esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulated Study of Different Decoupling Techniques
### Defected Ground Structure (DGS)  
A narrow vertical slot at the center of the ground plane is added to enhance isolation, as displayed in Figure 16. This antenna is almost omnidirectional and has almost the same  
Figure 14: (**a**) UMA with DS, (**b**) radiation pattern, (**c**) \(S_{11}\), and (**d**) \(S_{12}\).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Antenna Structure** & **Isolation (dB)** & **Antenna Structure** & **Isolation (dB)** \\ \hline Without CG & \textgreater{}15.1 & Without CG & \textgreater{}15.1 \\ \hline With 90\({}^{\circ}\) and without CG & \textgreater{}19.5 & With 180\({}^{\circ}\) and without CG & \textgreater{}15.5 \\ \hline With CG & \textgreater{}11.3 & With CG & \textgreater{}11.3 \\ \hline With 90\({}^{\circ}\) and CG & \textgreater{}13.6 & With 180\({}^{\circ}\) and CG & \textgreater{}15.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Isolation results of 90\({}^{\circ}\) and 180\({}^{\circ}\) placed AEs.  
Figure 15: (**a**) UMA with NL, (**b**) radiation pattern, (**c**) \(S_{11}\), and (**d**) \(S_{12}\).  
\(S_{11}\) plot as two-port UMA with CG. The isolation is mainly increased at lower and higher frequency ranges as compared to two-port UMA with CG.
## 7 Conclusions  
In this paper, we have furnished a small-sized antenna covering the n257/n258/n261 band radiating in a frequency range from 24.25 to 29.5 GHz with a bandwidth of 5.25 GHz. The antenna also covers the ISM24 GHz band. Firstly, the characteristics of the single-element antenna were described, and, later on, the design was extended to a four-port MIMO antenna. Very small overall dimensions of 16.2 mm \(\times\) 16.2 mm \(\times\) 0.254 mm are achieved for the proposed four-port antenna. With regard to the MIMO parameters, the ECC is less than 0.002, and the DG is greater than 9.99 dB, and these parameters, as discussed in relation to the mentioned band, are within the tolerance limits. The isolation between the neighboring MIMO elements is also less than \(-\)23.5 dB.  
Conceptualization, A.S. and S.S.; Methodology, A.S. and S.S.; Software, A.S. and S.S.; Validation, A.S. and S.S.; Formal analysis, R.K.; Investigation, R.K.; Resources, R.K., V.S. and G.W.; Writing--original draft, V.S. and G.W.; Writing--review & editing, R.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Improving a Process-Based Model to Simulate Forest Carbon Allocation under Varied Stand Density
## 3 Results  
### Comparison of Stand Characteristics  
A comparison of the observed and predicted DBH and heights showed good consistency (Figure 1a-d). The determination coefficient (R\({}^{2}\)) of the mean DBH and mean height ranged from 0.7 to 0.8 (\(p<0.001\), Figure 1), and the modified model by \(W\), \(L\), and \(N\) had higher regression coefficients than the original model. The \(MAE\) range of the mean DBH and mean height was 15.4%-24.8% and the \(NRMSE\) range of all of the stand variables was 0.2-0.4 (Table 3). The Willmott indices of agreement between the observed and simulated basic stand variables were 0.6-0.8 (Table 3). In the modified model, the \(MAE\), \(NRMSE\), and Willmott index were better than the original model.  
Figure 1: Comparisons between simulations for the original model (**a,b**) and modified model (**c,d**), and the observed diameter at breast height (DBH, cm) and tree height (m), respectively, for 10 poplar stands with different densities in northern Jiangsu Province. The solid line represents the fitting line and the diagonal line represents the 1:1 line.  
The coefficient of determination between the simulated and estimated aboveground biomass in the modified version (R\({}_{W,L,N}\)2 = 0.54) was better than in the original model (R\({}_{\mathrm{original}}\)2 = 0.48, Figure 2). For the modified model, the slope was 0.8 and the intercept was 16.6 t ha\({}^{-1}\) (Figure 2b). The MAE and NRMSE for the modified model were lower than these for the original model, and the Willmott index was higher than for the original model (Table 3).
