esa-sceva/satcom-chunk-collection huggingface dataset:
# Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch
## 2 System Architecture
### System Architecture and Packet Processing Logic  
This proposed system architecture is mainly based on an extended OpenFlow switch. Figure 1 shows our proposed architecture. The blue parts are the existing main components in an OpenFlow switch, while the orange parts are developed by this paper.  
The blue components include kernel datapath, user space flow table pipeline and OpenFlow message processing. Both the kernel's datapath and user space contain flow tables. Packets can match a rule and execute the rule's action. To speed up packet processing, most packets finish processing and forwarding in the kernel directly. OpenFlow message processing is in charge of receiving and sending OpenFlow messages with the controller. The extended parts include connection-state processing and the self-learning service. Connection-state can perform the connection state tracking, synchronization of connection states and stateful matching. Self-Learning can perform some packet-processing functions in the data plane by the action of OpenFlow and limit the need to send packets to the controller.  
As shown in Figure 1, steps 1-7 show how the packets can be processed in this new architecture. The connection-state processing service can track the real-time connection state between a controller and a switch. Once the connection state changes, the connection state will be synchronously transmitted to the kernel and user space on time (see step 1 in Figure 1). When a packet of sensor data arrives at the switch's ingress port, the kernel datapath will call the appropriate function to parse the packet header. Those fields parsed as well as the connection state together form a lookup key (see step 2 in Figure 1). Then the key is matched against the flow rules presented in the datapath flow tables (see step 3 in Figure 1). If there is no match, Netlink is used to send the packet as an Upcall message to  
Figure 1: System architecture.  
the user space for lookup in flow table pipeline (see step 4 in Figure 1). If matching a rule in the user space, the packet will be processed according to the rule's action, such as self-learning and the packet sent to the controller, _etc._ When the action is self-learning, it will parse the packet and insert/update a corresponding rule in the flow table (see step 5 in Figure 1). When the action is sending the packet to the controller, the OpenFlow message processing service will encapsulate the packet into a Packet-In message and send it to a controller (see step 6 in Figure 1). If connection to the switch goes well, the controller will receive and process the Packet-In message. Then the controller sends a Flow-Mod message to the switch and installs a new rule in the user space flow table (see step 7 in Figure 1). Finally, the actions of the new rule are executed on the packet and a new flow rule is inserted into the datapath so that the subsequent packets of the flow can be processed fast in the datapath.
## 4 Discussion
### Intellectual Property  
Many people struggle to strike a balance between openness and IP, despite the fact that transparency is a necessary component for effective IP. IP mechanisms bring a period of exclusive rights to the authors in exchange for details about the invention or product, allowing others to build upon such innovation. Nevertheless, patent examiners continue to face challenges in carrying out their duties since most of the data that may assist them is not freely available, owing to the fact that the organizations maintaining such material have not recognized that making it public would be useful [81]. Transparency can help steer the development of a better IP system. We just saw a solid example of this necessity in the form of IP protections on COVID-19 vaccinations. COVID-19 vaccinations were created in an unprecedented amount of time. However, the virus continues to mutate, and new strains emerge, hastening contagions and increasing the death toll. As a result, it is critical to remove the legal impediments to increasing global vaccine manufacturing. The World Health Organization established the COVID-19 Technology Action Pool [82] to foster vaccine technology and know-how sharing, but none of the companies producing vaccines have signed up to date. We propose the following recommendations for improving intellectual property:  
* _Invest in the correct guidance early on._ Identifying sensitive data and evaluating the critical importance of protecting it from the public domain should be the first priority. Our survey participants agree greatly with the need for disclosing and describing information related to training and validation in full. This should evidently be accompanied after evaluating what information must be safeguarded and what information may be made available to the public--e.g., by performing a risk and cost-benefit analysis without jeopardizing openness.  
* _Educate employees about intellectual property_. Employees should be made aware of the value, boundaries, and risk of IP (or lack thereof) [83; 84; 85]. Training should make clear to them what needs to be protected, how to protect it, and from whom it should be protected [83; 84; 85].
