esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
## 3 Results and Discussion
### Treatment of Rapeseed
This contrasts with the obtained results for the conical reactor for 3 min treatment time and is possibly a consequence of the shorter gap size and different high voltage electrode configurations implemented by [PERSON] et al. (2017). Germination impairment of rapeseed treated indirectly with multiple filling conditions of plasma processed air (microwave discharge) for decontamination purposes was also reported by [PERSON] et al. (2017). On the other hand, single filling conditions did not completely harm the seeds which emphasize the importance of precisely investigating various parameters for different plasma sources. The germination acceleration is also indicated in Table 1, where a significant decrease in t\({}_{50}\) value was observed for D = 10%, 50%, 70%, and 90% when compared to both control groups. The t\({}_{50}\) value is the time when 50% of the viable seeds are germinated. Therefore, a reduction in this value indicates a faster germination rate. Germination acceleration caused by non-thermal plasmas has been observed for other plant species, such as wheat ([PERSON] et al., 2018), barley ([PERSON] et al., 2017), radish ([PERSON] et al., 2018), chickpea ([PERSON] et al., 2018), and beans ([PERSON] et al., 2018). In the case of rapeseed, [PERSON] et al., 2017 ([PERSON] et al., 2017), and [PERSON] et al., 2018 ([PERSON] et al., 2018), also observed faster germination for treatments using low-pressure discharges. In addition, [PERSON] et al., 2015 ([PERSON] et al., 2015), reported an acceleration in the germination of rapeseed under drought stress after treatment using a low-pressure CCP reactor. Another important parameter that can be assessed via a fitted germination curve is the germination uniformity (U\({}_{25-75}\)). [PERSON] et al., 2018 ([PERSON] et al., 2018), reported an improvement in the germination uniformity of rapeseed in a discharge power-dependent manner after treatment using a low-pressure RF discharge. Here, the uniformity of rapeseed germination was not affected by plasma (Table 1).
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Reliability Analysis Methodology Design  
In this study, reliability modeling and analysis is performed from model-based ideas. AADL enables the modeling of coupled hardware and software systems, however, it is not possible to consider the quantitative estimation of reliability in terms of mission reliability. The GO method is used for the analysis of system reliability, and given its advantages, such as easy validation and proximity to the actual product structure, the reliability of the ship electronic information system is analyzed using the GO method analysis.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 3 Adaptive UAV-BS Detector Based on the HMP Rule
### Computation of the HMP for GAMP-BG-MC Algorithm
#### 3.1.3. Part III Transition Probability Update Part  
The messages from \(f_{S_{k,1}},f_{S_{k,j}}\) to \(p^{10}_{k},p^{01}_{k}\) can be calculated by Equation (7) in [15].  
\[m^{\text{MF}}_{f_{S_{k,1}}\to p^{10}_{k}}(p^{10}_{k})\propto\text{Be}(p^{10}_{ k};B_{s_{k,1}}+1,2-B_{s_{k,1}})\]  
\[m^{\text{MF}}_{f_{S_{k,j}}\to p^{10}_{k}}(p^{10}_{k})\propto\text{Be}(p^{10}_{ k};\frac{B^{10}_{k,j}}{\rho_{k,j}}+1,\frac{B^{00}_{k,j}}{\rho_{k,j}}+1) \tag{25}\]Given the prior messages \(p(p_{k}^{10})\) and \(p(p_{k}^{01})\), the beliefs update by \(b(p_{k}^{10})\propto\text{Be}(p_{k}^{10};\hat{x}_{k},\hat{f}_{k})\) and \(b(p_{k}^{01})\propto\text{Be}(p_{k}^{01};\hat{c}_{k},\hat{d}_{k})\), where the parameters are updated through  
\[\begin{split}\hat{e}_{k}&=B_{s_{k,1}}+e_{k}+\sum_{j= 2}^{J}\frac{B_{k,j}^{10}}{\rho_{k,j}},\qquad\hat{c}_{k}=c_{k}+\sum_{j=2}^{J} \frac{B_{k,j}^{01}}{\rho_{k,j}},\\ \hat{f}_{k}&=1-B_{s_{k,1}}+f_{k}+\sum_{j=2}^{J}\frac {B_{k,j}^{00}}{\rho_{k,j}},\hat{d}_{k}=d_{k}+\sum_{j=2}^{J}\frac{B_{k,j}^{11}}{ \rho_{k,j}},\end{split} \tag{26}\]  
and the parameters \(B_{s_{k,1}},B_{k,j}^{00},B_{k,j}^{01},B_{k,j}^{10},B_{k,j}^{11},\rho_{k,j}\) can be calculated in Equation (24).
