esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Verification of the Long Distance Movement Experiment  
Figure 17 shows the operating state of the experiment of the subject--wheelchair trajectory, time history of gaze direction \(\varphi_{g}\), direction of movement \(\varphi_{out}\), and speed \(\varphi_{out}\)--when the subject performed a long distance movement. Figure 18 demonstrates gaze detection results, state of the experiment, result obtained from integration of MF based on environmental recognition and fuzzy set theory when the subject performed a long distance movement. Video S3-1 shows the state of the experiment, S3-2 shows the environmental recognition, S3-3 shows the MF when the subject performed a right turn on the front and back passage.  
We confirmed that the gaze and passage were able to be detected at each time in the real environment. Firstly, in the sections (1) to (4) in Figures 17a and 18a,b, the subject is gazing at left turn direction, and as a result the direction of movement changes to the left direction in Figure 17b, and he turned left. Secondly, in the sections (6) to (9) in Figures 16a and 17a,b, the subject is gazing at right turn direction, and as a result the direction of movement changes to the right direction in Figure 17b, and he turned right. Finally, in the sections (10) and (11) in Figures 17a and 18a,b, the subject is gazing at straight direction, and as a result the direction of movement changes to the straight direction in Figure 17b, and he went straight.  
This result confirms the ability of a passenger to operate the wheelchair by just gazing towards the direction in which he wants to move for a long distance.  
Figure 16: Details of Figure 14: (**a**) Detail of (d-3); (**b**) Detail of (e-3); (**c**) Detail of (d-4); (**d**) Detail of (e-4).  
Figure 17: Results obtained when the subject executed a long distance movement: (**a**) wheelchair trajectory; (**b**) gaze direction \(\varphi_{g}\); (**c**) direction of movement \(\varphi_{out}\); (**d**) Speed \(\overline{v}_{out}\).  
Figure 18: _Cont._
# Mitigation of Gaseous Emissions from Swine Manure with the Surficial Application of Biochars
## 1 Introduction
Biochars are known to affect GHGs emissions, particularly evidenced in the research related to soil. [PERSON] et al. [24] reported that biochar application on the soil during the rice-growing season could reduce up to 91% of CH\({}_{4}\) emissions. Moreover, [PERSON] et al. [25] reported N\({}_{2}\)O emission reduction after biochar application from 42-92% in a short-term incubation study. According to [PERSON] et al. [25] and [PERSON] et al. [26], biochar application on soil may increase CO\({}_{2}\) emission from 8-91%. The summary of the mitigation effects of gaseous emissions from the soil is presented in Table S2 [25; 26; 27]. The biochar properties affect the complex physical, chemical and biological mechanisms and could be explored for gaseous emissions mitigation. Biochars have a high sorption capacity that couldbe useful for gas filtration. Results from an experiment conducted by [PERSON] et al., (2016) [PERSON] et al. (2016) show that biochar can adsorb 77% of phenol and 26% of NH\({}_{3}\)[PERSON] et al. (2016). A mini-review of research on the uses of biochar as an adsorbent for different gases in laboratory-scale experiments is summarized in Table S3 [PERSON] et al. (2016); [PERSON] et al. (2016); [PERSON] et al. (2016); [PERSON] et al. (2016). Highly alkaline and porous biochar (HAP) is a result of the addition of 95% of N\({}_{2}\) and 5% of O\({}_{2}\) during a pyrolysis process [PERSON] et al. (2016). HAP has a high pH, which potentially can be effective in mitigating emissions of H\({}_{2}\)S and volatile fatty acids (VFAs). Most of the bacteria producing malodor and H\({}_{2}\)S have pH in the range of 6.5-7.5 [PERSON] et al. (2016). HAP has high porosity and a large surface compared with conventional process biochar (such as the red oak, RO) of lower pH. Most recently, we reported on the spatial and temporal effects on the pH in the liquid-air interface after the surficial application of HAP and RO biochars to water [PERSON] et al. (2016) and swine manure [PERSON] et al. (2016). This research reports on the next logical step, i.e., evaluation of the mitigation effect of biochars on gaseous emissions from swine manure. The objective of this research was the initial evaluation of HAP biochar and common RO biochar treatment of gaseous emissions from swine manure storages. Specifically, we evaluated:  
* Mitigation of odor
* Reduction of H\({}_{2}\)S, NH\({}_{3}\), GHG, and VOCs emissions
* How long biochar treatment lasts after application to manure surface.
