esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 FE Modeling and SSI Analysis Method  
### Modeling of the Structure  
Finite element (FE) models of 5-story and 40-story buildings were constructed. The geometry of the superstructures is shown in Figures 1 and 2, and those RC flat slab structures were based on the drawings provided by [PERSON] [12]. The structural configuration was commonly applied to both low-rise and high-rise structures. The dimensions and the strength of the main members are listed in Table 1. The substructure of both the low-rise and high-rise structures consists of two basement levels (Figure 3), and Table 2 shows the dimensions and strength of the substructure.  
Figure 1: Floor plan of typical section (unit: mm).  
Figure 2: Structural drawing of the superstructure.  
An SSI analysis using the direct method, a method that integrates the ground, foundation, and structure into a single system and directly analyzes the interaction between each component through continuum modeling, requires a long computation time due to the large number of interaction nodes [13]. To reduce computation costs, the simplified modeling technique using equivalent beam elements proposed by [PERSON] et al. [14] was applied for the modeling of columns (Equation (1)), and the rule of mixtures (RoM) concept proposed by [PERSON] et al. [15] was applied for the modeling of the slabs and walls with shell elements (Equation (2)). Finally, the equivalent material properties of the target structures were calculated based on the values presented in Tables 1 and 2. The calculation results in Tables 3 and 4, respectively, show the material properties for columns (\"MAT_CONCRETE_BEAM) and slabs and walls (\"MAT_ELASTIC). Table 5 provides the sectional properties of the columns.  
\[E_{m}=E_{cc}\bigg{(}1+\epsilon\times\frac{nA_{s}}{A_{g}}\bigg{)} \tag{1}\]  
\[E_{new\ approach}=\bigg{[}(E_{concrete})\bigg{(}\frac{V_{concrete}}{V_{total}} \bigg{)}\bigg{]}+\bigg{[}(E_{steel})\bigg{(}\frac{V_{steel}}{V_{total}}\bigg{)} \bigg{]} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**ID**} & **Dimension** & \multicolumn{2}{c}{**Strength of Concrete (MPa)**} \\ \cline{3-5}  & & **(mm)** & **1F–15F** & **16F–30F** & **31F–40F** \\ \hline \multirow{2}{*}{Column} & C1 & 1200 \(\times\) 1200 & 50 & 40 & 30 \\  & C2 & 900 \(\times\) 900 & 50 & 40 & 30 \\ \hline \multirow{2}{*}{Wall} & CW1 & THK 600 & 50 & 40 & 30 \\  & CW2 & THK 600 & 50 & 40 & 30 \\ \hline \multirow{2}{*}{Slab} & S & THK 250 & 40 & 30 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions and strength of the superstructure.  
Figure 3: Structural drawing of the substructure.
## 3 System Model
### Network Model  
We denote the time-sensitive network (TSN) as a directed graph \(G=(V,E)\), where the set \(V=ES\cup SW\) is the set of all nodes (end systems and switches) connected by physical links, and the set \(E\subseteq V\times V\) includes a set of full-duplex physical links or edges. Note that each full-duplex link is regarded as two-directional logical links denoted by ordered pairs \((v_{i},v_{j})\) and \((v_{j},v_{i})\), where the first element is the source and the latter is the destination. \(|V|\) and \(|E|\) denote the number of nodes and links, respectively.  
A physical link \((v_{i},v_{j})\) is characterized by the tuple \(<(v_{i},v_{j}).r,(v_{i},v_{j}).d>\), where \((v_{i},v_{j}).r\) represents the transmission rate of the link \((v_{i},v_{j})\), \((v_{i},v_{j}).d\) is the sum of maximum latency which is independent of the scheduling algorithm.  