## 1 Introduction
Various detection techniques, such as seismographs, GPS, and InSAR (interferometric synthetic aperture radar), have been applied to observe the surface deformation in Taiwan [25; 26; 27; 28; 29; 30; 31] (deeply buried strainmeters are characterized by high precision and wide bandwidth; they directly record crustal deformation and efficiently reduce the effect of man-made noise above the ground [32; 33]). Due to the high sensitivity of the borehole strainmeter, the data not only contains faint signals of seismic activity, but also the response to external environmental changes such as air pressure and rainfall will be found in the data. Figure 2 shows the observation data of the CINT station in 2011. The influence of rainfall can be detected by removing the responses of the air pressure and tide (arrows). The areal strain is compressive during periods with heavy rainfall. The earthquake event catalog shows there is no direct relation with these responses. The effect of rainfall on the strain has been frequently mentioned in groundwater observation studies [34; 35]. It has been suggested that well-confined aquifers can be regarded as quasistraimeters. In a number of studies, the crustal deformation could be calculated using the water level of the confined aquifer [36; 37]. Based on poroelastic constitutive relations [38], the ratio between the water-level change of the confined aquifer and the crustal deformation could be calculated using the earth tide response. The motivation of this paper is that we want to characterize some abnormal strain regarding to the loading of rainfall observed in the borehole strainmeter. We try to understand their mechanisms, and seek numerical solutions to eliminate such anomalies, as the basis for further strainmeter data processing for the accumulation of tectonic strain in the fold-and-thrust belt of Taiwan. Figure 2: Responses of air pressure and rainfall observed on channel 3 (CH3) at station CINT. Light blue lines represent air pressure records; green lines are hourly rainfall; the light gray and purple bars indicate seismic events within 100 km and 50 km from CINT, respectively. \(\varepsilon\) is the residual strain removing atmosphere and tidal response, separately. After removing the response of air pressure and tides, the influence of rainfall becomes apparent (pointed by red arrows).
## 4 Convolutional Neural Network-Based Cooperative Spectrum Sensing
### Convolutional Neural Network-Based Cooperative Spectrum Sensing  
In a deep-learning research, the CNN is widely used in computer vision fields, such as image classification, speech recognition, and handwriting recognition, by making use of spatial characteristics. In this section, we present the process of creating and training a CNN for PU state prediction.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Study of Ambient Temperature on Adsorption  
Three groups of 150 mL solutions with 10, 40, 80, 120, 160, and 200 mg/L cooper ion were prepared. To each sample was added 5 g RSAC particles. The three groups were mixed at 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C separately. To each sample was measured a cooper ion concentration of the supernatant when reaching adsorption equilibrium.  
Figure 3: Copper ion standard curve.  
The Freundlich and Langmuir models are often used to describe the adsorption behavior in solid-liquid systems [19]. Therefore, the adsorption isotherms of the Freundlich and the Langmuir models were plotted using nonlinear fits based on the experimental results of heavy metal ion adsorption by RSAC particles.  
The expression for the Langmuir model equation is (Equation (4)):  
\[\mathrm{q_{e}}=\frac{\mathrm{q_{max}K_{L}C_{e}}}{1+\mathrm{K_{L}C_{e}}} \tag{4}\]  
In Equation (4), \(\mathrm{q_{e}}\) is the adsorption equilibrium adsorption capacity (mg/g), \(\mathrm{C_{e}}\) is the equilibrium concentration (mg/L), \(\mathrm{q_{max}}\) is the maximum adsorption capacity (mg/g), and \(\mathrm{K_{L}}\) is the Langmuir adsorption constant.  