## 2 Materials and Methods
### Physical and Mechanical Properties  
The density was calculated in accordance with EN 1936 [34]. The compressive strength (CS) of the test specimens was determined based on the EN 196-1 [35] using a compression test machine (Tinus-Olsen). These tests were carried out on 4 \(\times\) 4 \(\times\) 4 cm prismatic specimens. Each result was obtained by testing three specimens.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **CP** & **SC** & **E** & **OR** \\ \hline SiO\({}_{2}\) (\%) & 13.8 & 0.01 & 0.6 & 26.8 \\ \hline Al\({}_{2}\)O\({}_{3}\) (\%) & 3.5 & 0.04 & 0.07 & 6.8 \\ \hline Fe\({}_{2}\)O\({}_{3}\)(\%) & 2.3 & - & 0.6 & 5.2 \\ \hline MnO (\%) & 0.1 & 0.04 & - & 5.6 \\ \hline MgO (\%) & 0.7 & - & 0.4 & 5.1 \\ \hline CaO (\%) & 59.3 & 54.0 & 62.4 & 16.4 \\ \hline Na\({}_{2}\)O (\%) & 0.1 & 0.5 & - & 1.5 \\ \hline K\({}_{2}\)O (\%) & 0.5 & - & 0.2 & 22.5 \\ \hline TiO\({}_{2}\) (\%) & 0.2 & - & - & - \\ \hline P\({}_{2}\)O\({}_{5}\) (\%) & 0.1 & 0.1 & - & - \\ \hline SO\({}_{3}\) (\%) & 1.7 & 0.3 & 1.3 & - \\ \hline LOI (\%) & 15.5 & 43.2 & 34.4 & 9.4 \\ \hline Specific Density (g/cm\({}^{3}\)) & 3.19 & 2.63 & 2.64 & 2.73 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main chemical components (% weight) of Portland cement, scallop shells, eggshells, and olive oil pomace ash. Note: CP: Portland cement; SC: scallop shells; E: eggshell; OR: fly ashes; LOI: loss on ignition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Composition** & **Cement (\%)** & **Waste (\%)** & **Water/Solid Ratio** \\ \hline CP-100 & 100 & - \\ \hline CP-92.5-SC-7.5 & 92.5 & 7.5 \\ \hline CP-20-SC-80 & 20 & 80 \\ \hline CP-92.5-E-7.5 & 92.5 & 7.5 \\ \hline CP-20-E-80 & 20 & 80 \\ \hline CP-92.5-OR-7.5 & 92.5 & 7.5 \\ \hline CP-20-OR-80 & 20 & 80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tested compositions. Note CP: Portland cement; SC: scallop shells; E: eggshell; OR: biomass fly ash.
## 2 Problem Description
### Error Budget Analysis
Consequently, primary strategies to eliminate thermal noise encompass enhancing the effective bandwidth, increasing the integration time, and optimizing the carrier-to-noise ratio. Notably, the effective bandwidth is limited by the CCSDS standard; specifically, the S-band and X-band effective bandwidths for VLBI are approximately 7.5 MHz and 40 MHz, respectively. The carrier-to-noise ratio is related to the downlink signal power and the receiving performance of the ground station. Since the receiving performance of the ground station is usually stable and has little room to improve, the carrier-to-noise ratio is directly affected by the downlink signal power. \[\sigma_{\tau}=\frac{\sqrt{2}}{2\pi\Delta f\sqrt{T(P/N_{0})}} \tag{1}\]  
The integration time is directly limited by the accuracy of the spacecraft's predicted ephemeris. The data processing of interferometry is essentially the correlation of two station data which have been compensated for by using a prior time delay model and offers the arrival time difference of the same wavefront. The prior time delay model is calculated by using the spacecraft's predicted ephemeris and the station's coordinates. Inaccurately predicted ephemeris can lead to an imprecise prior delay model, resulting in a significant residual delay rate during correlation processing. Suppose the residual delay rate is \(f_{\tau}\) and the integration time is \(T\), only when\(f_{\tau}\times T\) is far less than 1, can the correlation peak of the two station data be close to the maximum value. However, if the predicted ephemeris is  
Figure 2: \(\Delta\)DOR error budget for X-band in CDSN interferometry system. inaccurate, leading to a large \(f_{r}\), then an increase in the integration time results in a decrease in both the correlation peak and measurement accuracy. In other words, the increase in the integration time is constrained by the accuracy of the predicted ephemeris. Therefore, while holding other parameters fixed, improving the utilization efficiency of the downlink signal power, which equivalently increases the downlink signal power-to-noise, will be beneficial to improve the measurement accuracy, especially in the far deep space explorations.