An example of the TSN topology is shown in Figure 1. Each end system executes one or more types of real-time application that periodically sends flows under firm latency constraints. We assume that the whole network is precisely synchronized, that is, as long as the routes and transmission time slots are properly planned, the flows can be transmitted based on the schedule without conflict links in ideal circumstances. The controller has to gather all network information and all flow information and schedule routes and time slots. A feasible route consists of end systems, switches, and links which the flows traverse. The time slot must be reserved for the links within each flow's route.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Notation** & **Description** \\ \hline \multirow{4}{*}{_index_} & \(i,j,x,y,z\) & the index of a node \\  & \(k,m\) & the index of a flow \\  & \([i,j]\) & the index of a link \\  & \(l\) & the index of a link \\ \hline \multirow{4}{*}{_set_} & \(V\) & the vertex set \\  & \(E\) & the edge set \\  & \(ES\) & the set of all end systems \\  & \(SW\) & the set of all switches \\  & \(F\) & the set of all flows \\ \hline \multirow{4}{*}{_vector_} & \(\mathbf{src^{F}}\) & source vector \\  & \(\mathbf{dest^{F}}\) & destination vector \\ \cline{1-1}  & \(\mathbf{prd^{F}}\) & period vector \\ \cline{1-1}  & \(\mathbf{td^{F}}\) & transmission duration vector \\ \cline{1-1}  & \(\mathbf{\omega}\) & transmission window offset \\ \cline{1-1}  & & vector \\ \hline \multirow{2}{*}{_decision variable_} & \(\mathbf{r}\) & route decision vector \\ \cline{1-1}  & \(\mathbf{\varphi}\) & time slot offset vector \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
# 16-Channel Wavelength Division Multiplexers Based on Subwavelength Grating
###### Abstract  
Wavelength Division Multiplexing (WDM) plays an important role in optical interconnection. In this paper, a 16-channel WDM device is designed on a Silicon-On-Insulator (SOI) substrate by using a sub-wavelength grating (SWG) structure, which can cover O-band and C-band at the same time, and the output channel is reversely coupled from the main waveguide to realize wavelength demultiplexing. The simulation results show that the loss of our 16-channel wavelength demultiplexing device is less than 0.5 dB and the crosstalk is less than 17 dB. When WDM is performed in O-band, the transmission loss of the C-band in the bus waveguide is less than 0.05 dB and is insensitive to the grating duty cycle, with good process tolerance. The footprint of the device is 200 \(\upmu\)m \(\times\) 150 \(\upmu\)m, and the size of the single-channel filter is 200 \(\upmu\)m \(\times\) 2 \(\upmu\)m, which can realize WDM with large bandwidth in a compact structure.  
silicon photonics; (de) wavelength-division-multiplexing; subwavelength grating; directional coupler +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] 16-Channel Wavelength Division Multiplexers Based on Subwavelength Grating.  
**Appl. Sci. 2023**, 13, 1833. [[https://doi.org/10.3390/app13031833](https://doi.org/10.3390/app13031833)]([https://doi.org/10.3390/app13031833](https://doi.org/10.3390/app13031833))  
and
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## Appendix A  
### Appendix A.1  
This Table A1 presents the phase excitations for each antenna element at observation angles ranging from 165\({}^{\circ}\) to 15\({}^{\circ}\), synthesized using the Taylor method. This method focuses on controlling the sidelobe levels by adjusting the phase values of the antenna elements. The Taylor method optimizes the phase distribution to create a desired beam pattern while maintaining low sidelobe levels. The phase values in the table are determined to minimize the sidelobe amplitudes while ensuring that the main beam is directed toward the desired angle, enhancing directivity and performance.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 2 Materials and Method
### Algorithmic Principle
#### 2.2.2 Observation Equations of Free Station Control Network  
Free station control networks differ from traditional traverse or triangulation and trilateral control networks in that the stations do not necessarily have to be set up at control points. Theoretically, any number of stations can be established at any location within and around the coverage area of the control network. This is of great significance for control networks where the line of sight between control points is obstructed. Typically, there are two or more common observed targets between stations to enable them to be connected to  
Figure 2: Technical route of this paper.  
a network. Free station control network observations consist of horizontal directions and distances from the stations to their observed control points. Distances are usually derived from slope distances and vertical angles.  