The expression for the Freundlich model equation is (Equation (5)):  
\[\mathrm{q_{e}}=\mathrm{K_{F}Ce^{\frac{1}{2}}} \tag{5}\]  
In Equation (5), \(\mathrm{q_{e}}\) is the adsorption equilibrium adsorption capacity (mg/g), \(\mathrm{C_{e}}\) is the equilibrium concentration (mg/L), \(\mathrm{K_{F}}\) is the Freundlich adsorption constant, and n is a constant related to the adsorption capacity.
## 4 Modeling of the System Components
### Refrigerant Pump
#### 4.2.1 Volumetric Efficiency  
The volumetric efficiency can be written as a function of the leakage flow rate as:  
\[\eta_{vol}=\frac{\dot{m}_{act}}{\dot{m}_{th}}\times 100=\frac{\dot{m}_{th}-\dot{m}_{ lk}}{\dot{m}_{th}}\times 100=\left(1-\frac{\dot{m}_{lk}}{\dot{m}_{th}}\right)\times 100 \tag{16}\]  
where subscripts \"\(act\)\" and \"\(lk\)\" refer to actual and leakage, respectively. The leakage mass flow rate of the pump was modeled using the adiabatic flow in a nozzle, in which the leakage flow travels from the pump discharge pressure to the suction pressure through small clearance gaps. The leakage mass flow rate using this nozzle throttle model can be deduced as the following neglecting the variation of the leakage flow liquid density with pressure:  
\[\dot{m}_{lk}=\rho_{lk}\cdot A_{lk}\cdot v_{lk}=\rho_{su}\cdot A_{lk}\cdot v_{lk} \tag{17}\]  
where the subscript \"\(su\)\" refers to the suction. The fluid velocity in the throttle can be calculated using [PERSON]'s equation:  
\[v_{lk}=\sqrt{\frac{2(P_{ex}-P_{su})}{\rho_{su}}}=\sqrt{\frac{2\Delta P}{\rho_{ su}}} \tag{18}\]  
Combining Equations (17) and (18), the leakage mass flow rate is derived as Equation (19):  
\[\dot{m}_{lk}=A_{lk}\sqrt{2\rho_{su}\cdot\Delta P} \tag{19}\]  
The throttle area (\(A_{lk}\)) represents the average leak area of the pump that was modeled in this work using the non-dimensional viscosity as the following:  
\[\frac{A_{lk}}{A_{bore}}=a\cdot\overline{\mu}=a\left(\frac{\mu\cdot\omega}{ \Delta P}\right) \tag{20}\]  
where \(A_{bore}\) is the bore area of the piston chamber taken from the pump specification sheet, and \(\omega\) is the shaft speed (rad/s). The empirical coefficient \"\(a\)\" can be obtained using the minimization of the objective function (\(f\)) presented in Equation (21) for each data point:  
\[f_{i}=\left(\frac{\eta_{vol,i,val}-\eta_{vol,i,exp}}{\eta_{vol,i,exp}}\right)^ {2} \tag{21}\]  
The coefficient \"\(a\)\" was obtained for each data point and correlated to two non-dimensional numbers of the pump, namely the suction Prandtl number (\(Pr_{su}\)) and the reduced pump speed (\(N_{r}\)) using the regression data fitting method.  
\[a=f(Pr_{su},N_{r}) \tag{22}\]  
\[Pr_{su}=\frac{\mu_{su}\cdot C_{p,su}}{\lambda_{su}} \tag{23}\]  
\[N_{r}=\frac{N}{N_{max}} \tag{24}\]  
where \(N_{max}\) is the maximum pump speed declared in the pump specification sheet (1500 rpm).  
The calculated volumetric efficiency is shown versus the experimental volumetric efficiency in Figure 13, showing the good accuracy of the model. The volumetric efficiency model is needed to assess the real mass flow rate of the pump for different working conditions. The calculated mass flow rate of the pump using the pump speed as the input is plotted in Figure 13, confirming the accuracy of the model for the range of the working conditions of the pump obtained during the experiments.
