esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study presents a comprehensive analysis of a simplified design methodology for timber-concrete composite roof and floor structures employing metal web beams, also known as posi-joinsted beams, easi-joist, or open web joists, validated through both laboratory experiments and finite element (FE) method analyses. The proposed method integrates the transformed section method and the \(\gamma\)-method, as outlined in Annex B of EN1995-1-1 for mechanically jointed beams. The investigation focuses on roof and floor structures featuring posi-joinsted beams, oriented strand board (CSB) sheets connected by screws, and a layer of concrete bonded to the OSB sheets using epoxy glue and granite chips. Two groups, each consisting of four specimens, were prepared for the laboratory experiments. Each specimen comprised two posi-joinsted beams, 1390 mm long, connected by OSB/3 boards measuring 400 mm in width and 18 mm in thickness. The beams had a cross-sectional depth of 253 mm, corresponding to beams of grade PS10, with top and bottom chords made from solid timber (95 mm \(\times\) 65 mm). Bracing members with cross-sections of 100 mm \(\times\) 45 mm were used to join the bottom chords of the beams. A layer of self-levelling mass SakretBAM, 50 mm thick, was bonded to the OSB/3 boards using SicaDur 31 epoxy glue and granite chips (16-32 mm). The specimens underwent three-point bending tests under static loads, and FE modelling, conducted using Ansys R2 2022 software, was employed for both experimental groups. A comparative analysis of results obtained from the simplified design method, FE simulations, and experimental data revealed that the simplified method accurately predicted maximum vertical displacements of the roof fragment, including posi-joinsted beams, with precision up to 11.6% and 23.10% in the presence and absence of a concrete layer, respectively. The deviation between normal stresses in the chords of the beams obtained through the simplified method and FE modelling was found to be 7.69%. These findings demonstrate the effectiveness and reliability of the proposed design methodology for timber-concrete composite roofs with posi-joinsted beams.  
simplified design method; transformed section method; \(\gamma\)-method; laboratory experiment; three-point bending; finite element analysis; load-carrying capacity; maximum vertical displacements; timber-concrete composite; stone chip method; posi-joinsted beam; roof structures; floor structures; metal web joists +
Footnote †: journal:  
0000-0001-9991-4000][PERSON]  
1 Institute of Structural Engineering, Riga Technical University, LV-1048 Riga, Latvia; [EMAIL_ADDRESS] (A.R.); [EMAIL_ADDRESS] (K.B.-V.); [EMAIL_ADDRESS] (E.B.); [EMAIL_ADDRESS] (V.L.)
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Antioxidants and Antimicrobials
#### 5.4.7 Ozonated Water  
Ozonated water has been applied especially to fresh-cut vegetables for sanitation purposes. Ozone has a greater effect against certain microorganisms and rapidly decomposes to oxygen, leaving no residues and causing less regulatory concern than chlorine [8; 110]. Some studies about the effect of ozone on PPO activity have been performed. In this way, different ozonated water concentrations on fresh-cut celery were used, finding that the higher ozone concentration in water was (up to 0.18 ppm), the less PPO activity was found, probably due to the microbial reduction [82]. In addition, another study found similar results for fresh-cut lettuce, reducing enzymatic browning [111]. The use of ozone in CA for fresh-cut processing was checked in carrots, paying attention to PPO activity and lignification. Thus, both PPO and POD were reduced by applying 2% O\({}_{2}\) and 8% CO\({}_{2}\) after a washing treatment with ozone (10 mg/L) for 10 min [27]. However, its application in extending the shelf-life of fresh-cut potatoes did not seem to offer any effect on PPO activity, whereas NaturSeal and other dip treatment such assodium acid sulfate had a positive effect [112].