Let \(j\) and \(k\) be the measuring station and the target point to be determined, respectively, and the observation equation of the horizontal observation value \(Hz_{jk}\) between them is:  
\[\hat{H}z_{jk}=\tan^{-1}\frac{\hat{Y}_{k}-\hat{Y}_{j}}{\hat{X}_{k}-\hat{X}_{j}}+ \hat{\beta}_{j} \tag{7}\]  
where \(\hat{H}z_{jk}=Hz_{jk}+v_{jk}\) is the observed value after adjustment; \(v_{jk}\) is the correction; \((\hat{X}_{j},\hat{Y}_{j})\) and \((\hat{X}_{k},\hat{Y}_{k})\) are the coordinate parameters of the station and the target point, respectively; and \(\hat{\beta}_{j}\) indicates the orientation angle parameter of the station, i.e., the coordinate azimuth of the zero direction of the horizontal circle of the total station, and \(\hat{X}=X^{0}+\hat{x},\hat{Y}=Y^{0}+\hat{y}\) and is expanded to Taylor's level:  
\[v^{\prime\prime}_{jk}=a_{jk}\hat{x}_{j}+b_{jk}\hat{y}_{j}-a_{jk}\hat{x}_{k}-b_{ jk}\hat{y}_{k}+\hat{\beta}_{j}-l_{jk} \tag{8}\]  
where \(a_{jk}=\frac{\rho^{\prime\prime}\Delta Y^{0}_{k}}{(\hat{S}^{0}_{k})^{2}}\), \(b_{jk}=-\frac{\rho^{\prime\prime}\Delta X^{0}_{k}}{(\hat{S}^{0}_{k})^{2}}\), \(l_{jk}=Hz_{jk}-\alpha^{0}_{jk}-\beta^{0}_{j}\), and \(a^{0}_{jk}=\tan^{-1}\frac{\gamma^{0}_{k}-\gamma^{0}_{j}}{\chi^{0}_{k}-\gamma^ {0}_{j}}\).  
The observation equation for the horizontal distance observation \(D_{jk}\) is:  
\[\hat{D}_{jk}=D_{jk}+v_{djk}=\sqrt{\left(\hat{X}_{k}-\hat{X}_{j}\right)^{2}+ \left(\hat{Y}_{k}-\hat{Y}_{j}\right)^{2}} \tag{9}\]  
Expanding by the Taylor series, we obtain:  
\[v_{D_{jk}}=c_{jk}\hat{x}_{j}+d_{jk}\hat{y}_{j}-c_{jk}\hat{x}_{k}-d_{jk}\hat{y}_ {k}-l_{D_{jk}} \tag{10}\]  
where \(c_{jk}=\frac{\Delta X^{0}_{jk}}{D^{0}_{jk}}\), \(d_{jk}=\frac{\Delta Y^{0}_{k}}{D^{0}_{jk}}\), \(\Delta X^{0}_{jk}=X^{0}_{k}-X^{0}_{j}\), \(\Delta Y^{0}_{jk}=Y^{0}_{k}-Y^{0}_{j}\), \(D^{0}_{jk}=\sqrt{\left(X^{0}_{k}-X^{0}_{j}\right)^{2}+\left(Y^{0}_{k}-Y^{0}_{ j}\right)^{2}}\), and \(l_{D_{jk}}=D_{jk}-D^{0}_{jk}\).
## 2 Design of Distance-Rendering Methods
### Reference Method Based on Measurements  
Different SRIRs were measured in the classroom with a spherical microphone array (SMA), _Eigenmike(r) EM32_ from MH Acoustics. The choice to use SRIRs was motivated by the flexibility of this solution. While a binaural impulse response (BRIR) only allows temporal modifications, an SRIR can be subject to spatial modifications and binaural decoding on any HRTF set. Nine SRIRs were measured for distances ranging from 1 to 7 m (\(1,1.5,2,2.5,3,4,5,6,7\) m) by changing the speaker position and with a single microphone position. The different measurements were performed on an axis shifted 60 cm from the median line of the room to avoid spatial symmetry of the first reflections. This precaution was taken to favor decorrelation of the binaural stimuli, which also optimized their externalization [18]. These SRIRs were converted from a fourth-order ambisonic signal to BRIRs. Here, an \"energy-preserving\" decoding method was used [19]. In order to decode the SRIR to binaural format, the spherical harmonic excitation of the fourth-order ambisonic signal was sampled on virtual speakers' positions. A virtual spherical array of 24 uniformly distributed speakers, following an equal-area grid, was used and the listener was considered at its center. To produce the final BRIR signals, each of the speakers' signals was filtered with the head-related transfer function (HRTF) of a _Neumann KUI100_ dummy head, according to its direction. This set of nine resulting BRIRs will be referred to as the \"reference room\". The BRIR corresponding to a distance of 1 meter was used as an initial impulse response for the two following distance-rendering models.
