esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Remote Sensing Techniques
### Doppler Radar  
Doppler radar wind measurements do not actually represent the temporal and spatial scales of wind gusts as defined here, because the Doppler radar resolution is of the order of 200-300 m. However, the advantage of Doppler radars is the long measurement range (up to about 150 km) and the availability of measurements from rainy conditions, which are a challenge for Doppler lidars, as seen on the second day in Figure 6. Hence, a Doppler radar can be an essential tool in nowcasting approaching gust fronts related to deep convection. Gust fronts can indeed be observed by a Doppler radar when there are insects and other passive material targets in the lower troposphere [37; 89].  
Figure 6: Time–height cross-sections of (**a**) the wind-gust speed (\(U_{max,L}\)) and (**b**) gust factor (\(G_{L}\)) from the lidar measurements after applying the despiking procedure. For comparison, the gust factors (\(G_{S}\)) from the meteorological mast are shown in (**c**). The bottom panel shows the stability conditions based on the inverse of the Obukhov length during each 10-min sample: dark red: very unstable (\(-0.01>L^{-1}\geq-0.02\)), orange: unstable (\(-0.005>L^{-1}\geq-0.01\)), yellow: near neutral unstable (\(-0.002>L^{-1}\geq-0.005\)), green: neutral (\(|L^{-1}|\leq 0.002\)), light blue: near neutral stable (\(0.005\geq L^{-1}>0.002\)), blue: stable (\(0.02\geq L^{-1}>0.005\)) and dark blue: very stable (\(0.1\geq L^{-1}>0.02\)). The criteria for stability groups are based on [88]. Redrawn with permission from [39].  
However, it is important to note that the duration and the horizontal and vertical scales of gust fronts are much larger than the gusts addressed in this paper. In [37; 89], they lasted for several minutes, had a horizontal extent of a few kilometers and encountered a depth from a few hundred meters up to two kilometers or even more. These types of gravity-driven gust fronts associated with downdrafts from deep convection cannot be regarded as typical atmospheric boundary-layer wind gusts, but require different spatial and temporal scales to represent their physical processes and their effect on structures.
# Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
## 4 Design Method
### Limit States  
The ultimate limit state means that the structure or structural component reaches the maximum load-bearing capacity or reaches the limit state of deformation, which is no longer suitable for load-bearing applications [32]. The following states are considered the  
Figure 10: Required outer diameters of rod 6 under yielding and buckling conditions.  
Figure 9: Required outer diameters of the rods as the wind load increases.  
ultimate limit states: (1) the cables have broken, (2) the triangular brackets have undergone buckling or yielding, (3) the structure or components have overturned or slipped, and (4) large vibrations causing serious damage have occurred.  
The structure or component should have a normal service or durability performance under the serviceability limit state [32]. Under the serviceability limit state, the structure or structural components should have (1) no excessive midspan deformation compared to the criteria, (2) no partial damage, including cracks, that affects the normal use or durability of PV modules, and (3) no strong WIV that affects normal use or fatigue life.  
In China, the designs of cables and triangle brackets were calculated under the ultimate limit state using Equation (7). The deformation of the structure at the midspan was checked using Equation (8) under the serviceability limit state: Equation (9) yields the following combination method for different types of loads [32; 37]:  
\[\gamma_{\rm o}S_{\rm d}\leq\frac{R_{\rm tk}}{\gamma_{\rm R}} \tag{7}\]  
\[S_{\rm d}\leq C \tag{8}\]  
\[S_{\rm d}=\gamma_{\rm G}S_{\rm GK}+\gamma_{\rm P}S_{\rm PK}+\gamma_{\rm w}S_{\rm WK} \tag{9}\]  
where the importance factor of the structure \(\gamma_{\rm o}\) is 1.0 as suggested by Chinese standards [32]; \(R_{\rm tk-C}\) and \(R_{\rm tk-B}\) are the resistance force of the cables and triangle brackets, respectively; \(\gamma_{\rm R-C}\) and \(\gamma_{\rm R-B}\) are the safety factor of the cables and triangle brackets, which are set as 2.0 and 1.7, respectively; \(C\) is the criterion of the midspan deformation; \(S_{\rm d}\) is the load combination effect (design value of the CSPV); \(S_{\rm GK}\), \(S_{\rm PK}\), and \(S_{\rm WK}\) are the load effects of self-weight, prestressed load, and wind load, respectively; and \(\gamma_{\rm G}\), \(\gamma_{\rm P}\), and \(\gamma_{\rm w}\) are the load factors of self-weight, prestressed load, and wind load, respectively. The load effect refers to the internal force and deformation of the structure caused by the self-weight of the PV modules, prestressed load, or wind load \(w_{\rm k}\). Table 3 lists the load coefficients of the structure for the two limit states.