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 4 Encoder Design Details
### Design for Flexible Configuration
We designed a parameterized encoder module; static  
Figure 10: Parallelism improvement of Parities \(P_{2}\) calculation. parameters are available during power on process, and dynamic parameters are changed by L2/L3 setting via MAC-PHY (Port Physical Layer) API (Application Programming Interface). Then, the encoder schedules the hardware to perform encoding operations. The encoder can thus handle variable code length and rate for LDPC encoding through a fixed hardware architecture, which makes the encoder compatible with all configurations following 5G standard.
## 5 Open Problem and Research Challenges
### Open Challenges  
In recent years, UWSN has gotten much interest to obtain information about aquatic resources. The data packets are sent from the underwater sensor node to the surface node through the underwater route. Many difficulties have been addressed in order to improve sensor network performance. However, the researchers must still overcome specific difficulties in the unique and complicated underwater environment.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 2 Materials and Methods
### Synthesis
#### 2.1.2 3-(2-Oxobenzo[d]oxazol-3(2\(H\))-yl)-N-(4-sulfamoyliphenyl)propanamide (**4**)  
To a mixture of acid **1** (1.04 g, 5 mmol), 4-aminobenzenesulfonamide (1.03 g, 6 mmol), DMSO (15 mL), triethyl amine (1.52 g, 15 mmol, dropwise), and HBTU (2.84 g, 7.5 mmol) were added, and the mixture was stirred for 20 h at room temperature. After the completion of the reaction (TLC), the mixture was diluted with water (20 mL), and the formed precipitate was filtered off, washed with water, and recrystallized from propan-2-ol.  
Yield 1.41 g (78%), mp 252-254 \({}^{\circ}\)C;  
\({}^{1}\)H-NMR (400 MHz): \(\delta\) 2.85 (t, 2H, \(J\) = 6.6 Hz, CH\({}_{2}\)CO), 4.13 (t, 2H, \(J\) = 6.6 Hz, CH\({}_{2}\)N), 7.11 (t, 1H, \(J\) = 7.8 Hz, H\({}_{\rm{Ar}}\)), 7.21 (t, 1H, \(J\) = 7.8 Hz, H\({}_{\rm{Ar}}\)), 7.24 (s, 2H, NH\({}_{2}\)), 7.28-7.36 (m, 2H, H\({}_{\rm{Ar}}\)) 7.67 (d, 2H, \(J\) = 8.5 Hz, H\({}_{\rm{Ar}}\)), 7.73 (d, 2H, \(J\) = 8.5 Hz, H\({}_{\rm{Ar}}\)), 10.39 (s, 1H, NH);  
\({}^{13}\)C-NMR (101 MHz): \(\delta\) 34.45, 38.25 (CH\({}_{2}\)CO, CH\({}_{2}\)N), 109.51, 109.60, 112.43, 118.69, 122.16, 123.82, 126.69, 127.44, 130.97, 138.38, 141.77, 141.96, 153.63, 169.20 (Car, CO);  
IR (KBr): \(\
u_{\rm{max}}\) = 3374, 3304, 3260 (NH\({}_{2}\), NH), 1754 (C=O) cm\({}^{-1}\).  
Calcd for C\({}_{16}\)H\({}_{15}\)N\({}_{3}\)O\({}_{5}\)S (361.37), %: C 53.18; H 4.18; N 11.63. Found, %: C 53.00; H 4.01; N 11.41.