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 2 System Model and Conventional Selection Schemes
### Threshold-Based Relay Selection Scheme  
The threshold-based relay selection scheme is one of the sub-optimal relay selection schemes. This scheme determines the threshold in order to select the relay. The threshold depends on how to define the threshold value [3]. There are many methods for these thresholds. The average SNR of the  
Figure 2: Multi-hop system model.  
received signal or the average of the channel magnitude of the source and relay are frequently used as the threshold. The threshold-based relay selection scheme selects the relay that has higher value than the threshold. The average of the channel magnitude method in this paper is as follows,  
\[Threshold(T)=\frac{\left|H_{S,R_{1}}\right|^{2}+\left|H_{S,R_{2}}\right|^{2}+ \cdots+\left|H_{S,R_{K}}\right|^{2}}{K}, \tag{1}\]  
\[SR_{r}=\left(\left[\left|H_{S,R_{1}}\right|^{2},\left|H_{S,R_{2}}\right|^{2}, \cdots,\left|H_{S,R_{K}}\right|^{2}\right]>T\right), \tag{2}\]  
where the \(K\) is a total relay index and the \(\left|H_{S,R_{K}}\right|^{2}\) is a channel magnitude. The cooperative communication is performed through the selected relay (\(SR_{r}\)). In the threshold-based relay selection scheme, the performance of the cooperative communication is degraded since the threshold only considers the source-relay channel, not all CSI. However, since only channel information between the source and relay is needed, this scheme has a low-complexity.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Initiator of Communication
#### 3.4.1 Source  
This type of routing protocol sets up the routing paths following the request of sensor nodes, which can need a service from the sink or to send their data. Thus, communication starts from nodes to sink.
## 2 Materials and Methods
### Measurement and Calculation of Kinematics Parameters
#### 2.2.2 Movement Tracking of Butterfly Flight  
We characterized and analyzed the motion and postures of the experimental butterfly flying horizontally in the cube container. The forward flight might be defined as translation horizontally in space. Because of the difficulty of recording the specific forward flight of a butterfly in the laboratory experiment, we redefined the forward flight such that the ratio between the vertical and horizontal translation of the COG is smaller than 0.15, \(|dy/dx|\)\(<\) 0.15, within a cycle. The wings and body movements were recorded with a high-speed camera and operated at the frame rate of 1000 fps with pixel size 1280 \(\times\) 1024. To reduce the calculation amount of image processing for flight trajectory, the collected video is framed with pixel size 500 \(\times\) 900 so that the calculation amount is reduced by about 50% and the processing time is reduced by more than half. The image-based feature point recognition method is utilized to track and analyze the motion.  
The four feature points for the observation records of the spatial displacement are shown as purple thoracic COM \((x_{t},y_{t})\), white joint point \((x_{j},y_{j})\), blue abdominal end \((x_{a},y_{a})\), and red wingtip \((x_{wt},y_{wt})\) in Figure 1c. The thorax axis and the abdomen axis are shown as solid green and light blue lines, respectively. Since the wing root is very close to the thoracic COM, the wing root and the thoracic COM are considered to coincide during the measurement. The joint angles of the thorax and abdomen during the free flight are observed. \(\theta_{p}\) and \(\theta_{a}\) are obtained from the geometric relationship, and numerical values of the feature points coordinates are derived as in Equations (1) and (2)  
\[\theta_{p}=\arctan\left(\frac{y_{t}-y_{j}}{|x_{t}-x_{j}|}\right). \tag{1}\]  
\[\theta_{a}=\arctan\left(\frac{y_{a}-y_{j}}{|x_{a}-x_{j}|}\right). \tag{2}\]The finite difference algorithm adapts to calculate velocity and acceleration in the \(X\)-axis directions (the same with the \(Y\)-axis directions) is given by  
\[v_{x_{i}}=\frac{x_{i+1}-x_{i-1}}{2 dt}. \tag{3}\]  
\[a_{x_{i}}=\frac{2x_{i+2}-x_{i+1}-2x_{i}-x_{i-1}+2x_{i-2}}{7 dt^{2}}. \tag{4}\]  
where \(v_{x_{i}}\) and \(a_{x_{i}}\) are the velocity and acceleration of the feature points in the \(X\) direction at frame \(i\), respectively. \(x_{i}\) is the position coordinate value of the feature point on the \(i\)th frame image. \(dt\) is the time interval between adjacent frames of the video. Then, the derived velocity and acceleration are low-pass filtered to remove noise. In the experiment, the instantaneous wingtip velocity was calculated as follows:\(V_{tip}=\sqrt{v_{x}^{wt^{2}}+v_{y}^{wt^{2}}}\), where \(v_{x}^{wt}\) and \(v_{y}^{wt}\) represent the velocity of the wing tip in the \(X\) and \(Y\) direction, respectively.