## 2 Dynamic Time Warping
A solution is sought to determine the optimal matching path using dynamic programming and recursive methods [16]. \[D(i,j)\ =\ Dist(i,j)+\min[D(i-1,j),D(i,j-1),D(i-1,j-1)] \tag{4}\]  
The cumulative distance \(D(i,j)\) is the sum of the distance \(Dist(i,j)\) from the current grid point and the cumulative distance from the nearest adjacent element to that position. Starting from the initial point of \((1,1)\) and in accordance with the three aforementioned constraints, nodes that satisfy the conditions are iteratively searched until the end point \((n,m)\) is reached, resulting in the best matching path within the matrix \(\mathbf{M}\). The average sum of the distance values along this final optimal matching path effectively expresses the similarity between the time series \(a\) and \(b\). A smaller average distance value signifies higher similarity and closer resemblance between the two time series, whereas a larger value indicates lower similarity. The raw signal captured by the pressure sensor on the axial piston pump is shown in Figure 3. It is evident that the waveform exhibits approximate periodicity, based on which waveform segmentation is performed. However, employing a conventional fixed-length segmentation method leads to error accumulation. To address this issue, we employ the DTW approach for waveform segmentation. A complete pulsation cycle data segment was selected from the original pressure pulsation signal as a matching template for \(T\). Subsequently, this template was used to segment the overall signal data and create a dataset. A flowchart of the algorithm is presented in Figure 4. The objective is to identify the segment within the original signal, denoted as \(S\), that exhibits the highest similarity to the template \(T\). To achieve this, the DTW algorithm was employed to calculate the similarity between the template and the signal, starting from a designated position, \(S_{p}\). Throughout this process, the algorithm adhered to specific criteria, including a predefined similarity threshold \(t_{s}\), minimum allowable output length \(L_{\min}\), and maximum allowable output length \(L_{\max}\). The algorithm iteratively adjusts the starting position \(S_{p}\) based on the computed similarity. Ultimately, the output consists of a set of start and end points that define the waveform segment [14]. The DTW algorithm leverages dynamic programming and adopts a flexible path-alignment strategy to handle matching problems between time series of varying lengths. This ensures that the segmented waveforms exhibit high similarity while demonstrating robustness against noise. Figure 3: Time domain waveform plot of the original pressure signal.
# Performance-Based Analysis of Single-Layer Cylindrical Steel Reticulated Shells in Fire
## 5 Structural Response Analysis in Different Fire Scenarios
### Displacement Analysis of the Cylindrical Latticed Shell
It can be seen from Figure 20a that the thermal expansion of structural members causes upward displacement of nodes with the rise of temperature in 0-650 s. The thermal expansion is the main factor of structural deformation. In 650-1350 s, the temperature of the members is relatively stable and basically lower than 500 \({}^{\circ}\)C. The elasticity modulus of the members reached to 75% of the elasticity modulus of steel. The yield strength is basically not reduced and the displacement of the node remains relatively stable. In 1350-1863 s, when the temperature is over 500 \({}^{\circ}\)C, the elastic modulus and yield strength of the steel are reduced by 50%. The internal forces of the structure redistributes at 1863 s. The steel members enter into the plastic state. The node displacement suddenly increases to 1.0 m at 1863 s. The reticulated shell structure reached to the limit state and lost its bearing capacity. Among the three fire scenarios, the local temperature rise caused by the fire source being in the corner makes the structure the most dangerous. In the fire development stage, each node produces an upward displacement due to the thermal expansion effect of the steel structure, which is caused by the heating of the roof. The thermal expansion effect increases with a decrease in the distance to the fire source. The displacement value increases with an increase in the upward displacement of the joint. During the stationary stage of the fire, each node reaches maximum displacement and remains in a relatively stable interval. Then the fire source declines slowly, and thermal expansion weakens with each node slowly returning to its original place. Throughout the entire fire process, the node's maximum displacement never reaches the limits specified by the failure criteria. The entire structure remains stable. In the meantime, thermal expansion of the pipes causes the upward displacement of the components due to the effects of gravity load. Therefore, the deflection of the structure under a load is smaller than at normal temperatures, which is beneficial to structural stress in the initial stages of a fire. Figure 19: Structural deformation under fire location III.