# Simulation and Validation of a Steering Control Strategy for Tracked Robots
## 4 Deviation Correction Control Method of Tracked Robot
### Principle Analysis of LiDAR Ranging  
The inspection robot uses a two-dimensional laser LiDAR for ranging. The laser radar uses a laser beam of 768 nm wavelength, and the measuring method adopts the principle of TOF (time of flight). During the detection process of the robot, the LiDAR can collect obstacle data in 683 directions per scanning cycle, and the detection range is between \(-120^{\circ}\) and \(+120^{\circ}\). The definition of data points is shown in Figure 7a; the shaded part in the figure is the detection blind area of the robot. The obstacle detection interface is depicted in Figure 7b, with the software program developed in a VC++ environment. LiDAR is used as the coordinate system origin to measure the surrounding obstacles, and the counterclockwise rotation of point B is positive. The distance of point E from the LiDAR is OE, expressed by \(\rho\). Then, the distance between any point on the DBC arc and the LiDAR can be calculated using the following formula.  
\[\left\{\begin{array}{l}x=\rho\cos\beta\\ y=\rho\sin\beta\end{array}\right.,\ \beta=\pm 120^{\circ} \tag{12}\]  
Figure 6: Correction principle of robot position and attitude adjustment.
## 3 Synthesis and Input of Cross-Fault Ground Motions
### Synthesis of Cross-Fault Ground Motion
#### 3.1.3 Synthesis of Cross-Fault Ground Motion  
The actual ground motion is a random process in which the high-frequency and low-frequency components are coupled, and cross-fault ground motion is no exception. Based on the analysis of \(11\) ground motions and \(28\) seismic recordings by [PERSON] et al. [44], it is found that the near-fault velocity pulse is a low-frequency component of less than \(1\) Hz. Therefore, the Butterworth filter is used to extract the high-frequency random components with a cut-off frequency greater than \(1\) Hz in three ground motions. The filter frequency response formula \(H(f)\) is as follows:  
\[H(f)=\frac{1}{1+\left(\frac{f}{f_{\rm c}}\right)^{2n}} \tag{8}\]  
where \(f\) is the input frequency, \(f_{\rm c}\) is the cut-off frequency, and \(n\) is the filter order of \(4\).  
After filtering the near-field seismic records according to the above method, the high-frequency bottom wave and the low-frequency pulse function are superimposed to obtain the cross-fault ground motion with slip displacement. Limited to space, the time history curves of three groups of synthetic ground motions with an average slip displacement of \(50\) cm are given in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**NGA Number** & **Earthquake** & **Magnitude** & **Station** & **Rrup (km)** & \begin{tabular}{c} **PGA** \\ **(cm\(\cdot\)s\({}^{-2}\))** \\ \end{tabular} & \
\begin{tabular}{c} **PGV** \\ **(cm\(\cdot\)s\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **PGD** \\ **(cm)** \\ \end{tabular} & **Tp (s)** \\ \hline
316 & Westmorland, 1981 & 5.9 & PTS225 & 16.66 & 0.23 & 55.58 & 37.10 & 4.39 \\ \hline
802 & Loma Prieta, 1989 & 6.93 &
\begin{tabular}{c} STG090 \\ WVC000 \\ \end{tabular} & 8.50 & 0.33 & 45.97 & 33.33 & 4.57 \\
9.31 & 0.26 &
\begin{tabular}{c} 42.06 \\ \end{tabular} & 24.27 & 5.65 \\ \hline \end{tabular}
\end{table}
Table 1: Three selected near-fault ground motion records.  
Figure 9: Low-frequency displacement time history of sliding displacement of \(40\)–\(80\) cm.
## 5 Simulations
### Theoretical Mode Solution  
As the linear structure has a time-varying quality, its transient natural frequencies and modal shapes are evolving. However, at time \(t\), the mass, stiffness, and damping can be considered to be determined. Therefore, it is possible to find the transient modal natural frequencies and modal shapes at that time, as for LTI structures.  