## 2 Current Status of Spaceborne GNSS-R Microsatellites  
Over the past two decades, with the continuous development of science and technology, countries around the world have launched many spaceborne GNSS-R microsatellites. In 2003, the successful launch of the UK-DMC laid the foundation for the development of spaceborne GNSS-R technology [6]. In response to the increasing demand for Earth observation, various countries launched different spaceborne GNSS-R satellite missions in the following years, including the TDS-1 [31], CYGNSS [32], BF-1 A/B [14], FSSCat [12], FengYun-3E/3F/3G [33,34], and Tianmu-1 satellite missions. The successful launch of spaceborne GNSS-R satellite missions has brought new opportunities and promoted the continuous development of spaceborne GNSS-R technology. The development history and specific information of GNSS-R satellite missions are shown in Figure 1 and Table 1.  
The satellites currently known to collect raw IF signals among spaceborne GNSS-R satellites mainly include the following, and detailed information about these satellites is provided in reference [21]. Here, we provide a brief introduction to the relevant applications of these satellite raw IF signals. UK-DMC: This satellite was launched in a sun-synchronous orbit at an altitude of approximately 680 km and has collected multiple sets of raw IF signals [35], from which GPS signals have been discovered [6]. UK-TDS-1: This satellite was launched in low Earth orbit at an altitude of approximately 635 km and an inclination angle of 98.4\({}^{\circ}\). It occasionally collects raw IF samples of direct and reflected signals, which provides opportunities for research on new applications such as sea surface target and sea ice detection [36], sea surface altimetry [37], and ionospheric detection [38], among others. CYGNSS: This consists of eight microsatellites launched in low Earth orbit at an altitude of 520 km and an orbital inclination of 35 degrees. Its main task is to measure the sea surface wind speed of tropical cyclones and their surrounding waters [39]. Some studies have processed CYGNSS raw IF signals to generate new CYGNSS data products to improve data resolution and optimize the application of data products in scientific research [40; 41]. Currently, CYGNSS raw IF signals are widely used, including in inland water detection [42], inland water height retrieval [24], sea surface height measurement [43], ionospheric detection [44], and the potential of interferometry in height measurement [45].
## 2 Background
It does not require TCP/IP stack, and can be alternatively used over UDP. Apart from the aforementioned CoAP and MQTT, additional application-layer protocols strengthen support for IoT devices [35]. The OMA LWM2M protocol [26], for example, introduces device management functionality and transfer of service data from the network to devices over sensor or cellular networks. It is based on CoAP and DTLS, but other connection bindings are supported, too. Despite various adaptations of communication networks and protocol stacks for IoT, cloud-centric IoT systems apply more common communication, transport, and application technologies to make the cloud resources available to others. These cloud systems range from, e.g., IoT collection and analysis platforms [36], public open-data resources [37], geolocation and maps [38], open weather information [39], and similar sources. The technologies for cloud APIs are based on TCP/IP stack with HTTP or WebSocket at the application layer. If structured data formats are applied for content upload/download, these are commonly JSON or XML. We have three options for matching IoT device-specific communications and message formats with those of the cloud backend:  
1. Support IoT specific protocols in cloud APIs: not likely, if the cloud service is externally provided and the IoT solution developer therefore cannot affect the cloud API implementation and functionality. 2. Develop a dedicated proxy function between the available API implementation and the one appropriate for our IoT devices. 3. Apply a modular application gateway, as the LDAF presented in this paper, where common API and device connectivity functions are readily available and where the developers can concentrate on application-specific optimization of traffic volumes.
## 1 Introduction  
### Background  
The control of anthropogenic carbon dioxide releases has drawn considerable interest from academia and manufacturers in recent years due to the growing apprehension about global climate change. Nowadays, the amount of carbon dioxide emissions has risen to 400 ppm in the atmosphere, which is considered a 30% rise in contrast to 1958 [1, 2]. In 2015, the Intergovernmental Panel on Climate Change (IPCC) suggested a new proposal for emissions mitigation which was approved by several countries at the 21 st Conference in Paris [3]. Carbon capture and utilization and storage (CCUS) has already been researched as a successful choice to reduce greenhouse emissions, where the major uses of carbon dioxide removal can be at large-scale CO\({}_{2}\)-emission sources, typically coal. However, 30% of overall carbon dioxide emissions are produced from energy production by coal [4, 5].  
