esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response of a Large LNG Storage Tank Based on a Shaking Table Test
## 1 Introduction
[PERSON] and [PERSON] [16] used ANSYS to analyze the main parameters affecting the dynamic behavior of the cylindrical water tank. [PERSON] [17] et al. studied the seismic response of the liquid storage tank isolated by different isolation devices under the excitation of near-fault ground motion. The above research results have certain guiding value for the isolation design of LNG storage tanks. However, from an overall point of view, the existing seismic research of LNG storage tanks revolves around numerical simulation and simplified mechanical model. The conclusions of these two studies lack the verification of experimental results. [PERSON] et al. [18,19] proposed a simplified mechanical model of LNG storage tank considering the thermal insulation layer, which simplified the thermal insulation layer as a spring-damping element. [PERSON] [20,21] et al. proposed a limited sliding isolation device and applied it in a rectangular liquid storage container. They found that the device could not only play a good isolation effect, but also ensure that the displacement of isolation layer would not exceed the limit. [PERSON] and [PERSON] [22] present the capabilities and applications of an advanced laser scanning vibrometer system utilizing a non-contact method and discuss the results of experimental modal analysis of selected lightweight structures using the instrument. The results show that the method can measure the number of points and their resolution and precision. In view of this, this paper carried out the storage tank shaking table test, combined with the simplified mechanical model of the LNG storage tank, and used the numerical calculation method to calculate the corresponding seismic dynamic response. By comparing and analyzing the test results and numerical results, the validity and rationality of the simplified mechanical model of the LNG storage tank are verified, and the impact of the isolation bearing on the isolation effect of the storage tank is analyzed, which provides technical support for the isolation design of the LNG storage tank.
## 3 Discussion
### Single-Energy CT
The PL approach can be widely used to both prospectively and retrospectively measure patient bone quality for research and clinical investigations related to motor vehicle crash injuries, falls, and aging, especially since lumbar vBMD was computed from PL-QCT scans with accuracy comparable to QCT. In another recent study, 1175 participants had QCT for both BMD and low-dose chest CT. For vertebral body segmentation and labeling, two convolutional neural network (CNN) models were used, respectively. Vertebral BMD was calculated using a histogram approach, with parasigninal muscle and surrounding fat serving as references (2018). By using the subcutaneous fat and parasigninal muscle as internal references and automatically setting their coordinate ROIs for BMD calibration, a recent study accomplished the aim of predicting cage placement after extreme lateral interbody fusion (2018). The authors used the following steps to assess the analysis: the best muscle and fat ROIs were automatically placed for calibration based on a priority algorithm in locations closer to the vertebrae position provided by the user; the segmentation of muscle and fat tissue was performed using an HU range; and followed by the construction of a convolution map based on a kernel pyramid for better tissue boundary robustness. The authors reported that the PL-QCT system can improve the accuracy of vBMD measurements, possibly contributing to the evaluation of patients who underwent cage placement after extreme lateral interbody fusion [31]. The studies included emphasize the challenges related to CTHU variance among scanners, particularly in the context of BMD measurement. Various calibration methods are explored, ranging from manual to automatic ROI selection, with a particular focus on enhancing accuracy through innovative techniques like asynchronous PB calibration and PL internal tissue calibration. The adoption of automatic ROI selection is proposed as a pivotal strategy to improve the precision and repeatability of BMD measurements, showcasing the potential of these advancements for reliable clinical applications in detecting decreased BMD across diverse patient groups.
## 2 Methodology
### Knowledge and Definition
Generally, we measure the value of the vector \(V_{\text{LOS}}\) in the orbital coordinate system; i.e., \(S_{o-LOS}\), and then obtain \(S_{a-LOS}\) by (7). For spaceborne SAR, the imaging performance is affected by the squint and look angles of the antenna beam in the orbital coordinate system, and the on-orbit measurement also directly measures these two angles, and then obtains the nominal azimuth angle and elevation angle according to the conversion relationship (7). If the squint and look angles do not meet the imaging requirements, the beam pointing is adjusted by adjusting the satellite attitude angles.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 4 Stability Analysis on Traffic Collision Risk
### Risk Local Stability Analysis  
As per Section 3.1, the criterion of risk local stability should meet the following criterion:  
\[\int_{-\infty}^{+\infty}\left|\mathcal{L}^{-1}(\frac{\mathcal{L}(\Delta r_{n}( t))}{Y_{n-1}(s)})e^{-st}\right|\,\mathrm{d}t<\infty \tag{23}\]  
which leads to:  
\[Re(\delta_{i})<0,\ \ \forall\ \text{poles}\ \delta_{i}\ \text{of}\ \ \frac{\mathcal{L}(\Delta r_{n}(t))}{Y_{n-1}(s)},\ \ s=j\omega,\ \ i=1,2,3... \tag{24}\]  
where \(Re(.)\) calculates the real part of the poles.  