# Albatross-Like Utilization of Wind Gradient for Unpowered Flight of Fixed-Wing Aircraft
## 4 Rayleigh Cycle Method  
In the real world, the wind gradient is generally not sufficiently large to enable sustainable climbing by an aircraft. The Rayleigh cycle affords another flight strategy for utilizing a gradient wind field for unpowered flight. This strategy is based on the observation that albatrosses are capable of flying for long periods without flapping their wings. [PERSON] [10] observed that the wind layer where the albatrosses fly increases according to the altitude. A Rayleigh cycle can be divided into four phases, as shown in Figure 4.  
Let us suppose that the wind over the ocean surface in Figure 4 is divided by the wind shear into two layers, with the wind speed above the wave being 7 m/s and that below it being 0 m/s. The first phase of a Rayleigh cycle is the upwind climb segment. At the beginning of this phase, both the airspeed and the ground speed of the albatross are 17 m/s upwind. After the albatross crosses the wind shear, the ground speed is maintained at 24 m/s, but the airspeed is changed to 24 m/s.  
The second phase is the high-altitude turn segment. In this phase, the albatross executes an approximately uniform circular motion in the air, with its heading changing from upwind to downwind. Thus, the airspeed is still 17 m/s, but the ground speed changes to 31 m/s.  
The third phase is the downwind dive segment. Similar to the upwind climb phase, in this phase, the albatross crosses the wind shear in the wind direction. The ground speed is maintained at 31 m/s, and the airspeed also changes to 31 m/s.  
The fourth phase is the low altitude turn segment, which completes the Raleigh cycle, linking back to the first phase. Similar to the high-altitude turn phase, in this fourth phase, the albatross executes an approximately uniform circular motion in the air, and then returns to the beginning states. The final airspeed and ground speed are both 31 m/s, indicating that the albatross almost gains double the upper layer wind speed during a Raleigh cycle. In other words, it harvests energy from the wind shear.  
Figure 4 is only an intuitive illustration of the Rayleigh cycle. In practice, the wind shear does not produce a sudden change in the wind speed, because it has a continuous wind gradient profile. The altitude fluctuation during a Rayleigh cycle is also considerable. Thus, there is always a transformation between gravitational potential energy and kinetic energy over the entire cycle, with the drag also  
consuming energy. Therefore, we prove below that the upwind climb and downwind dive can be used to harvest extra energy.
## 2 Materials and Methods  
### Similarity Ratio Design  
This indoor model test used the engineering prototype from the Shengli Road excavation in Nanchang City, Jiangxi Province, China, as the background. The depth of the excavation reaches 11.7 m, which is a deep excavation. There are adjacent multistory buildings with a safety level of A, pipelines, etc. We selected the typical cross-section ZK63 of the prototype excavation as the object of the model test for analysis. According to the survey data, the soil layer information is shown in Table 1.  
The excavation retaining system was designed as a \"steel sheet pile + strut\", with the sheet pile being a hot-rolled U-sheet pile of type SKSP-IV, the cross-section size of which is shown in Figure 2. The pile length was designed to be 18.56 m, and the sheet pile was Q295 steel. The underground main structure was built by the inverse method, with the top (0.45 m), middle (0.35 m), and bottom (0.55 m) plates serving as the retaining structure's horizontal internal struts.  