# The Use of Asymmetric Polynomial Profiles for Planning a Smooth Trajectory
## 2 Materials and Methods
### Constraint of the Single Kinematic Quantity  
It was assumed that the kinematic quantities which can be constrained are: velocity s\({}^{(1)}\)(t), acceleration s\({}^{(2)}\)(t) or jerk s\({}^{(3)}\)(t) that is i = 1, 2, 3. The conditions which the function with constraints must satisfy are:  
\[\forall\limits_{\text{t}\in<0,\text{t}\text{e}>}\left|\text{s}^{(i)}(\text{t}) \right|\leq\text{s}^{(i)}_{\text{max}} \tag{4}\]  
\[\exists\limits_{\text{t}\in<0,\text{t}\text{e}>}\left|\text{s}^{(i)}(\text{t}) \right|=\text{s}^{(i)}_{\text{max}} \tag{5}\]  
The use of the above constraints referring to the function describing velocity and acceleration (i = 1,2) results from the possibility of the drive system [5,7,28,44]. However, the jerk constraint (i = 3) provides: better accuracy of trajectory mapping, continuous and coordinated motion, reduction in the driving loads, limitation of activation of resonance systems resonant frequency [9,28,44,45].  
In order to derive the dependences expressing: the coefficient of polynomial p and the asymmetry k as well as the time of realization of trajectory t\({}_{\text{end}}\) at the optional constraint s\({}_{\text{max}}\)\({}^{(i)}\) and the assumed initial and terminal values: s\({}_{\text{begin}}=\text{s}(0)\), s\({}_{\text{end}}=\text{s}(\text{t}_{\text{end}})\), v\({}_{\text{begin}}=\text{s}^{(1)}(0)\), v\({}_{\text{end}}=\text{s}^{(1)}(\text{t}_{\text{end}})\) one should:  
1. Determine the zero place of the function s\({}^{(i+1)}\)(t) in the interval t\(\in\)\(\langle 0\), t\({}_{\text{end}}\rangle\);
2. Determine the value of the function s\({}^{(i)}\)(t) for successive zero places and then choose the one in which the function s\({}^{(i)}\)(t) archives the largest absolute value;
3. Create a system of three equations in which there are compared: * the dependence for the largest absolute value obtained in stage 2 with the given maximal value of the limited quantity s\({}_{\text{max}}\)\({}^{(i)}\);
* the dependence for the terminal position with the given terminal position;
* the dependence for the terminal velocity with the given terminal velocity;
4. Solve the created system of equations by determining the coefficients of: polynomial p, asymmetry k and time of realization of the trajectory t\({}_{\text{end}}\).
## 2 Problem Formulation and Preliminaries
\(\{t_{k}^{i}\}\) of each node satisfies the following triggering condition:  
\[\mathfrak{F}\Big{(}e_{i}(t),\dot{\xi}_{i}\Big{(}t_{k}^{i}\Big{)}\Big{)}=0 \tag{17}\]  
**Remark 3**. : _An event-triggered transmission strategy is proposed to save communication resources in this paper. Equation (17) states that the state-triggering time of each node is independent and not affected by other nodes. In addition, the event-triggering condition in [30] only satisfies a specified inequality, and the selection of parameters has a significant impact on the stability of the systems. On the contrary, the event-triggering condition (17) obtained on the basis of the system stability in this paper can better ensure the performance of systems. For each node \(i\), if \(t_{k}^{i}\) satisfies the update condition (17), the \(\hat{x}_{i}(t)\) will update at event-triggering instant \(t_{k}^{i}\)._  
Then, the error systems can be written as  