The simulation results were verified against the actual modes of the weakly damped structure. According to the theory and finite element analysis, all of the natural frequencies are undamped and the modal shapes are real. According to vibration dynamics theory, the frequencies and modal shapes can be computed using the secular equation:  
\[\left|\mathbf{K}-w_{k}^{2}\mathbf{M}\right|=0 \tag{20}\]  
where \(\mathbf{K},\mathbf{M}\in\mathbb{R}^{N\times N}\) are the stiffness and mass matrices, respectively, and \(w_{k^{\prime}}\) is the eigenvalue of Equation (20) corresponding to the \(k^{\prime}\)th natural frequency. The eigenvector \(\vec{\varphi_{k^{\prime}}}\) corresponding to the \(k^{\prime}\)th natural frequency and \(w_{k^{\prime}}\) is the \(k^{\prime}\)th mode shape, which satisfies:  
\[(\mathbf{K}-w_{k^{\prime}}^{2}\mathbf{M})\vec{\varphi_{k^{\prime}}}=0 \tag{21}\]  
The modal damping ratio is defined as:  
\[\xi_{k^{\prime}}=\frac{c_{k^{\prime}}}{2w_{k^{\prime}}m_{k^{\prime}}} \tag{22}\]  
where the modal mass matrix \(\mathbf{M}_{r}\), modal stiffness matrix \(\mathbf{K}_{r}\), and modal damping matrix \(\mathbf{C}_{r}\) satisfy:  
\[\mathbf{K}_{r}=\mathbf{M}_{r}\mathbf{\Lambda}=\mathbf{M}_{r}\begin{bmatrix}w _{1}^{2}&\cdots&0&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&w_{k^{\prime}}^{2}&\cdots&0\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ 0&\cdots&0&\cdots&w_{N}^{2}\end{bmatrix} \tag{23}\]  
Figure 6: Three-DOF system model with external excitation.  
where \(\mathbf{\Phi}=[\vec{\varphi_{1}},\vec{\varphi_{2}},\dots,\vec{\varphi_{k}},\dots,\vec{ \varphi_{N}}]\) is the modal shape matrix.
###### Abstract  
Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C\({}_{3}\)H\({}_{12}\)Si\({}_{3}\)), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650-800 \({}^{\circ}\)C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of -0.5 A/s and minimal surface roughness of -5 A. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (-2.7) and a minimal absorption coefficient (<200 cm\({}^{-1}\)) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies.  
silicon carbide; thin film; chemical vapor deposition; single-source precursor; NIR absorption; photoluminescence excitation spectroscopy; photonic integrated circuits +
Footnote †: journal: Applied sciences
###### Abstract  
Load balancing between required power demand and the available generation capacity is the main task of demand response for a smart grid. Matching between the objectives of users and utilities is the main gap that should be addressed in the demand response context. In this paper, a multi-user optimal load scheduling is proposed to benefit both utility companies and users. Different objectives are considered to form a multi-objective artificial hummingbird algorithm (MAHA). The cost of energy consumption, peak of load, and user inconvenience are the main objectives considered in this work. A hybrid multi-criteria decision making method is considered to select the dominance solutions. This approach is based on the removal effects of criteria (MERECs) and is utilized for deriving appropriate weights of various criteria. Next, the Vles Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method is used to find the best solution of load scheduling from a set of Pareto front solutions produced by MAHA. Multiple pricing schemes are applied in this work, namely the time of use (ToU) and adaptive consumption level pricing scheme (ACLPS), to test the proposed system with regards to different pricing rates. Furthermore, non-cooperative and cooperative users' working schemes are considered to overcome the issue of making a new peak load time through shifting the user load from the peak to off-peak period to realize minimum energy cost. The results demonstrate 81% cost savings for the proposed method with the cooperative mode while using ACLPS and 40% savings regarding ToU. Furthermore, the peak saving for the same mode of operation provides about 68% and 64% for ACLPs and ToU, respectively. The finding of this work has been validated against other related contributions to examine the significance of the proposed technique. The analyses in this research have concluded that the presented approach has realized a remarkable saving for the peak power intervals and energy cost while maintaining an acceptable range of the customer inconvenience level.  
MEREC; load scheduling; smart grid; demand response; multi-objective optimization 2024
## 2 Experimental Investigation
### Structural Characterization  
The surface morphology and the elemental composition were studied by a scanning electron microscope (SEM) \"HIROX SH-5500P\" (Jyfel Corporation--Hirox Europe, Limonest, France) (Figure 2c) with an integrated Energy-Dispersive X-Ray Spectroscopy (EDS) system \"QUANTAX 100 Advanced\"--Bruker. The SEM has an accelerating voltage range of 1-30 kV. The investigation was conducted at 20 kV.  
The microstructure of the samples was analyzed using a \"MIT500\" (Chongqing Optec Instrument Co., Chongqing, China) microscope at magnifications up to 1000\(\times\) and photographed with a DV500 digital camera. Metallographic samples were prepared according to a standard procedure. The microstructure of titanium samples was revealed by immersing them in the following solution: 1 mL HNO\({}_{3}\) + 1 ml HF + 48 mL H\({}_{2}\)O.  
