esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison with Climeworks Data  
Climeworks has developed a DAC plant at semi-commercial scale, and uses an amine-functionalized cellulose chemisorbent, as discussed here. The geometry of the adsorbent bed is the same as that used in our calculation. Overall, results show a good agreement between operating conditions and energy consumptions evaluated by us for the APDES-NFC-FD sorbent with those reported proposed by this DAC Company.  
The duration of a full cycle is lower for Climeworks and in the range of 4-6 h, because only the adsorption of CO\({}_{2}\) is considered in our model, whereas some humidity is present in the real air flow rate. However, experimental analyses show that after 5 h adsorption time, the CO\({}_{2}\) uptake curves are still on an upwards path, while the corresponding H\({}_{2}\)O curves have reached a steady state for all relative humidity values [105]. This explains why Climeworks utilizes a shorter cycle.  
For other operating parameters, no significant differences are present. In fact, for Climeworks the amount of captured CO\({}_{2}\) is 135 kgCO\({}_{2}\)/day, with an average single pass recovery of 0.9. Regarding the energetic consumption, the proposed electrical energy consumption is between 200 kWhel/tonneCO\({}_{2}\) and 450 kWhel/tonneCO\({}_{2}\). The required thermal energy consumption is 1500-2000 kWhh/tonneCO\({}_{2}\).  
Regarding the economic analysis, our total cost (751 USD/tonneCO\({}_{2}\)) is the same order of magnitude of that proposed by Climework (600 USD/tonneCO\({}_{2}\)) [111].  
Moreover, our calculated total cost can be higher than that estimated when humidity is present in the air. In fact, as reported in [PERSON] et al. [104], the energy consumption for the regeneration can increase up to 20% when the relative humidity increases from 20% to 80%.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 3 System Description
### Siding Loop Scenario  
As part of our design strategy to enhance the practicality of the system, we considered incorporating sliding loops with length \(\mathrm{L_{s}}\) in Figure 2, where the BS equipped with two transceivers located at the midpoint of the sliding loop \(\frac{\mathrm{L_{s}}}{2}\), where \(\frac{\mathrm{L_{s}}}{2}<\mathrm{L_{c}}\), at a height matching that of the train's roof of 4 m. It is designed to allow trains to temporarily stop or park aside from the main line to facilitate various operations, such as allowing faster trains to pass, changing locomotives or train configurations, or providing a place for trains to wait before entering a congested section of the track [32]. Positioning the BS at the midpoint ensures optimal communication with both trains, while shifting it towards the beginning of the sliding loop from any direction would lead to a trade-off, where one direction would receive superior communication, but the other direction would suffer from reduced signal quality or coverage; in this particular case:  
* The FSO BS first establishes communication with train operators, coordinating the reservation of the sliding loop for one train while the other train waits.
* Once communication is established, then it requests speed adjustments for safe passage and controls train entry and exit.
* Throughout, it continuously monitors train positions, speeds, and activities, implementing safety measures and emergency stop commands as needed to maintain railway system safety and efficiency.
