, either in the elastic stage or transitioning into the plastic stage. Throughout the loading process, the GPF exhibits superior lateral bearing performance.  
Figure 5: Loading protocol.  
Figure 6: Lateral load and displacement curve.
## 3 Results and Discussion
### Adsorption Isotherms  
Table 2 and Figure 8 show that the two models fitted the experimental removal data well, and that the Langmuir model fit better with a higher value of \(R^{2}\) (>0.99). The Langmuir model assumes that the adsorption of metal ions occurs through monolayer adsorption on a homogeneous surface without interaction between the adsorbed ions. However,  
Figure 6: Effect of (**a**) pH and (**b**) initial concentration of Cr(VI) on the Cr(VI) removal by FeBC.  
Figure 7: Effect of Ca\({}^{2+}\), Na\({}^{+}\) (**a**) and Cl\({}^{-}\) (**b**) in solution on the Cr(VI) removal by FeBC.  
the Freundlich model is based on the adsorption on the heterogeneous surface [14,31]. It indicated that monolayer adsorption and heterogeneous surface conditions may coexist under the experimental conditions, but that monolayer adsorption was more dominant [32]. Besides, KL increased with increasing temperature, indicating that the adsorption of Cr(VI) onto FeBC was endothermic. Therefore, the higher temperature was beneficial to the adsorption process of Cr(VI) onto FeBC.
## 2 Numerical Modeling
### FEM Numerical Simulation Method  
Modal analysis can obtain relatively accurate natural frequencies and modal shapes of the blade. This can not only verify the accuracy of finite element structural modeling, but also the basis for the corresponding structural dynamics analysis. In the environment of high wind speed and high turbulence, the blade as a slender body is very prone to structural resonance or blade tip flutter, so it is necessary to carry out structural modal analysis in advance.  
For wind turbine blades, the overall dynamic balance equation of the structure after finite element discretization is:  
\[[M]\big{\{}\ddot{u}\big{\}}+[C]\big{\{}\dot{u}\big{\}}+[K]\big{\{}u\big{\}}=0 \tag{5}\]  
When the intrinsic properties of the blade are often ignored in engineering, the equation becomes:  
\[[M]\big{\{}\ddot{u}\big{\}}+[K]\big{\{}u\big{\}}=0 \tag{6}\]  
For linear systems, the solution of the above equation can be written as:  
\[\{u\}=\{\phi\}_{i}\cos(\omega_{i}t) \tag{7}\]  
where \(\{\phi\}_{i}\) is the mode shape eigenvector corresponding to the \(i\)th order mode; \(\omega_{i}\) is the natural frequency of the order \(i\)th mode;  
\[\Big{(}[K]-\omega_{i}^{2}\Big{)}\big{\{}\phi\big{\}}_{i}=0 \tag{8}\]  
where \(\{\phi\}_{i}\) is the mode shape eigenvector corresponding to the \(i\)th order mode; \(\omega_{i}\) is the natural frequency of the order \(i\)th mode; \([K]\) is an intrinsic property of the structure, which is related to the elastic modulus of the structure itself and the Poisson's ratio.  
Therefore, in modal analysis, only the elastic modulus, Poisson's ratio and density of all materials of the blade need to be known to solve the modal analysis of the entire blade: the natural frequency and its corresponding mode shape.