## 3 Results and Discussion
### Content  
Utilizing the Wilcoxon method within the SPSS software, we obtained four significance values (Sig.) of 0.786, 0.858, 0.252, and 0.218, with all the \(p\)-values exceeding 0.05 (see Table 6). Furthermore, as presented in Table 7, the mean closely approached or exceeded a value of 6.  
Compared to OUI, it becomes evident that the MUI displays different users' content on a single interface page, necessitating the redesign of the UI. This situation has the potential to lead to misinterpretation or confusion due to the presence of multiple content streams. Nevertheless, our analysis of the questionnaires reveals that the readability of the content, including text, images, and GIFs, was not significantly affected in this scenario.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Item** & **N** & **Mean** & **SD** & **Min.** & **Max.** & **Median** \\ \hline \multirow{2}{*}{EQ9} & OUI & 22 & 5.77 & 1.631 & 1 & 7 & 6.00 \\ \cline{2-7}  & MUI & 22 & 6.00 & 1.234 & 3 & 7 & 6.50 \\ \hline \multirow{2}{*}{EQ10} & OUI & 22 & 6.05 & 1.676 & 2 & 7 & 7.00 \\ \cline{2-7}  & MUI & 22 & 6.23 & 1.232 & 2 & 7 & 7.00 \\ \hline \multirow{2}{*}{EQ11} & OUI & 22 & 6.23 & 0.973 & 4 & 7 & 6.50 \\ \cline{2-7}  & MUI & 22 & 6.55 & 0.671 & 5 & 7 & 7.00 \\ \hline \multirow{2}{*}{EQ12} & OUI & 22 & 6.32 & 0.945 & 4 & 7 & 7.00 \\ \cline{2-7}  & MUI & 22 & 6.59 & 0.796 & 4 & 7 & 7.00 \\ \hline \hline \end{tabular}  
* EQ9: Easy to read the texts of the ingredients list. EQ10: Easy to read the texts of the cooking step unit. EQ11: Easy to see the pictures of the cooking step unit. EQ12: Easy to watch the GIF of the cooking step unit.  
\end{table}
Table 7: Descriptive statistics for content/EQ9–EQ12.  
This indicates that the variation between the two interfaces has no effect on the information's readability or the reading experience of users, including that of text, images, and GIFs.
## 2 Material and Methods
### Methods
#### 2.2.4 Activation Functions  
Deep learning models comprise a wide range of hyperparameters that must be tuned to improve their performance. The choice of the proper activation function has been demonstrated to be critical [26]. Activation functions are defined by [27] using the following equation.  
\[\text{g: R}\rightarrow\text{R} \tag{1}\]  
\[\text{g}\]  
is a function that is differentiable almost everywhere. Its behavior consists of computing the weighted sum of input features plus a bias to decide whether the neuron is activated. A way to measure the performance of direct training is to test different activation functions and evaluate them. In this case, we have decided to use the Rectified Linear Unit (ReLU), Leaky ReLU, Swish, and Mish. In the following, we mathematically formalized these activation functions.  
ReLU was introduced by [28], which thresholds the output value at 0 when the calculations in the neuron are smaller than 0 and the value itself when it is greater or equal to 0. Equation (2) formalized this activation function.  
\[y_{i}=\left\{\begin{array}{l}\mathrm{x_{i}\ if\ x_{i}\ \geq\ 0}\\ 0\ \mathrm{if\ x_{i}<0}\end{array}\right. \tag{2}\]  
Leaky ReLU is a modification of ReLU introduced by [29]. Instead of returning 0 in the first condition, it produces a small gradient. Equation (3) describes this activation function.  
\[y_{i}=\left\{\begin{array}{l}\mathrm{x_{i}\ if\ x_{i}\ \geq\ 0}\\ \frac{x_{i}}{\alpha_{i}}\ \mathrm{if\ x_{i}<0}\end{array}\right. \tag{3}\]  
In this equation, \(\alpha_{i}\) is a parameter with a fixed value that ranges from \(1\) to \(\infty\).  