## 2 Experimental Measurements
### Experimental Procedure
In the Schwarz-P lattice structure, the inlet cross-section of each cell is circular, so \(A\) and \(P\) are given by \(\pi\)R\({}^{2}\) and \(2\pi\)R, respectively, where R is the radius. In contrast, calculating the hydraulic diameter D\({}_{h}\) for the gyroid structure is more complex, as its cross-section forms a sinusoidal channel [31]. Dimensional parameters, including local heat transfer coefficients, were identified. These coefficients are expressed in units of W/m\({}^{2}\)\({}^{\circ}\)C and can be represented mathematically as follows [32]:  
\[h=\frac{q^{\prime\prime}}{T-Tin}=\frac{q^{\prime\prime}}{\Delta T} \tag{5}\]  
In all scenarios discussed above, experimental data form the foundation of the calculations. The average \(Nu\)'s uncertainty can be determined using the following method [32]:  
\[\delta Nu=\sqrt{\left(\frac{\partial Nu_{x}}{\partial x}.\delta x\right)^{2}+ \cdots+\left(\frac{\partial Nu_{x}}{\partial y}.\delta y\right)^{2}+\cdots+ \left(\frac{\partial Nu_{x}}{\partial z}.\delta z\right)^{2}} \tag{6}\]  
The local heat transfer coefficient, \(h_{x}\), varies depending on the position along the heater surface, while \(T_{x}\) indicates the local surface temperature at that point. The inlet temperature, \(T_{in}\), sets the initial condition for the system's thermal behavior. \(D\), indicating the tube inlet diameter; and \(u_{in}\), which is the inlet fluid velocity across the unit. The flow behavior of the fluid is governed by its kinematic viscosity, \(\
u\), while \(k_{f}\) denotes the thermal conductivity of fluid, which measures its ability to conduct heat. The conducted uncertainty analysis revealed that the maximum uncertainty in the local \(Nu\) calculations was constrained to 2.6%, ensuring a high degree of confidence in the experimental results. Temperature gradients in TPMS structures can be influenced by pressure losses across the flow path. In regions with complex geometry where flow restrictions occur, local temperature variations may be affected by pressure-related phenomena. However, since all tested configurations in this study experienced similar flow conditions through geometrically identical TPMS structures, the relative influence of pressure effects remains consistent across scenarios. This consistency enables valid comparative thermal performance analysis between the different material and fluid combinations, which is the primary focus of this investigation.
## 4 Experiments and Analysis
### Comparative Analysis of the Results of Different Domain Adaptation Methods  
In order to verify the effectiveness of the distribution difference measurement method in this paper, it was first compared with several common adaptive methods. In the performance of migration experiments for the different adaptive methods, when the B and C datasets are selected as the source domains and A is selected as the target domain, the task is represented by B-C\(\rightarrow\)A; when datasets A and C are selected as the source domains and B is selected as the target domain, the task is represented by A-C\(\rightarrow\)B. A-B\(\rightarrow\)C represents the task when the A and B datasets are selected as the source domains and C is selected as the target domain.
###### Abstract  
Wireless Underground Sensor Networks (WUSNs) have been showing prospective super-vising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.  
wireless underground sensor networks; deep learning based cooperative communication channel; multi-input-single-output +
Footnote †: journal: Sensors  
0000-0001-556-4000][PERSON]  
0000-0002-224-75
# The Chemistry of Stress: Understanding the 'Cry for Help' of Plant Roots
## 3 Chemistry of Microbial Recruitment by Roots of Plants under Siege
In the follow-up study, the same bacteria was shown to trigger induced systemic resistance (ISR) against _Colletotrichum graminicola_[50]. In a recent study, benzoxazionids produced by maize plants was also found to alter the composition of root-associated microbiota, that in turn enhanced defense of leaves against the aboveground insect _Spodoptera frugiperda_[51]. While the concept of root exudate-mediated \"cy-for-help\" is shown for plants under biotic stresses, several studies suggest that this concept may also apply to plants exposed to abiotic stresses. For instance, roots of red clover (_Trifolium pratense_) grown in Fe-deficient soil accumulated phenolic compounds, which stimulated a siderophore-secreting _Pseudomonas_ sp. When tested in plants, the siderophores produced by the bacterium were effective in solubilizing Fe, thereby improving its uptake by clover plants in Fe-deficient soils [52]. Furthermore, _Arabidopsis_ roots are known to produce the coumarin scopoletin under Fe-deficiency [36]. The production of this compound was found to selectively impact the assembly of microbial community in the rhizosphere, resulting in enhanced plant growth under Fe limitation [53]. Under salt stress, roots of the halophyte _Limonum sinesne_ secreted several organic acids including 2-methyl butyric acid and palmitic acid with positive effects on the growth and chemotaxis of _Bacillus flexus_ KLBPM 491, a beneficial bacterium naturally found on _L. sinesne_. Re-introduction of this strain into soil significantly promoted growth of _L. sinesne_ seedlings under salinity stress [54]. Hence, root-derived exudates, apart from their direct role in plant defense, may also attract and activate plant-protective members of the rhizosphere microbiome to alleviate the imposed stresses. The summary of representative studies on direct and indirect role of root exudates on plant fitness can be seen in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Stress Type** & **Plant Species** & **Type of Exudation** & **Role of Exudate in Plant Defence** & **Reference** \\ \hline
**Biotic stress** & & & & \\ _Fusarium graminearum_ & Barley (_Hordeum_ _culgare_) & \(t\)-Cinnamic acid (water-soluble) & Direct via antifungal activity & [55] \\ _Pythium ultimum_ & Sweet Basil (_Ocimum_ _bacilicum_) & Rosamarinic acid (water-soluble) & Direct via antifungal activity & [56] \\ _Fusarium verticillioides;_ & Maize (_Zea mays_) & Terpenoids; zealexins, kauralexins & Direct via antifungal activity and suppression of herbivory growth & [37] \\ _Diabrotica balteata_ & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Representative studies where plants under various stresses can produce specific root metabolites that can directly and indirectly (via recruitment/modulation of beneficial root associated microbiome) affect plant fitness. \begin{table}
\begin{tabular}{l l l l l} \hline
## 2 Materials and Methods
### Operation and Control Mechanism  
The Xinlin V5 series FPGA is used in DCGE, and it communicates with the host computer via USB. The display control process is mainly managed by the host computer program, which can realize a 1\(\sim\)16 bit gray level display. The control process includes initialization, adjusting grayscale image, displaying grayscale image, and stopping display. We connected the DLP after power-on to the PC of the host computer, and the main interface is shown in Figure 6a. The interface is mainly composed of the grayscale preview area, control button area, and device information district. The image to be displayed is divided into six blocks, and each block can display different gray levels according to user settings. Firstly, the equipment needs to be initialized. The host computer modifies the control register on the DLP through the USB interface, so that the DLP development board enters the display preparation state. Secondly, users define the precision and content of the grayscale image to be displayed. If 8-bit is selected, the grayscale image has 256 gray levels, and users can then set the gray value of the six  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Gray Bit**} & **Fast Clear** & **Reset Time** & **Load Time** & **Active Time** & **Blank Time** & **Frequency** \\  & **Time (\(\upmu\)s)** & **(\(\upmu\)s)** & **(\(\upmu\)s)** & **(\(\upmu\)s)** & **(\(\upmu\)s)** & **(Hz)** \\ \hline
1 & & & & 50 & 0 & 20,000 \\
2 & & & & 150 & 0 & 6667 \\
3 & & & & 125 & 150 & 3636 \\
4 & & & & 250 & 150 & 2500 \\
5 & 0.64 & 14 & 30.72 & 500 & 150 & 1538 \\
6 & & & & 1000 & 150 & 870 \\
7 & & & & 1900 & 150 & 488 \\
8 & & & & 3800 & 150 & 253 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant parameters of DLP7000.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Category**} & **Parameter** & **Value** \\ \hline \multirow{2}{*}{DMD} & bit & 8 \\  & frequency & 253 Hz \\ \hline \multirow{4}{*}{Light source} & type & Xenon lamp \\  & power & 140 W \\ \cline{1-1}  & color temperature & 2856 K \\ \cline{1-1}  & mode & Reflection + V(\(\lambda\))filter \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of DCGE.  
Figure 5: Schematic diagram of gray generation algorithm.  
subareas in the preview area separately. Lastly, the approximate distribution of the generated grayscale image can be observed through the preview window, as shown in Figure 6(b). We downloaded this image to the onboard memory of the DLP, and after the data transfer was complete, the DLP displayed the grayscale image according to the preset display mode. In this way, we achieved accurate control and output of each gray level, and the luminance of the output image is measured through a photometer.