An X-ray structural analysis of the investigated alloys was made using the powder X-ray diffraction method. The investigations were performed with a Bruker D8 Advance powder X-ray diffractometer (Bruker AXS, Karlsruhe, Germany) with a copper anode (Figure 2d). The Bruker DIFFRAC.EVA v4 program and ICDD PDF-2 (2021) comparison database with reference measurements of inorganic compounds were used to determine the qualitative and quantitative phases of analysis. The crystallite size was calculated along a single line using the Scherrer equation and a Bruker Topas v4.2 diffraction profile analysis program. The surface topography was studied by means of atomic force microscopy (AFM). AFM imaging was performed on the NanoScope V system (Bruker Ltd., Bremen, Germany) operating in tapping mode in air at room temperature. Silicon cantilevers (Tap 300 Al-G, Budget Sensors, Innovative Solutions Ltd., Sofia, Bulgaria) with a 10-nm-thick aluminum reflex coating were used. The scanning rate was set at 0.5 Hz, and a scanning size of 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m and 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m was applied. The images were taken in the highest possible resolution mode of the AFM, which is 512 \(\times\) 512 pixels in JPEG format, using the NanoScope software v1.40r1.
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 2 Related Work
Since 2018, the transformer [27] has achieved great success in the field of natural language processing and has gradually been applied to image segmentation. The transformer has powerful feature extraction capabilities, which can simultaneously model both global and local features of an image. These abilities enable it to perform well in semantic segmentation, and many high-precision semantic segmentation models have emerged, such as SETR [28], SegFormer [29], MaskFormer [30], Mask2 Former [31], Lawin Transformer [32], ViT Adapter [33], and Mask DINO [34]. The time complexity of the transformer model mainly considers the self-attention and feedforward neural networks. When the number of input pixels is \(n\), the actual complexity of the self-attention is usually \(O\left(n\times L\right)\), where \(L\) is the length of the input sequence. For feedforward neural networks, the time complexity is usually proportional to the size of the input and the number of weight parameters. Transformers have higher time complexity compared to convolutional neural networks in general. However, when used for segmentation tasks, these models are usually easier to achieve higher accuracy than convolutional neural network models. When directly applying the aforementioned models to the task of cloud detection in remote sensing images, certain challenges persist. These include difficulties in distinguishing between thin clouds and surfaces, the intricacy of detecting minute cloud formations, and the potential for misidentifying bright surfaces or icebergs as clouds. To address these issues, it becomes imperative to enhance or reconfigure models tailored to natural image data, enabling their effective application to remote sensing image cloud detection tasks. A cloud detection method CloudViT [35] for a lightweight vision transformer was proposed by [PERSON] et al. in 2023, which improves the vision transformer and improves model accuracy while maintaining a small computational and parameter size. Our previous iterations such as Cloudformer [7] and CloudformerV2 [8] have effectively tackled the challenge of detecting small clouds, resulting in improved cloud detection accuracy and faster training inference. The overarching framework of related work is shown in Figure 1.
## 5 Analysis
### Identification of Plugging Based on \(Q_{c}\)/\(Q_{m}\)  
The best-calculated capacity and the associated plugging condition were determined using each design method for each pile individually. This was determined based on the performance of \(Q_{c}\)/\(Q_{m}\) and how close is the value to the ideal value of 1. For example, for a certain pile, if the \(Q_{c}\)/\(Q_{m}\) values for a design method for the unplugged, plugged, and internal plug conditions were 1.10, 1.85, and 1.47, respectively, it was assumed that this pile was unplugged since 1.10 is closest to the ideal value of 1.00.  
The measured capacity (\(Q_{m}\)) was plotted versus the best-calculated capacity (\(Q_{c}\)) in Figure 3 along with the identified plugging condition which helps identify the overall performance of all design methods and their average. It is noteworthy that the data showed significant scatter with a standard deviation ranging between 0.53 and 1.01. The scatter is attributed to a combination of factors including (1) significant variation in the calculated  
Figure 3: Calculated (\(Q_{c}\)) vs. interpreted (aka measured, \(Q_{m}\)) capacities for the best plugging conditions for each design method. Statics of \(Q_{c}\)/\(Q_{m}\) are shown in the inset tables.  
capacities using the different plugging conditions and design methods, (2) quality of the geotechnical data, (3) differences in the procedures employed to conduct the static load tests; and (4) absence of information related to pile setup. Nevertheless, Figure 3 was considered as the reference for comparing any guidance resulting from this analysis, since it represents the best achievable performance given the available data. Once again, inset tables summarize the plugging condition as well as the overall performance of each design method are presented in Figure 3. Several observations are possible. First, the majority of cases are classified as unplugged. Second, only a few cases are classified as internal plug, with only 3-12% of cases being classified as internal plug. Finally, the data in Figure 3 exhibit less scatter than Figure 2, but that is to be expected.  