\[ \begin{split}& t\in\Big{[}t_{2l}^{ij},t_{2l+1}^{ij}\Big{)},\\ &\dot{\delta}_{l}(t)=A\delta_{l}(t)+B\Bigg{[}\sum\limits_{j=1}^{N }a_{ij}^{\sigma(t)}\big{(}x_{j}\big{(}t_{k}^{i}\big{)}-x_{i}\big{(}t_{k}^{i} \big{)}\big{)}+b_{i}^{\sigma(t)}\big{(}x_{N+1}\big{(}t_{k}^{i}\big{)}-x_{i} \big{(}t_{k}^{i}\big{)}\big{)}\Bigg{]}\end{split}  \tag{18}\]  
and  
\[ \begin{split}& t\in\Big{[}t_{2l+1}^{ij},t_{2(l+1)}^{ij}\Big{)}, \\ &\dot{\delta}_{l}(t)=A\delta_{l}(t)+BK\Bigg{[}\sum\limits_{j=1}^{N }a_{ij}^{0}\big{(}x_{j}\big{(}t_{k}^{i}\big{)}-x_{i}\big{(}t_{k}^{i}\big{)} \big{)}+b_{i}^{0}\big{(}x_{N+1}\big{(}t_{k}^{i}\big{)}-x_{i}\big{(}t_{k}^{i} \big{)}\big{)}\Bigg{]}\end{split}  \tag{19}\]  
Let \(\hat{x}_{N+1}\big{(}t_{k}^{i}\big{)}=\hat{x}_{N+1}(t)\), and from the system (18) and (19), we have  
\[ \begin{split}\dot{\delta}(t)\!=\!\begin{cases}\big{(}I_{N}\otimes A +G\otimes\Gamma\!-\!\bar{H}_{\sigma(t)}\otimes BF\big{)}\delta(t)\!-\!\Big{(} \bar{H}_{\sigma(t)}\otimes BF\big{)}\!e(t)\!,\!t\!\in\!\Big{[}t_{2l}^{ij},t_{2 l+1}^{ij}\Big{)}\\ \big{(}I_{N}\otimes A+G\otimes\Gamma\!-\!H\otimes BK\big{)}\delta(t)\!-\!(H \otimes BK\big{)}\!e(t)\!,\!t\!\in\!\Big{(}t_{2l+1}^{ij},t_{2(l+1)}^{ij}\Big{]} \end{cases}\end{split}  \tag{20}\]  
where \(\delta(t)=col(\delta_{1}(t),\delta_{2}(t),\ldots,\delta_{N}(t)),e(t)=col(e_{1}( t),e_{2}(t),\ldots,e_{N}(t))\), \(H=\mathcal{L}+\mathcal{B}\), \(\bar{H}_{\sigma(t)}=\mathcal{L}_{\sigma(t)}+\mathcal{B}_{\sigma(t)}\), \(\mathcal{B}=diag(b_{1}b_{2},\ldots,b_{N})\), \(\mathcal{B}_{\sigma(t)}=diag\Big{(}b_{1}^{\sigma(t)},b_{2}^{\sigma(t)},\ldots,b _{N}^{\sigma(t)}\Big{)}\). The goal of this paper is to design an event-triggered distributed control law (3) such that CCPNs (1) under DoS attacks are stable and the normal transmission of information is guaranteed. The following lemma is needed in the sequel. **Lemma 1** ([25]). : _For positive definite matrix \(M\in\Re^{n\times n}\) and symmetric matrix \(N\in\Re^{n\times n}\), the following inequalities hold_  
\[ \begin{split}\theta_{\min}\Big{(}M^{-1}N\Big{)}x^{T}Tx& \leq x^{T}Nx\leq\theta_{\max}\Big{(}M^{-1}N\Big{)}x^{T}Mx\\ \theta_{\min}(N)x^{T}x&\leq x^{T}Nx\leq\theta_{\max}(N )x^{T}x\end{split} \]
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Hybrid Beamforming for Multi-User mmWave Networks  
After channel estimation, hybrid beamforming (HB) is a fundamental technology in mmWave networks that integrates analog and digital beamforming to enhance spectral efficiency at a lower hardware cost. This paper introduces an optimized HB model that incorporates adaptive beam training, machine learning-based optimization, and multi-user scheduling to promote network performance and alleviate interference. The proposed HB method involves two main stages. First, analog beamforming optimization is carried out with a greedy beam search algorithm, which repeatedly chooses phase configurations for phase shifters to achieve maximum signal strength with minimum interference. This guarantees effective signal transmission to multiple users. Then, digital beamforming is invoked using a zero-forcing precoder to further optimize signal transmission and suppress inter-user interference. To enhance beamforming choices, a multi-agent deep reinforcement learning (MADRL) system is integrated, in which the multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm learns to change beamforming methods based on real-time network states like signal power and interference levels.  