# A Charging and Discharging Data Privacy Protection Scheme for V2G Networks Based on Cloud-Fog-End
## 2 Problem Formalization
### Safety Objectives  
To ensure the secure upload of large-scale charging and discharging privacy data in V2G networks, the proposed privacy protection scheme should meet the following security objectives:  
1. Conditional anonymity: The real identity of EV users must be kept confidential. However, if necessary, the TA can track the real identity of malicious EV users for accountability.
2. Confidentiality: Charging and discharging data should be kept confidential to ensure that attackers cannot eavesdrop on plaintext data during communication.
3. Unforgeability: Ensure that attackers cannot forge CP/FN uploaded charging and discharging data reports.
4. Public verifiability: The signcryption can be verified through public information.
5. Resistance to attack: In addition to the eavesdropping, forgery, and tampering mentioned above, the scheme must also resist impersonation attacks, replay attacks, and so on.
## 1 Introduction
SIL simulation relies mainly on a separate computer where the flight code is completely simulated, along with a sensor model and vehicle dynamics to provide similar results to those of a real-life experiment; SIL is mainly used during the development stage of the simulation model. The SIL platform relies on UDP and TCP communication among its various parts, this communication is mainly governed by what is known as Lockstep, which is a technique developed to synchronize all the simulator parts together to avoid any packet loss or desynchronization between them. Lockstep issues a command for each cycle, performed by the controller to mimic the operation in real life [7; 8]. HIL is a good tool for evaluating the performance of deployed models and algorithms on real-world hardware. The HIL simulations run the flight code and algorithms on the real-time computer, models the vehicle dynamics on the host computer, feeds data through a serial connection, and then visualizes the received responses [8; 9; 10]. Most HIL systems are developed for simulating the quadrotor only [11; 12; 13; 14]. Recently, SIL simulations with Gazebo and PX4 were conducted by [PERSON] [6] for a quadrotor with a suspended load. The intentions were to test his design for a landing system. However, no HIL simulations for the quadrotor have been demonstrated; therefore, there is a gap which must be filled by researchers. Pixhawk hardware has been widely used for UAV applications in recent years [15]. It offers high-quality autopilot hardware at a low cost and with a high level of availability. Furthermore, the open-source code PX4, which is autopilot firmware for driving unmanned aerial and ground vehicles, can be installed on this device. Pixhawk can be used in conjunction with simulation software, such as Gazebo, to create a HIL system. [16; 17; 18]. The Gazebo software is popular in the robotics community and is completely open-source so that the user can easily define 3D virtual worlds, sensor models, and communication protocols. In particular, this software contains the open dynamics engine (ODE), which can present a system model robot with high accuracy in real-time conditions. ODE can provide real-time dynamical simulations and the corresponding sensor readings for the UAVs [18; 19]. The present paper is organized as follows: The quadrotor model of the quadrotor and the suspended load is given in Section 2; the suggested controller is given briefly in Section 3, followed by the procedure to develop the HIL environment in Section 4. The simulation results are discussed in Section 5. The conclusion is given in Section 6.