Among different carbon-capture processes, post-combustion CO\({}_{2}\) recovery is believed to be the most effective and flexible technology to be integrated with coal power plants without creating crucial retrofitting [6, 7]. Many separation technologies can be integrated to mitigate CO\({}_{2}\) contents from flue gas streams, such as chemical absorption, physical absorption, physical adsorption, and membranes [8, 9, 10, 11, 12]. Chemical absorption technology is considered to be the most favorable process because of its ability to capture 90% of CO\({}_{2}\) from a high flue gas rate with large concentrations of no less than 99%. High thermal energy requirements and climate influence probabilities in terms of solvent discharge and degradation are the major drawbacks of the technology [13, 14].  
The membrane gas separation process, which is investigated in the present article, is believed to be an applicable and optimistic method for carbon dioxide reduction that can compete with the conventional CO\({}_{2}\) separation processes regarding energy penalties and price [15; 16; 17]. Membrane technology is appealing for CO\({}_{2}\) capture due to its features such as its simple structure, clean energy, and environmentally friendly nature [18].  
The development of highly CO\({}_{2}\)-selective, extremely permeable membranes has been the main goal of research into the prospect of CO\({}_{2}\) removal by membranes. Effective CO\({}_{2}\)-selective membranes have been recorded, some of which are manufactured on a large scale [19; 20; 21].
## 3 Proposed Scheduler to integrate the MES and AIV
### LabVIEW-AIV Communication
#### 3.2.1 ROS Package  
As mentioned in Section 2, ROS exchanges data using Publisher and Subscriber nodes. Here, the ROS package is developed using C++. In the ROS package, a Subscriber is generated which,  
Figure 6: Front panel of ‘_WebserviceA_’ webservice (after edits) (sfcList data is not displayed because of its specific company product details).  
subscribes to a topic (topic published by LabVIEW Publisher) to receive commands for the AIV from LabVIEW. Similarly, a Publisher is generated which publishes a topic (topic subscribed by LabVIEW Subscriber) to send an acknowledgment to LabVIEW. To take coordinates of the physical location, a map is generated using SLAM (Simultaneous Localization And Mapping) [37] technique. To generate the map, the AIV is taken round the workplace and allowed to scan the surrounding area with its main LiDAR sensor. Here, hard codded coordinates of physical locations are used. The coordinates are taken using amcl (adaptive Monte Carlo Localization) node [37], which takes laser scans, transform messages, and outputs estimated position. The pseudo-code for the ROS Publisher/Subscriber used here is given in Figure 7. This code is compiled and executed using the respective ROS commands [37].  
The _'Movetagal'_ function of Figure 7 defines a client, which is responsible for sending navigation goal requests to the move_base node. The move_base node sends the goal to the global path planner, which finds a global path from the robot location to the goal location. The global path planner calculates the safe path to reach the goal location and also sends this trajectory to the local planner. The local planner then executes each segment of the global plan. So, given a plan to follow and a map, the local planner provides velocity commands to move the AIV. The local planner also monitors the odometry and laser data and selects a collision-free local plan for the AIV. The local planner recomputes the path in real-time to keep the AIV safe from striking objects, yet still allowing the AIV to reach the destination. Details about the global and local path planner and obstacle avoidance are given in Reference [37]. According to Figure 7, if strings _'Go_to_Location_PA'_ or _'Go_to_Location_PB'_ are received from LabVIEW, then AIV will go to the physical location PA or PB, respectively. The coordinates of PA and PB locations are defined as _A.x_, _A.y_, _A.z_, _A.w_ and _B.x_, _B.y_, _B.z_, _B.w_, respectively. In Figure 7, only two locations (PA  
Figure 7: Pseudo-code for Robot Operating System (ROS) Publisher/Subscriber node.  
and PB) are shown as an example, while in this work total six locations are defined (three pickups and three drop-offs). Note that, here and in Section 3.3, PA, PB, and PC are used for pickup locations and represent PICK1, PICK2, and PICK3 (mentioned in Section 3.1) respectively. Similarly, DA, DB, and DC are used for drop-off locations and represent DROP1, DROP2, and DROP3, respectively.