For an improved beam, the digital and analog hybrid beam training with a long short-term memory (DAH-BT-LSTM) network is adopted. This system learns over time from previous beam training experience and minimizes excessive beam search so that it adapts adjustments when in a changing network environment. By combining machine learning and reinforcement learning methodologies, the devised HB approach optimizes beam alignment, reduces training overhead, enhances performance, and saves interference. The overall strategy guarantees stable and efficient communication in 5G mmWave networks, and it is thus apt for high-density user environments.
## 3 IVR-Based SDO Streaming Distance Education System
### IVR-SDO Streaming Education System Architecture
#### 3.1.1 SDO Client  
The main components of the SDO client are the I/O module, rendering engine, network module, and SDO image transfer module.  
For generating realistic human avatars that represent users' presence, the rapid 3D avatar creation method proposed by [PERSON] et al. was used [30]. This method can quickly and automatically create a user avatar within 2 min using a single depth camera and turntable. For the simplicity of implementation, this study uses only a head and hand model instead of a full-body avatar model. The I/O module tracks the head and hand poses using an HMD and a controller to control the avatar according to the user's motions. To support voice conversations between users, the voice I/O is controlled through the microphone and earphones attached to the HMD. The rendering engine consists of a sound engine and graphics engine, which enables verbal interaction by combining the avatar engine that controls the avatar model according to the user's motions with the voice sent from remote users, which renders SDO images received from the network module in a 3D virtual space in an immersive manner.  
The network module transfers the necessary data, such as SDO images, voices, and head and hand poses, to the network for SDO distance education. The SDO image transfer module merges the SDO images that are split-transmitted from the relay server via multiple channels to their original size and decodes them using the JPEG2000 decoder.
# A Trustworthy Communication Hub for Cyber-Physical Systems
## 4 Building Blocks of the CPS Communication Hub
### Trustworthy Information Sharing Process  
The information sharing process requires preceding creation of trust relationship/agreements between owners of the physical resources, the presence of the resources in the communication spaces of the owners, and enabling basis for trustworthiness. The information sharing of the CPS hub relies on the publish/subscribe paradigm, because in the CPS system, there are typically multiple and a variety of numbers of potential consumers for a single source of information [7].  
The sharing of information contains the following phases; Figure 7. The first phase connects the owner to the CPS communication hub, authentication of the owner, and registration of the presence of the physical resources of the owner into the home domain of the owner. As the result of this step, the presences of the physical resources are registered in the communication spaces of the owner in the CPS communication hub. The referred resources can be identified by <owner>@<domain>/<resource> notation.  
In the second phase, some resource may subscrib to receive information published by some other resources. The publish/subscribe service requires a separate authentication done using the same owner identity to the CPS information sharing service as that applied in step 1. The published/subscribed information is identified by notation <owner>@<domain>/<resource>/<topic>. Any resource can publish information according to its own strategy, if the authentication has been done in a successful manner. However, when a resource subscribes for information published by other owners/resources, then the trust service checks whether it is allowed or not.  
In the third phase, the CPS trust service checks the status related to the agreements between the owner (A), which is publishing the information, and the other owner (B), which wants to subscribe to the information published by A. If there is an agreement between A and B, then the subscribed information may be delivered to the subscriber B. However, before the delivery, the security policies of the owner are checked by the CPS trust service using an external policy-based authorization service.  
Let us assume that the <owner> (Z) has a <physical resource> (B), which wants to subscribe the <logical resource> information that is published by the <physical resource> (A), which belongs to the <owner> (X), Figure 8. The respective information-sharing algorithm is represented in Figure 9.  
Figure 8: Information sharing process.  
Figure 7: Information sharing of CPS communication hub.