# A Study on the Creep Characteristics of Gassy Clay Mixed with Silt
## 1 Introduction
They found that the trapped gas in the soil hindered drainage, increased the undrained shear strength of the soil and reduced the consolidation coefficient. [PERSON] et al. [15] conducted undrained cyclic triaxial tests on normally consolidated fine-grained gas-bearing soil and found that during the cyclic shear process, the collapse of gas-filled pores led to an increase in cyclic pore water pressure and a decrease in stiffness, while water partially discharged into the gas cavity could prevent this process. [PERSON] et al. [16] compared the drained stiffness and undrained stiffness of gas-containing and non-gas-containing fine-grained sediments and found that stress history had almost no effect on the undrained stiffness of gas-containing fine-grained sediments, while the drained stiffness of non-gas-containing fine-grained sediments was related to stress history. [PERSON] et al. [17] conducted triaxial tests on natural gas-containing sediments in the Gulf of Guinea. The results showed that the expansion and dissolution of gas led to an increase in the compression rate of the soil, a decrease in the preconsolidation pressure and the undrained shear peak strength, and an increase in the pore pressure. [PERSON] et al. [18] analyzed the effect of water content on the strength and deformation characteristics of thin silt sand interlayers in silty gas-containing clay in a tidal flat area in Cangnan through triaxial tests and consolidation tests. The results showed that the soil cohesion was negatively correlated with the water content, while the internal friction angle was almost unaffected by the water content. [PERSON] et al. [19] compared the triaxial compression properties of gas-saturated soil and water-saturated soil and found that the stiffness and strength of gas-saturated samples were higher than those of water-saturated samples, and the strain softening behavior of gas-saturated samples was more obvious and the volumetric strain was larger. [PERSON] et al. [20] used a mixed pressure coring system to recover cores from shallow natural gas hydrate deposits on the deep seabed and conducted uniaxial and triaxial compression tests to obtain strength and stiffness data for shallow natural gas hydrate deposits. The abovementioned research on gassy soils is relatively advanced but mostly consists of simple experimental studies such as triaxial tests or cross-plate shear tests. In particular, for gassy soft clay, due to its soft characteristics after sample preparation, existing studies rarely involve it. This paper, based on the actual \"Cross-River Tunnel\" project and considering practical unconsolidated and undrained engineering conditions, conducted triaxial shear tests and creep tests on gassy soft clay. It analyzed the shear deformation characteristics and long-term strength properties of gassy clay containing silt. A long-term strength prediction model for silty clay considering confining pressure and gas content is proposed, and the effectiveness of the prediction model is verified through experiments.
## 2 Materials and Methods
### Geometric Model  
This study investigated a simplified building with 15 floors, and the height of each floor was 2.7 m. Figure 1 shows schematics of the building, which included an elevator shaft, a lobby, and a room. No furnishing in both the lobby and the room was considered. We assumed the windows of the room were kept open. In the elevator shaft, only the elevator car was accounted for. There were clearances between the elevator and the lobby and between the lobby and the room. For simplification, all of the clearances were approximated into square openings located at the center of the interface but bearing the same area as the realistic situations. Such simplification has been widely adopted in previous studies [29; 30; 35; 36]. The opening between the shaft and the lobby had an area of 0.04 m\({}^{2}\), while the opening between the lobby and the room had an area of 0.0225 m\({}^{2}\). Each room was connected to the outside via a window with an area of 0.45 m\({}^{2}\). Moreover, there was a vent with 1 m\({}^{2}\) at the top of the shaft. The dimensions of the elevator car (in orange) were 1.6 m long, 2.1 m wide, and 2.3 m high. Figure 1c presents the planar view of the building to further illustrate the relative positions of different parts. Table 1 summarizes the geometric parameters of each component in the building.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & **Length (m)—X** & **Width (m)—Y** & **Height (m)—Z** \\ \hline Elevator shaft & 2.4 & 2.65 & 40.5 \\ Lobby & 3 & 4 & 2.7 \\ Room & 5 & 4 & 2.7 \\ Elevator car & 1.6 & 2.1 & 2.3 \\ Vent hole & 1 & 1 & - \\ Window & - & 0.6 & 0.75 \\ Opening between & - & 0.2 & 0.2 \\ shaft and lobby & - & 0.15 & 0.15 \\ Opening between & - & 0.15 & 0.15 \\ lobby and room & - & 0.15 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric parameters of the investigated building.  
Figure 1: Geometric model of a building chosen for study: (**a**) outer appearance; (**b**) perspective view; (**c**) planar view.
## 4 Finite Element Analysis
### Weld Modeling and Steel Tube-Concrete Infill Interface Model  
Weld modeling is seldom taken into consideration in finite element analysis. However, it was found that if the effect of welding is not considered, the numerical simulations of the load carrying capacity and joint rigidity are smaller than those of experimental results [67]. In addition, [PERSON] et al. [68; 69] pointed out that in the nonlinear finite element analysis of welded joints, the mechanical properties of weld and steel parent metal should be considered differently. In this study, Shell181 was used for weld modeling by adding a circle of Shell181 around the intersection of brace and chord members, and the previous test results of weld metal under monotonic loading were used for the material properties of welds (see reference [35] for details). It should be noted that the contact element was used for simulating the case of hidden weld unwelded. The weld modeling is shown in Figure 15.  