Swish is an activation function described by [30]. It seems to be a nonlinear interpolation between the linear function and ReLU with smooth behavior. Equation (4) describes this activation function.  
\[y_{i}=\mathrm{x}\cdot\sigma(\beta x) \tag{4}\]  
In the equation above, \(\sigma(z)=(1+\exp(-z))^{-1}\) is the sigmoid function, and \(\beta\) is a constant or trainable parameter.  
Mish is a self-regularized non-monotonic function introduced by [31], whose mathematical formalization can be found in Equation (5).  
\[y_{i}=\mathrm{xtanh}(\mathrm{softplus}(x))=\mathrm{xtanh}(\ln(1+e^{x})) \tag{5}\]
## 3 Discussion
### Aerosol Layers Detection
The CNN technique further improved layer detection and cloud-aerosol discrimination, always during daytime operations when the solar background was stronger, enabling better identification of cloud edges and tenuous cloud features. However, the CNN technique exhibited limitations during night, particularly in detecting subvisible features, such as thin optically shaped aerosol layers. Comparisons between the CNN technique and the operational CATS Cloud-Aerosol Detection (CAD) algorithm revealed a good agreement for clear-air bins but showed discrepancies between the cloud and aerosol classifications, especially during the day. Overall, the integration of CNN techniques and wavelet denoising enhanced the CATS lidar data by increasing the SNR, improving layer detection, and enabling the detection of more atmospheric features at finer horizontal resolutions. An example of how the CNN algorithm improved detection during the day compared to the operational CATS algorithm is represented in Figure 2. The study suggested combining traditional techniques with machine learning methods for future lidar data processing, to improve the accuracy of detection of aerosol layer and cloud properties, particularly during the day at finer horizontal resolutions, which is relevant to the air quality community. The findings emphasized the importance of machine learning tools for future lidar missions, enabling the generation of near-real-time (NRT) data products with shorter latencies and improved accuracy.
# BPMNE4 IoT: A Framework for Modeling, Executing and Monitoring IoT-Driven Processes +
## 2 Background
### Internet of Things  
The Internet of Things provides a network of interconnected physical objects (things) equipped with hardware, software, sensors, actuators and network connections that are uniquely addressable based on standard communication protocols [7; 12; 13]. These interconnected devices allow for gathering data from the physical world, processing these data, acting on the physical world and communicating with other smart things. In this article, we follow the suggestions of the _IoT Reference Model_[5] and view _Sensors_ and _Actuators_ as key components of IoT devices and environments. An IoT device may consist of multiple sensors and actuators of varying complexity. From the IoT perspective, the following basic types of devices are of interest:  
* _Sensors_ enable the detection and measurement of physical and chemical properties such as temperature, brightness, humidity, and pressure. Thereby, a sensor detects the physical quantity and transforms it with appropriate transducers (e.g., inductive, capacitive, magnetic, or piezoelectric) into an electrical output signal [14]. Sensors may be attached to physical objects, placed in the environment, or embedded in physical objects [5]. Usually, the generated sensor data is retrieved by clients on a publish/subscribe basis using web-based protocols (e.g., MQTT, RESTful API) [5; 12].
* _Actuators_ are driving elements that transform electrical signals into mechanical motion (e.g., translate, rotate and switch on/off) or changes of physical quantities such as light, pressure or temperature. In general, actuators are able to change the state of a physical entity [5]. They are often controlled and accessed by web services based on the REST paradigm and HTTP [12; 15].  
Another fundamental aspect of interconnected physical objects in the IoT is the exchange of information between these objects and with external systems. Therefore, the way data and information are structured and exchanged is crucial. In the _IoT Reference Model_[5], these aspects are defined in the _information view_ layer. This layer allows for an overview of the process information flow [5]. The following message exchanges patterns existing for IoT devices:  
* _Push pattern:_ The push pattern is a one-way communication between two parties. Interested parties receive the information they have subscribed to. This pattern can be considered as an event-driven data communication [6]. The _data consumer_ is interested in the events that take place in the physical world and waits for them to occur. The physical world is the element triggering events and informing the _data consumer_ of their occurrence [6].