**Remark 1**.: _If a vehicle is traffic-local-stable, as governed by the car-following model, it will also be locally stable with respect to collision risk, and vice versa._  
Proof.: Firstly, we show that being traffic-local-stable is sufficient for being risk-local-stable. Based on the definition of traffic local stability (Equation (4)), if the traffic is local-stable, we will have:  
\[y_{n-1}(t)\to 0,\ \dot{y}_{n}(t)\to 0\ \text{and}\ y_{n}(t)\to 0,\ \text{when}\ t\to\infty \tag{25}\]  
According to Equation (16), when Equation (25) holds, the following equation is satisfied.  
\[\lim_{t\to\infty}r_{n}(t)=\frac{v^{eq}}{d^{eq}}=r_{n}^{eq} \tag{26}\]  
Hence, we have  
\[r_{n}(t)-r_{n}^{eq}\to 0,\ \text{when}\ t\to\infty \tag{27}\]  
which implies the risk is local-stable.  
Then, we show that being risk local-stable is sufficient for being traffic-local-stable. If risk is local-stable, we have Equation (27). Combining Equation (27) and Equation (16), we have  
\[v^{eq}y_{n}(t)+d^{eq}\dot{y}_{n}(t)-v^{eq}y_{n-1}(t)\to 0,\ \text{when}\ t\to\infty \tag{28}\]  
Further, for the risk local stability, the leading vehicle could be in the equilibrium state, i.e., \(r_{n-1}(t)=r^{eq}\) and \(y_{n-1}(t)=0\), while risk local stability only focuses on the evolution of the perturbation for vehicle \(n\) over time. Then, Equation (28) is further simplified to  
\[v^{eq}y_{n}(t)+d^{eq}\dot{y}_{n}(t)\to 0,\ \text{when}\ t\to\infty. \tag{29}\]  
Consider a large enough \(t\), we have the following ordinary differential equation (ODE):  
\[v^{eq}y_{n}(t)+d^{eq}\dot{y}_{n}(t)=0 \tag{30}\]  
The solution to the equation is either \(y_{n}(t)=0\) or \(y_{n}(t)=Ce^{-\frac{rt^{eq}}{2 rt}t}\), where the constant \(C\) equals \(y_{n}(0)\). Both solutions imply traffic local stability when \(t\) goes to infinity.  
The proof is complete.
## 2 Materials and Methods
### Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR)  
Moderate amounts of liver tissue were homogenized using a Bioprep-24 homogenizer, and RNA was extracted with Trizol Reagent (Thermo Fisher Scientific; Waltham, MA, USA). The concentration and purity of RNA were determined using a micro-spectrophotometer (Nano-300, Hangzhou Allsheng Instruments Co., Ltd., Hangzhou, Zhejiang, China). RNA was reverse-transcribed to cDNA using RevertAid First Stand cDNA Synthesis Kit with oligo d(T) primer (Applied Biosystems, Foster City, CA, USA). Target gene expression levels (IRS-1, GLUT-4, PPAR, SREBP-1c, TNF-\(\alpha\), and IL-1\(\beta\)) were measured using SYBR(r) Select Master Mix with Step One Plus Real-Time PCR System (Thermo Fisher Scientific, New York, NY, USA). The target gene expression was normalized with \(\beta\)-actin and calculated using the 2\({}^{-\Delta\Delta\Omega}\) method. The primers used for RT-qPCR are listed in Table 1. RT-qPCR was performed by the following cycle conditions: an initial holding at 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 15 s and annealing/extension at 60 \({}^{\circ}\)C for 1 min. IL-1\(\beta\) (0),  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Gene** & **Sequence (\(\boldsymbol{\zeta^{\prime}}\)-\(\boldsymbol{\zeta^{\prime}}\))** \\ \hline \multirow{2}{*}{IRS-1} & Forward: CCCGCGTTCAAGCAGGTCTG \\  & Reverse: CCCGTAGATCCAATCAGGT \\ \hline \multirow{2}{*}{GLUT-4} & Forward: ACACTGCGTCCTGATTCT \\  & Reverse: CCAGCCACGTGCATTCGTA \\ \hline \multirow{2}{*}{PPARy} & Forward: GGAAGACCACCACCATTCCCTT \\  & Reverse: GTAATCAGCAACCATGGGTCA \\ \hline \multirow{2}{*}{SREBP-1c} & Forward: GCAGCCACCATCAGCCTG \\  & Reverse: CACCAGTGAGCTCTCTGTGT \\ \hline \multirow{2}{*}{TNF-\(\alpha\)} & Forward: CAGCCGGCTCTGTCTC \\  & Reverse: CGATACCCCAAGTTG \\ \hline \multirow{2}{*}{IL-1\(\beta\)} & Forward: GAAATGCACCCTTTTGACAG \\  & Reverse: TGAGCTCTCTCATAGG \\ \hline \multirow{2}{*}{\(\beta\)-actin} & Forward: GAGAAAATCTGGACCACCACC \\  & Reverse: GCACAGCCTGGATACACACGTA \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primer sequences of genes; IRS-1: Insulin receptor substrate; GLUT-4: Glucose transporter type 4; PPARy: Peroxisome proliferators-activated receptor \(\gamma\); SREBP-1c: Sterol-regulatory element-binding protein-1c; TNF-\(\alpha\): tumor necrosis factor \(\alpha\); IL-1\(\beta\): Interleukin 1\(\beta\).
## 3 Results and Discussion
### Sensory Evaluation, Odor-Active Compounds, and Other Explanatory Factors
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Volatile Compound**} & \multirow{2}{*}{**RI**} & \multirow{2}{*}{**Identification**} & \multirow{2}{*}{**Odor Description**} & \multicolumn{6}{c}{**Relative Concentration (RC) \({}^{1}\) or Detection Frequency (DP) \({}^{2}\)**} \\ \cline{5-10}  & & & & & **CONC1** & **CONC2** & **CONC3** & **HYDR1** & **HYDR2** & **COLL1** \\ \hline
31 & 1-Octanol-3-0 & 1449 & RI, MS & - & \(77\pm 5\) & \(89\pm 13\) & \(22\pm 7\) & ND & ND & ND \\
32 & Acetic acid & 1467 & RI, MS & - & ND & ND & ND & \(34\pm 150\) & ND & ND \\
33 & Metholam & 1492 & RI, MS & - & \(22\pm 2\) & \(34\pm 11\) & \(2\pm 1\) & \(11\pm 3\) & \(1\pm 0\) & ND \\
34 & 2-Decanone & 1514 & RI, MS & - & \(4\pm 2\) & \(2\pm 2\) & \(2\pm 1\) & ND & ND & ND \\
35 & 2-4-Petholam & 1531 & RI, MS & - & \(4\pm 2\) & \(2\pm 2\) & ND & ND & ND & ND \\
36 & Unknown 7 & 1543 & - & Burnt, black current & NA & NA & DF 0 & DF 0 & DF 100 & NA \\
37 & 1-Octanol & 1559 & RI+, MS, O & Sur, pungent & \(17\pm 1\) & \(19\pm 3\) & \(5\pm 1\) & ND & \(3\pm 1\) & ND \\
38 & Unknown 8 & 1955 & - & Musyst, green, pungent & NA & NA & DF 0 & DF 75 & DF 0 & NA \\
39 & 2-Undecan & 1621 & RI+, MS, O & Pungent & \(31\pm 4\) & \(50\pm 13\) & \(6\pm 2\) & ND & ND & ND \\
40 & Unknown 9 & 1649 & Berry & NA & NA & DF 0 & DF 50 & NA & NA \\
41 & 2-Methylbutanoate acid & 1687 & RI, OS, O & Musy, sulfuric & ND & 0 & ND & \(32\pm 2\) & \(6\pm 3\) & \(8\pm 3\) & \(8\pm 3\) \\
42 & 2-Dodecanone & 1727 & RI+, MS, O & Meat broth & ND & \(6\pm 6\) & ND & \(1\pm 1\) & ND & ND \\
43 & Unknown 10 & 1757 & Liquorio, copying & NA & NA & DF 75 & DF 75 & DF 0 & NA \\
44 & Unknown 11 & 2074 & Sweet, cotton candy & NA & NA & DF 50 & DF 75 & DF 100 & NA \\
45 & Unknown 12 & 2084 & Rhubark, acidic & NA & NA & DF 0 & DF 0 & DF 50 & NA \\
46 & Unknown 13 & \(>\)2186 & Solvent, like & NA & NA & DF 0 & DF 0 & DF 0 & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cont. To produce mild-tasting and -smelling fish protein products, the lipid oxidation and degradation of raw material should be assessed carefully. Lipid oxidation could be prevented, e.g., with the addition of antioxidants [52]. To limit the degradation of raw materials, appropriate storage conditions and quick processing after capture is essential. Additionally, the storage stability of final products throughout the shelf-life should be assessed carefully.