## 3 Analysis of the Scattering Phenomenon in Underground Mines and Distribution of Dust Particles
### Analysis of the Interaction of Incident Light with Suspended Dust Particles  
In UM tunnels, when a light beam interacts with an air molecule (Rayleigh scattering) or a suspended dust particle (Mie scattering), the path that this light beam follows until it reaches the optical receiver is modified. The variable that describes this change is the deflection angle \(\theta_{s}\), as can be seen in the geometric pattern of the scattering effect in Figure 2. \(\theta_{s}\) is determined by the angular distribution of scattering, namely the scattering phase function \(p(\theta_{s})\). Specifically, the generalized Rayleigh scattering phase function is expressed as  
\[p_{ray}(\theta_{s})=\frac{3[1+3\gamma+(1-\gamma)cos^{2}(\theta_{s})]}{16\pi(1 +2\gamma)}, \tag{14}\]  
where \(\gamma=\delta/(2-\delta)\) is an atmospheric parameter. On the other hand, the Mie scattering phase function can be represented by the generalized Henyey-Greenstein function as follows:  
\[p_{mie}(\theta_{s})=\frac{1-g^{2}}{4\pi}\Bigg{[}\frac{1}{\sqrt{(1+g^{2}-2 gcos (\theta_{s}))^{3}}}+\frac{g(3 cos^{2}(\theta_{s})-1)}{2\sqrt{(1+g^{2})^{3}}} \Bigg{]}, \tag{15}\]  
where \(g\) is an asymmetric atmospheric factor expressed as  
\[g=\frac{\int_{0}^{\infty}G(r_{d},\lambda,m)n(r_{d})dr_{d}}{\int_{0}^{\infty}n (r)dr_{d}}, \tag{16}\]  
and \(G\) is obtained by  
\[G(r_{d},\lambda,m)=\frac{4}{d_{s}^{2}Q_{sca}}\sum_{n=1}^{\infty}\bigg{[}\frac {n(n+2)}{n+1}(a_{n}a_{n+1}+b_{n}b_{n+1})+\frac{2n+1}{n(n+1)}(a_{n}b_{n}) \bigg{]}. \tag{17}\]Finally, to consider the entire effect of the interaction of the light beam with dust particles and air molecules, the joint effect of Rayleigh and Mie scattering must be modeled as a Bernoulli distribution. Therefore, the total scattering phase function \(p_{total}(\theta_{s})\) along with its respective Probability Distribution Function (PDF) are expressed as  
\[p_{total}(\theta_{s})=\frac{k_{sca}^{Ray}}{k_{sca}}p_{ray}(\theta_{s})+\frac{k_{ sca}^{Mie}}{k_{sca}}p_{mic}(\theta_{s}), \tag{18}\]  
\[f_{sca}(\theta_{s})=p_{total}(\theta_{s})\sin{(\theta_{s})}. \tag{19}\]
## 3 General Findings
### Key Findings Addressing the Research Questions
#### 3.2.2 Identified Determinants for the BIS Adoption and Acceptance
Some new determinants are also emerged by the analysis of the findings such as infrastructure, result demonstrability, facilitating conditions, requisite knowledge & skills, institutional isomorphism, organization culture, information access, performance perception, vendor support, level of BIS use, capacity, compliance body, user participation and involvement, information culture, intrinsic motivation, extrinsic motivation, playfulness, perceived enjoyment, computer anxiety, distribution of power, decision confidence, perception value of strategy, positive mood outcomes, trust, risk management, consumer pressures, and some others, that have not used commonly for the BIS adoption in selected studies. It is confirmed by the previous studies that technological determinants are more stable influencing the adoption decisions of BIS as compared to other both organizational and environmental determinants [62; 74; 91] and this perception is validated by the findings of the current study again. A limited number of studies endorsed and addressed the significance of the environmental and individual determinants. The BIS adoption has been influenced the least or neglected by the researchers in terms of environmental and individual determinants in prior studies [3; 80; 102]. In addition, the outcomes of some studies endorsed that significance of determinants are based on their context of use. For example, some studies have used technological, organizational and environmental determinants at an organizational level, and some have used the same determinants at individual level. A tag cloud is created which is based on the frequency of use of each determinant. The larger text font in Figure 7 reveals the higher frequency of that particular determinant. This figure is provided a convenient way to compare and visualize the significance of each determinant. For instance, organization size, cost, relative advantage, competitive pressure, complexity, compatibility, organization readiness, and top management support were highly ranked and showed in larger font All determinants font size is depicted according to their rank.