* _Pull pattern:_ The _data consumer_ fetches the necessary data object when needed or the next data can be processed [16; 17]. The _data provider_ notifies the _data receiver_ as soon as new values are available.
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 2 Theoretical Background
### Criteria and Methods for the Comparison of Traffic Flow Models
[28] compared 10 single-regime models using goodness of fit and tested the models' stability by comparing how the same model parameters change on different sections of the same road. In many other studies, models are compared with a qualitative assessment only, e.g., a visual check is made of the match between the models and empirical data, limitations are identified, and model properties are assessed [4; 17; 29]. The conclusion from the review of the literature is that while researchers are in agreement about the criteria of a good model of the fundamental relationship, there is no agreement as to how traffic flow models should be assessed or compared. There are no commonly agreed criteria that would apply to the assessment, comparison, or benchmarking of traffic flow models. Similarly, ways to assess particular criteria are not agreed, and it is usually up to the individual interpretation of the researcher.
# Seismic Analysis of a Large LNG Tank Considering Different Site Conditions
## 4 Seismic Analysis of a Large Liquefied Natural Gas (LNG) Tank
### Records Selection  
The Chinese seismic codes for large LNG storage tanks are based on the seismic codes of building structures [37]. The normative theoretical model, which is derived from the [PERSON] rigid two-particle theoretical model, accounts for the influence of the elastic tank wall. Furthermore, the Chinese seismic code classifies the design objective for large LNG tanks into the second of three tiers, which means designing for repairable damage without collapse. In addition, the Chinese seismic code classifies seismic sites into four classes depending on soil stability, shear wave velocity, and thickness of overburden. Class I contains the strongest soil, and soil strength gradually weakens from Class II to Class IV. Structural behaviour observed during past earthquakes have shown that the same type of structure is often damaged to different degrees depending on the difference in soil layers that structures reside on. Selecting seismic records and response spectra according to corresponding sites leads to more reasonable response calculation results. Therefore, it is important to study the influence of site differences on system response. In the past, no one has studied the impact of location on large LNG tanks under earthquake loading in detail. In this paper, the seismic response analysis of a large LNG prestressed storage tank is performed under 12 seismic waves for four different classes of sites. Based on guidelines for seismic wave selection [37], 12 seismic waves are selected and scaled to ensure compatibility with the design spectrum at the chosen site. The scaling and matching process has been performed independently for each input motion. The spectra of the scaled seismic motions are compatible with the Chinese design response spectrum. The details for 12 ground motions  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **25\% Liquid Level** & **50\% Liquid Level** & **75\% Liquid Level** & **100\% Liquid Level** \\ \hline specification & 4.637 & 3.099 & 2.381 & 1.923 \\ simulation result & 4.626 & 3.065 & 2.362 & 1.916 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The basic natural frequency of solid-liquid coupling under different liquid volume.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **25\% Liquid Level** & **50\% Liquid Level** & **75\% Liquid Level** & **100\% Liquid Level** \\ \hline specification & 0.0799 & 0.098 & 0.103 & 0.105 \\ simulation result & 0.081 & 0.092 & 0.098 & 0.108 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The basic natural frequency of liquid sloshing under different liquid volume.  
(3 earthquakes in each site) are shown in Table 9, and the acceleration response spectra for ground motions as well as the code acceleration response spectra are shown in Figure 6.
## 6 Technical Challenges and Future Directions  
Our analysis has revealed that the passage to the F5G era poses significant challenges for the network operators, as a new ecosystem needs to emerge in order to support the new services and applications. In this section, we delineate the main technical challenges and future directions for the fixed telecom industry, which are: energy efficiency, capacity scaling, data security/integrity of communications, dense connectivity, low-cost manufacture of integrated optical components and cost-efficient, finely tuned optical switching. Addressing each technical challenge in a cost-effective way generates an opportunity for further investigation and, hopefully, product commercialization.