The material characteristics and geometric dimensions of the selected model and prototype in the model test needed to conform to a certain similarity relationship. Considering the feasibility of the model test box and the layout of test components, the height of the model sheet pile was determined to be 1.8 m. Based on the similarity criterion [18], the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Soil Layers**} & **Layer** & **Volumetric** & **Cohesive** & **Internal** & **Scale Factor** \\  & & **Thickness** & **Weight** & **Forces** & **Friction Angle** & **\(m\) (MN/m\({}^{4}\))** \\  & & **\(h\) (m)** & **\(\gamma\) (kN/m\({}^{3}\))** & **\(c\) (kPa)** & **\(\varphi\) (\({}^{\circ}\))** & **\(m\) (MN/m\({}^{4}\))** \\ \hline
1 & Miscellaneous fill & 3.1 & 16.5 & 8.0 & 10.0 & 1.8 \\
2 & Silty clay & 1.8 & 18.8 & 22.7 & 10.1 & 3.3 \\
3 & Medium sand & 0.2 & 20.3 & 0.0 & 32.0 & 17.3 \\
4 & Pebble & 9.4 & 20.8 & 0.0 & 42.0 & 31.1 \\
5 & Strong-weathered rock & 0.8 & 20.5 & 25.0 & 30.0 & 17.5 \\
6 & Medium-weathered rock & 5.0 & 24.0 & 45.0 & 50.0 & 49.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil layer parameters of ZK63 cross-section.  
Figure 2: Cross-section size of SKSP-IV U-steel sheet pile (mm).  
basic physical quantities (geometric dimensions, material mechanical properties, etc.) of the model test were designed. The similarity relations and constants between the prototype and the model are listed in Table 2 by the three theorems of similarity. Furthermore, based on the similarity in volumetric weight, it could be determined that the elastic modulus of the model material was 1.5-2.5 GPa. Finally, the retaining structure primarily underwent bending deformation during excavation, and it should have theoretically met the similarity relationship of bending stiffness.
## 7 Discussion and Further Work
### Securing the Architecture  
Since EDAS aims to evaluate the security of a critical infrastructure by capturing and communicating security information (events), its adaptation loop also needs to be protected. The protection becomes more serious when its components are deployed in distributed settings. It must be ensured that the events, carrying security information, are communicated via well-protected channels and protocols and remain genuine and protected during the communication. Furthermore, access to the platform needs to be protected and well managed, and mechanisms should be provided to ensure its availability. To meet these requirements, we suggest the following:  
1. The EDAS platform should also monitor itself. This implies that there is a security monitoring and adaptation loop within the platform itself. Thus, EDAS should be considered as a critical event source in the architecture and needs to be monitored as other monitored objects in the scope.
2. Security tools, such as firewalls, intrusion detection, access controls and availability monitoring applications, should be installed on the platform to provide the required security services to it. The events generated by these applications can also be integrated into the architecture to make the analysis process more accurate.
3. Event communication protocols should provide security mechanisms for confidentiality and integrity. Furthermore, it should be energy efficient to comply with the resources of low-end devices, such as sensors.  
EDAS is primarily dependent on an event framework that can generate, handle and log meaningful events. In EDAS, we assume that event framework has already been programmed into the thing. This facility is typically available as an out-of-the-box solution for various purposes, including debugging, error tracking, security logging and other troubleshooting operations. In IoT-eHealth, it is a must have component for patient safety to ensure that the body sensors are working reliably.
## 4 Simulation and Analysis of the DLLFP
### Wavenumber-Thickness Product Versus Angular Frequency Curve  
Figure 7 shows the curves of relationship between the wavenumber-thickness product and angular frequency for DLLFP in different physical fields. With the help of parametric scanning, the excitation frequency \(f_{0}\) is scanned from 10 Hz to \(f_{\mathrm{max}}\) = 25 kHz with a scan  
Figure 6: Sound–structural coupling frequency dispersion diagram.  