## 2 Materials and Methods
### Study Case  
#### 2.2.1 Problem Statement  
Three ATES systems located in two different aquifers in the center of Brussels are operated for heating and cooling three different recent or recently renovated buildings located in an urban reallocation zone of Brussels, named 'development zone' in Figure 1. The first one was started in March 2014 (Building 1), the second in August 2017 (Building 2), and the third (located in the deeper aquifer) in April 2020 (Building 3). The objective is to assess the potential thermal interferences between the three systems, using a groundwater and heat transport numerical model with the existing data. The same approach was previously adopted by [PERSON] et al. [3], who studied the possible interference between the two first systems. The two first systems were designed with eight wells (4 'cold wells' and 4 'warm wells') each. The third system required ten wells (5 'cold wells' and 5 'warm wells') drilled deeper and screened only in the fractured Paleozoic bedrock.  
For each system, pumping in the 'warm wells' provides heat to the building in winter. Note that heat is being stored during the summer around these wells by injection of warm water from the cooling of the building. In summer, the 'cold wells' are used to pump groundwater at the local aquifer temperature to cool the building. Cold is stored in the aquifer around these wells during winter by injection of cold water from the heating of the building. The numerical model developed previously by [PERSON] et al. [3] (using the FEFLOW(r) software) was modified to include explicitly the Paleozoic bedrock and the overlying aquitard layer between this deep aquifer and the shallow aquifer.  
Figure 1: Location map of the study case in Brussels (Belgium). Locations of the model, development zone, and the three studied buildings and their ‘cold’ and ‘warm wells’ of the ATES systems.
# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
## 1 Introduction
Paper Organization and Contribution:  
In this paper, we study the application of federation learning in wireless network traffic prediction. To achieve this goal, this paper addresses research-related issues in the following article sections. In Section 2, we discuss recent developments and applications of federation learning and wireless network traffic prediction. Then, in Section 3, we discuss the specific implementation details of the methodological techniques used in our proposed framework. Subsequently, in Section 4, we show the details of the experiments and the conclusions of the comparative analysis with existing methods. Based on the proposed FedGSA framework and the descriptions in previous articles, the contributions of this paper can be summarized as follows:  
* To balance the individuality of clients and the correlation characteristics among multiple clients to obtain a global model with better generalization capability, we propose a two-layer global aggregation scheme based on gradient similarity, which quantifies the client similarity relationship by calculating the Pearson correlation coefficient of each client's gradient to guide the weighted aggregation on the server side;
* To address the problem of statistical heterogeneity between traffic patterns collected by different clients, which can lead to difficulties in generalizing the global model, we introduce a quasi-global model and use it as an auxiliary tool in the model aggregation process;
* Considering the time-dependent characteristics of base station network traffic, we use a sliding window strategy here to represent the traffic of each time slot as a two-channel Tensor matrix, and divide the historical traffic data into adjacent time traffic data and periodic daily historical traffic data;* We conducted validation experiments on two publicly available real datasets and compared and analyzed the experimental results with existing experimental methods.
## 4 Experimentation and Results
### Computation Time and Running Time
#### 4.6.2 Real-Time Processing Capability  
These results demonstrate that the model is suitable for real-time applications. Customer behavior can be precisely tracked in real time using the YOLOv5 + DeepSORT combo, tracking customers at retail establishments with minimal delay. The real-time processing speed of alternative deep learning models like Faster R-CNN and conventional techniques like Haar cascades and Kalman filters, on the other hand, was often below 15 frames per second.