The contact interaction between the steel tube and concrete infill was simulated by setting contact pairs with the contact elements. The steel tube was defined to be the target surface and the outer surface of concrete infill was defined to be the contact surface. In addition, the friction coefficient (\(\mu\)) should be defined to characterize the friction behaviors of the contact interaction between the steel tube and concrete infill. The friction coefficient (\(\mu\)) was suggested to be from 0.2 to 0.6, and 0.4 was selected in this study.  
Figure 14: Finite element mesh of a Warren CHS tubular truss.
## 4 Forecasting Methods  
Forecasting-based methods use historical data of time series to train a predictive model that can forecast future data points. This method assesses the anomaly level by comparing predicted values with actual values, considering anomalies as a significant increase in prediction error, as illustrated in Figure 5a. This section will explore predictive anomaly detection methods, focusing primarily on four types: CNN-based, RNN-based, GNN-based, and Transformer-based models.  
Figure 5: The examples of each type of anomaly criteria: (**a**) a forecasting loss; (**b**) a reconstruction loss; and (**c**) a contrastive loss.
# Enhancing Efficiency of Electromagnetic Simulation in Time Domain with Transformation Optics
## 6 Summary  
In summary, we have shown the capability of the FDTD simulation with TO for non-uniform grids to enhance efficiency. The non-uniformity is accomplished by the derived spatially-varying numerical anisotropic medium from the designated mapping function. In the demonstration, we used fine grids around the aperture in a large film of a PEC-like material, and coarse grids at other areas to reduce the computational cost. To obtain converged transmittance spectrum, simulations in uniform grids are performed; the resultant spectrums are used to approximate that when the cell size approaches to zero. In comparison, the approximation is close to that reproduced from the theoretical work in [33]. For the 1D coordinate transformation cases with the multiple-linear and linear-nonlinear functions, the normalized transmittance spectrums agree with that from the uniform-grid simulation when the central cell sizes are the same, but both the cases require only 22.83% and 66.34% of the computational cost, respectively, showing the accuracy and efficiency of the method and implying its flexibility for complex configurations. The efficiency is further improved by performing the 2D coordinate transformations; in this case, the cost is remarkably reduced to 5.31% while the accuracy remains. A finer central cell size of 0.625 nm is utilized for the more accurate spectrum while its cost is still less than that of the uniform case. The increasing rate of the computational cost due to cell resolution is also analyzed; the analysis shows that the increasing rate of the cost is two orders slower than that in the uniform case. According to the results, we are confident that the method should be helpful to the FDTD simulation study in the sub-wavelength optics with accuracy and efficiency.  
Conceptualization, J.-S.H., R.-C.S., Y.-C.L. and K.-R.C.; Data curation, W.-M.C. and M.-C.Y.; Formal analysis, J.-S.H., M.-C.Y. and K.-R.C.; Funding acquisition, K.-R.C.; Investigation, [PERSON]-S.H., W.-M.C., [PERSON]-C.Y., [PERSON]-C.L. and K.-R.C.; Methodology, [PERSON]-S.H., W.-M.C., [PERSON]-C.Y., [PERSON]-C.S., [PERSON]-C.L. and K.-R.C.; Project administration, [PERSON]-S.H. and K.-R.C.; Resources, K.-R.C.; Software, [PERSON]-S.H., W.-M.C., [PERSON]-C.Y., [PERSON]-C.S. and [PERSON]-C.L.; Supervision, [PERSON]-C.L. and K.-R.C.; Validation, [PERSON]-S.H., [PERSON]-C.L. and K.-R.C.; Visualization, J.-S.H.; Writing-original draft, J.-S.H.; Writing-review and editing, J.-S.H. and K.-R.C.  
This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 104-2112-M-006-004-MY3.  
The authors are grateful for computational resources provided by National Center for High-performance Computing (NCHC) of the National Applied Research Laboratories (NARLabs) of Taiwan.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.