The data in Figure 3 represent the capacity corresponding to the plugging condition closest to the interpreted value, for each design method. It was observed that the unplugged condition had superior performance compared to the other plugging conditions. This was further investigated and statistically proven for large diameter open-ended piles in the analysis presented by [PERSON] et al. [49].  
Design methods did not always agree on whether a pile is plugged, unplugged, or internally plugged, so a voting system was used to identify the onset of plugging based on the majority of votes by the four design methods.
## 2 Materials and Methods
### BSA Release Kinetics  
In vitro release profiles of BSA from PLGA-based MPs were evaluated in simulated vitreous body (SVB), which was prepared following a previously published method [21]. Briefly, agar was dissolved in hot distilled water (0.4% \(w/v\)) and, afterwards, HA (1600 kDa) was added (0.5 % \(w/v\)), along with a few drops of sodium azide solution (0.02% \(w/v\)) as a preservative. For each experiment, a dispersion containing 25 mg of MPs in SVB (weight ratio = 1:20) was poured into a vial; then, 1 mL of PBS at pH 7.4 was poured onto the gel and the system was incubated at 37 \({}^{\circ}\)C in a thermostatic bath. At predetermined times, 500 \(\upmu\)L of supernatant was withdrawn and replaced with fresh PBS, while monitoring the pH of the release medium with litmus paper. BSA was quantified by the Bradford assay as described in Section 2.4. Results are expressed as fraction of BSA released \(\pm\) the standard deviation of three replicas.  
The main phenomena dictating protein release were estimated by modeling the released BSA fraction through the equation proposed by [PERSON] and [PERSON], adapted for the MPs produced in this work [22]. In brief, BSA release from PLGA-based MPs was considered to be mainly governed by two major mechanisms, namely solubilization and matrix erosion. The first refers to protein dissolution at the interface between MP porosities/surface and the release medium, which is primarily dictated by protein diffusion through aqueous domains within MP evolving macropores [23]. The second mechanism considers the fraction of released BSA controlled by matrix bulk erosion, which is due to PLGA degradation and POLOX solubilization.  
The first term is defined by Equation (1):  
\[\varphi_{S}=\varphi_{S,\infty}[1-exp(-k_{S}t)], \tag{1}\]  
where \(\varphi_{S}\) is the fraction of BSA released due to solubilization at time \(t\) [day]; \(\varphi_{S,\infty}\), is BSA fraction released after a virtually infinite time if no other phenomena contribute to BSA release; \(k_{S}\) [day\({}^{-1}\)] is the solubilization rate constant.  
Erosion-controlled release refers to \((1-\varphi_{S})\), which is the fraction of the protein entrapped within MPs, which is described by the following Equation (2):  
\[\varphi_{E}=(1-\varphi_{S})\frac{exp[k_{E}(t-t_{MAX})]}{1+exp[k_{E}(t-t_{ MAX})]}. \tag{2}\]  
Here, \(k_{E}\) [day\({}^{-1}\)] is the erosion constant, which takes into account the loss of matter due to both PLGA autocatalytic degradation and POLOX dissolution; \(t_{MAX}\) [day] is the time at maximum release rate. The sum of Equations (1) and (2) provides the total released BSA fraction. The fraction, therefore, is given by the sum of the contributions:  
\[\varphi_{TOT}=\varphi_{S,\infty}[1-exp(-k_{S}t)]+(1-\varphi_{S})\frac{exp[k_ {E}(t-t_{MAX})]}{1+exp[k_{E}(t-t_{MAX})]} \tag{3}\]  
Equation (3) has been solved by nonlinear least squares fitting using a 4 th-order Runge-Kutta method, with \(\varphi_{S,\infty}\), \(k_{S}\), \(k_{E}\) and \(t_{MAX}\) as adjustable parameters.