## 2 OCT-A Applications in Pediatric Population
### OCT-A in Retinopathy of Prematurity
In an effort to overcome these difficulties, new handheld OCT (HH-OCT) and OCT-A (HH-OCTA) devices are undergoing development and validation to determine the role they will play in the clinical management of patients with ROP and children with other ophthalmic pathologies [89; 94; 95; 96; 97]. In 2010, [PERSON] et al. evaluated the specific problems that arose from performing hand-held spectral domain OCT (HH-SD OCT) in children. They tested some technical corrections in the OCT device, which led to an improved image acquisition [94]. Later, [PERSON] et al. presented a prototype of a portable OCT-A and a portable ultra-wide field OCT [89]. When this device was used with a noncontact lens, only a 40\({}^{\circ}\) field of view was obtained, but when a pediatric wide-angle lens and a contact lens were used, almost a 100\({}^{\circ}\)\(\times\) 100\({}^{\circ}\) OCT and 20\({}^{\circ}\)\(\times\) 20\({}^{\circ}\) OCT-A scans could be obtained in two seconds [89]. The development of these OCT-A portable devices could improve the understanding of choroidal and retinal vasculature development. In addition, portable ultra-wide field OCTs could depict the changes that occur in the peripheral vitreoretinal interface of ROP patients [89; 94; 95; 96; 97].
## 4 Analysis of the Properties of LCPSO
### Removing the Simplifying Hypotheses
#### 4.6.2 Communication Constraints  
Obviously, communication constraints within the swarm yield to a limitation of our system: for example, our \(N\) agents will only be able to communicate with certain other agents, which will multiply the number of attractors and behaviors. We have different phenomena that will constrain the swarm:  
* Isolation of individuals: if, at any given time, one or more agents make bad choices, they may be unable to communicate with anyone, and consequently they will be unable to move following Equation (18).
* Emergence of subgroups: two opposite attractors in the group can lead to the fission of the group where all the agents are connected in two or more subgroups, so there is no more direct or indirect link between any agent \(i\) and \(j\).  
We illustrate these cases in Figure 5, with the illustration of an efficient tracking of a source, in Figure 5a, and a less efficient tracking, in Figure 5b. The source follows the environment function (1) after \(t=100\) s. We can see the importance of the setting of \(r_{eq}\) compared to \(r_{com}\). In Figure 5b, we can see the isolated agents, which are attracted by themselves, and consequently unable to move, and sub-groups of 2 robots, which are unable to track the target because they are too few. We will discuss in Section 5 the choice of these parameters, since it is directly related to the tracking performance of the target.  
Figure 4: Evolution of convergence time of the formation with the LCPSO algorithm as a function of \(c_{0}\) and \(N\) in dimension 2. \(c_{2}=0.5\), \(r_{eq}=7\) m. (**a**) \(N=7\); (**b**) \(N=15\).
# Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks
## 2 Materials and Methods  
### Tested Materials  
The beehive model used was the Langstroth [29]. Designed by [PERSON] in 1851, the Langstroth beehive is one of the most popular beehive designs globally. It offers several advantages over other hive types, including a design based on the bee space principle, modularity, efficiency in honey harvesting, standardisation, ease of inspection, maximised honey production, adaptability, longevity, and durability.  
The selection of materials for this study was guided by a multicriteria approach that prioritised ecological sustainability, regional availability, and safety. We focused on natural materials that offered a viable alternative to synthetic insulators, specifically targeting those with a reduced environmental impact. This selection process ensured that our study utilised materials that were not only environmentally friendly but also locally accessible and safe for intended applications. Given these considerations, four different insulating materials with specific thermal conductivity coefficients were chosen, all of which have a natural origin: black agglomerated cork, white agglomerated cork, wood fibres, and rock mineral wool. Black agglomerated cork is produced by using the cork's own natural resin, suberin, as a binder for the cork particles. This process not only enhances the material's insulating properties but also maintains its ecological integrity. In contrast, white agglomerated cork, also known as composite agglomerate, incorporates synthetic resins as binders. This difference in manufacturing can significantly affect its thermal and physical properties. The characteristics of these materials are presented in Table 1.