step set to 249.9 Hz, and the wall thickness of Layer 1 is scanned from 2 mm to 3.5 mm with a scan step set to 0.15 mm and the scan type is set to the specified combination. The mode analysis frequency is given as \(f_{0}\) = 10,000 Hz for the interior of the pipe, the mode search method is set to manual search, the APPACK solver is selected as the mode solver, and the mode search base \(k_{0}\) = 2\(\pi\)\(f_{0}\)/\(c_{0}\). The numerical simulation is solved with the MUMPS direct solver, the free triangular meshing technique is chosen to divide the mesh in the pressure acoustic domain, and the mapped distribution is chosen to divide the mesh in the solid mechanics domain, with 6638 cells in the whole geometric mesh. The physical fields are the pressure acoustic domain, solid mechanics domain, and acoustic-structural coupling domain. The fluid inside the DLLFP is water, the \(x\)-axis is the wavenumber-thickness product \(kd\), and the \(y\)-axis is the angular frequency \(\omega\) in rad/s. The relationship curve between wavenumber-thickness product and angular frequency of the pipe in the case of Layer 2 is calculated, with a wall thickness of 3 mm and the pipe filled with water.  
As Figure 7 shows, some propagation modes in the DLLFP already have large angular frequencies \(\omega\) at a small wavenumber-thickness product \(kd\). Since the group velocity \(c_{\rm g}=\omega\)/\(k\), this means that these propagating guided wave modes have a large group velocity \(c_{\rm g}\). In ultrasonic guided wave nondestructive testing, the echoes of these propagation modes of the guided wave when encountering flanges or damages will be more easily extracted, which ensures the reliability of the judgments derived from the analysis of the echoes.
## 4 Experiment and Analysis
### Comparison of Various Models on KAIST Dataset  
For the sake of evaluating the universality and reliability of the algorithm, the model's performance was further verified on the cleaned KAIST pedestrian dataset in this study. The results, as displayed in Table 6, in comparison with the most progressive YOLO series models, the algorithm's performance presented in this study is better, achieving ideal results in various evaluation indicators. This demonstrates that our model boasts a powerful capacity to generalize and can be flexible given the detecting requirements of various environments. Specifically, our precision, recall, F1 score, and mAP are as high as 96.6%, 92.2%, 94%, and 97.5%, outperforming the baseline model by 2.4%, 2.5%, 2%, and 2.7%, respectively. In general, our mAP is 2.7 to 17.9% higher than that of other methods, and the running time is reduced by 50 to 84%, confirming that our model is highly adaptable to different datasets and reduces the risk of performing well on a single dataset while having sufficient generalization ability. Figure 11 provides a more intuitive and clear data comparison trend to fully assess the overall effectiveness of the method  
Figure 10: Performance curves of the proposed infrared target detection method. (**a**) PR curves; (**b**) F1 curves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Person** & **Bicycle** & **Car** & **mAP** \\ \hline PearlGAN [11] & 54.0 & 23.0 & 75.5 & 50.8 \\ MMTOD-CG [37] & 50.3 & 63.3 & 70.6 & 61.4 \\ CMPD [38] & 69.6 & 59.8 & 78.1 & 69.2 \\ IARet [39] & 77.2 & 48.7 & 85.8 & 70.6 \\ YOLOF [40] & 67.8 & 68.1 & 79.4 & 71.8 \\ FCOS [41] & 69.7 & 67.4 & 79.7 & 72.3 \\ BU(AT,T) [3] & 76.1 & 56.1 & **87.0** & 73.1 \\ BU(LT,T) [3] & 75.6 & 57.4 & 86.5 & 73.2 \\ TerminalDet [24] & 78.2 & 60.0 & 85.5 & 74.6 \\ YOLO-FIR [43] & **85.2** & 70.7 & 84.3 & 80.1 \\ Cascade R-CNN [44] & 77.3 & **84.3** & 79.8 & 80.5 \\ YOLOX [45] & 78.2 & **85.4** & 80.2 & **81.2** \\ Ours & **85.1** & 76.8 & **90.1** & **84.0** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance effect of various algorithms on FLIR dataset when IOU = 0.5. The best two data are described in bold.
# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 3 Smart Cities' Technologies
### Internet of Things  
The importance of IoT for smart cities cannot be overstated, as without it, the current models of smart cities would be impossible. IoT refers to autonomous devices with sensing capabilities, some level of computational power, and the ability to transmit data over a network, which is not required to be the Internet [220; 221]. IoT is used in myriad of independent applications (e.g., healthcare, industrial, or environmental applications) [222], but smart city applications are, currently, the biggest domain in terms of scale and number of devices used. In this section, we will discuss how IoT is used in smart cities.  
IoT allows for the collection of data and the automation of processes in the different dimensions of a smart city. For example, IoT might be used to improve how energy is generated and distributed since smart meters are able to collect data to be used for the creation of power load forecasts, consumer profiles, or the detection of faults [223]. Still, in terms of energy, smart grids allow for traditional power generation methods, microgrids and distributed power generation to work in conjunction to allocate power as needed [224]. IoT also allows the active monitoring of faults in city infrastructures [225], the maintenance of public facilities (e.g., continuously monitoring the conditions of green spaces such as temperature and humidity [226] and providing smart irrigation to efficiently use water resources [227]), or improving the construction processes in various ways (e.g., worker safety, planning, or logistics) [228]. IoT also impacts mobility by improving transportation in urban and rural areas [229; 230] and how aviation companies handle air traffic management or potential issues [231]. Additionally, IoT might optimize healthcare provision [232; 233; 234; 235] and facilitate the proactive participation of the citizens in the city government [236].  
However, IoT also brings with it challenges; the data collected need to be stored and analyzed, and the tools normally used for these tasks are not designed for the volume generated. Furthermore, new security and privacy challenges have arisen, and new solutions must be designed. Additionally, more dynamic and adaptable systems are required for control and automation as systems become more complex, necessitating the use of AI systems. These challenges are magnified by the scale of smart cities; therefore, more advanced solutions are needed.
## 4 Attack via Demand Response
### Manipulating Power Demand (Load)  
**Case 3:** Malicious load curtailment: An adversary has compromised DRAS or cyber system in PU to send malicious commands to X% of smart meters in an area to perform irrelevant load curtailment.  
Load curtailment is a DR function to request customers who are enrolled in the DR program, and they participate to reduce their power consumption in response to contingency. It is treated as a regulating reserve. PU has the power supply and consumption information of all areas, and DRAS has information about its own area. If there is any contingency, such as low power generation or high power demand, they know how much load to curtail from a specific area. The DRAS server decides the number of customers in its area and how much load each customer should curtail on a rotating basis [32]. This satisfies two objectives: (1) fairness, since the load is spread across all the customers, and (2) the impact of load curtailment on a customer is reduced. Once decided, it sends load curtailment commands to all customers.  
**Case 4:** Malicious load shedding: An adversary has compromised DRAS or cyber system in PU to send malicious commands to X% of smart meters in an area to perform irrelevant load shedding.  
Load shedding is used to reduce the load in response to contingency where the grid is not able to provide generation to satisfy demand. This functionality is used in extreme conditions where there is a huge generation loss in an area. It is treated as a contingency reserve. DRAS decides the number of customers in a zip code to send disconnect signals to shed the load.
# Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators
## 2 Modeling and Control
### Control
#### 2.2.1 PI Controller  
A PI controller (Figure 1c)) is a classical control scheme that utilizes feedback to calculate an error from the desired output and adjust the control action based on that error. Proportional, integral, and derivative terms with adjustable coefficients are summed up to acquire the new action. There are many ways to tune the coefficients of the PI controller. In this work, the coefficients of the PI controller were tuned by identifying a linear state-space representation of the system from simulation data and designing a robust controller for it. The tuning algorithm used the Zieger-Nichols second method [22] to estimate the coefficients of the PI with at least 10 dB gain and 100 degrees phase margins. PI controllers are powerful and flexible; however, they are not optimal, in general. The problem is that they do not use a model of the system, but only feedback error and constant coefficients, meaning that they are reactive, instead of proactive