# Public Perception of the Introduction of Autonomous Vehicles
## 4 Discussion
### Qualitative Data Analysis
Respondents are worried about the potential for the vehicle's system to be hacked, leading to loss of control or disruptions in driving. * Safety and decision making: safety concerns extend to AVs making critical decisions when multiple factors are involved. There is a call for clear priorities and guidelines in such situations. * Infrastructure and technical challenges: respondents mention worries about the readiness of road infrastructure to support AVs, the reliability of satellite communication in some areas, and overall infrastructure inefficiency. * Moral and ethical risks: some respondents express concerns about the ethical implications of AV accidents, questioning who would bear responsibility in the event of an accident. * Reduced human control: concerns are raised about AVs not being controlled by humans and the implications of this for safety and decision making. * Vehicle and system failures: risks related to technical problems, software errors, and manufacturing defects in AVs are identified as potential hazards. * Implications for employment: some respondents are worried about job losses due to the increased use of AVs and the reduction in the number of human drivers. In summary, the public's perspective on the risks of transitioning to AVs highlights concerns about system malfunctions, human complacency, increased vehicle numbers, hacking and cybersecurity, safety and decision making by AI, infrastructure challenges, moral and ethical risks, reduced human control, vehicle and system failures, and potential job losses. These concerns reflect a wide array of considerations regarding the adoption of autonomous vehicles.
## 3 Results and Discussion
### Drying Performance of Inflatable Solar Dryer Compared to Conventional Sun Drying  
The drying curve of paddy rice inside the ISD and for conventional sun drying in each batch is presented in Figure 8. The final mass of the material dried in the ISD in batch 1, 2 and 3 was 581.5, 937.0 and 896.0 kg, respectively. In batch 1, the moisture content of fresh paddy rice (15.6% w.b.) was barely reduced during drying in the ISD and under the sun. The reason might have been clouds in the sky, which occurred predominantly on that day. An increase of the moisture content of the paddy rice dried under the sun was due to short rains, which happened suddenly, and therefore the paddy rice was not immediately covered by a tarpaulin. On the other hand, the ISD proofed to be effective in protecting the paddy rice from rainwater.  
After about eight hours of drying, it can be observed in batch 2 and 3 that the moisture content decreased considerably in both paddy rice dried in the ISD and under the sun. However, the final moisture content of paddy rice dried in the ISD was significantly higher than the one under the sun in batch 2 and 3 at \(p\leq 0.05\).  
In the case of solar drying, where a simultaneous mass and heat transfer occurs on a defined amount of products, the thermal energy supply is not flexible because it is strongly dependent on solar radiation [19; 33]. Therefore, drying efficiency of the ISD was often hard to predict, since it was strongly related to the climatic conditions and the loading capacity [18; 34].
## 3 System Model
### Simulations Results
#### 4.3.3 Temperature Variation versus Simulation Time  
Temperature variation indicates the change in temperature (\(\Delta\) temperature) rise over different simulation times. The graph is given in Figure 7 represents \(\Delta\) temperature as the simulation time increases. Initially, the value of \(\Delta\) temperature for ATEAR, MRRP, SHR, TARA and Enhanced ATEAR increased by 1.54 \({}^{\circ}\)C, 3.9 \({}^{\circ}\)C, 2.4 \({}^{\circ}\)C, 3.97 \({}^{\circ}\)C and 1.25 \({}^{\circ}\)C respectively. It can be clearly noticed that because of balancing of temperature among the nodes, the change of temperature in ATEAR and Enhanced ATEAR was less than the MRRP, SHR, and TARA. Similarly, as the simulation time increased to 6000 s, the change in temperature observed in ATEAR, MRRP, SHR, TARA, and Enhanced ATEAR was 0.43 \({}^{\circ}\)C, 6.98 \({}^{\circ}\)C, 10.5 \({}^{\circ}\)C, 8.05 \({}^{\circ}\)C and 0.29 \({}^{\circ}\)C. The \(\Delta\) temperature for ATEAR was 0.43 \({}^{\circ}\)C, which means the average temperature of the network slightly changed after reaching the threshold value; \(\Delta\) temperature for Enhanced ATEAR was 0.001 \({}^{\circ}\)C because the temperature of a node decreases after some time when no data forwarding was taking place. The \(\Delta\) temperature in MRRP, SHR, TARA held a higher value because of thermal unawareness.
# Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques
## 6 Proposed Method
### Grey Wolf Optimization
Meanwhile, the PV array's output power similarly fluctuates once the outside atmosphere changes. To avoid the procedure dropping into an infinite sequence, it is essential to start again the procedure for tracking the power. Once the output power difference is fulfilled, the procedure is re-initialized, which is stated in Equation (21). \[\left|\frac{P_{real}-P_{m}}{P_{m}}\right|>\Delta P \tag{21}\]  
where the actual output power is stated as \(P_{real}\), the PV output power is represented as \(P_{m}\), and the threshold for the output power change is expressed as \(\Delta P\), which is fixed as 0.2. The duty cycle equation is written as (13):  
\[Y^{t+1}=Y^{t}_{p}-B^{t}\times\left|D^{t}\times Y^{t}_{p}-Y^{t}\right|\]0  
where \(D_{i}(k+1)=new\ duty\ cycle\ value\). The controller architecture designed in this PV system is given in \[Y^{t+1}=Y^{t}_{p}-B^{t}\times\left|D^{t}\times Y^{t}_{p}-Y^{t}\right|\]1 In Figure 5, \(I_{\alpha},\ I_{b},\ \text{and}\ I_{c}\) represent the current that occurs at three phase sources. \(V_{ISO}\) States the instantaneous shunt voltage at the quadrature axis. The error attained between the reference current and the actual current is fed into the BPSO-GWO controller to produce the direct axis current Id *. Then, the quadrature axis current \(\text{Iq}\) * is set as zero, because it is responsible for reactive power. The output current is then compared to the actual current supplied to the electrical grid. \[Y^{t+1}=Y^{t}_{p}-B^{t}\times\left|D^{t}\times Y^{t}_{p}-Y^{t}\right|\]2 Control circuit diagram of the proposed method. The aim of the controller, as shown in Figure 5, is to transfer all the active power produced by the PV system to the grid, and also to produce a nil amount of reactive power so that unity power factor is obtained, except when the grid operator requires reactive power. The error attained between the reference current and actual current is fed into the BPSO-GWO controller to produce direct axis current I d *. Then, the quadrature axis current, I q *, is set to be zero because it is responsible for reactive power. The output current is then compared to the actual current supplied to the electrical grid. The error difference between the two signals is connected to the current control, which produces the gate signal. The same process is evaluated for the voltage control loop also. Then, the phase locked loop (PLL) is used to synchronize the three-phase voltage with the current. From the calculated voltage and current, switching pulses are created which are used to trigger the switches present in the multilevel inverter. The derivation of the fitness function for the proposed BPSO-GWO method is outlined in this section. Thus, the fitness function of the proposed algorithm is formulated as Equation (23). \[Y^{t+1}=Y^{t}_{p}-B^{t}\times\left|D^{t}\times Y^{t}_{p}-Y^{t}\right|\]3  
where \(P\) represents the power, \(d\) is the duty cycle, \(i\) is the number of current grey wolves, and \(k\) is the number of iterations.
# SITAEI HC1 Low-Current Hollow Cathode
## HC1 Description
### HC1 Emitter  
The emitter is the active cathode element providing the required electrons by means of the thermionic emission mechanism. A dispenser S-type emitter was selected as the most commonly used emitter in electric space thrusters [6]. The S-type emitter is made of a porous tungsten matrix impregnated with a barium-calcium-aluminate mixture (BaO/CaO/Al\({}_{2}\)O\({}_{3}\) with a 4:1:1 molar ratio). Thermionic dispenser cathodes require an activation process to force the emitting compound to diffuse toward the inner surface of the emitter, which is performed before the very first ignition and after each atmospheric exposure to restart the cathode. The HC1 emitter is shaped as a hollow cylinder, the dimensions of which were selected with the aid of the numerical model following the design criteria described in Section 2.1, according to the requirements in terms of the discharge current and mass flow rate listed in Table 1. The emitter thickness was selected to ensure a long operation for versatile applications on multiple missions. The emitter lifetime was computed with the aid of a semiempirical formula relating the impregnated evaporation to the operating temperature resulting from the thermal model [12]. According to the model results shown in Figure 2, the predicted emitter lifetime at a surface temperature of 1270 K exceeds \(10^{6}\) h, which is well beyond the typical requirements of low-current hollow cathodes (up to \(10^{4}\) h).
## 4 UAN Classes
### So UANs  
SO UANs constitute a very recent M2M solution gaining momentum in the urban scenario. In this type of UAN, SOs, which constitute a new category of cellular network operators, deploy their own base station infrastructure dedicated to sensor node connectivity. In contrast with traditional mobile networks, which were originally designed for voice applications and have evolved towards broadband services, SO networks are optimized for low throughput and low energy applications. Examples of SOs include SIGFOX and LoRa [36,37].  
In SO UANs, sensor nodes communicate with base stations or gateways by means of a backhaul that consists of a very low bandwidth (in the range between tens and thousands of bit/s), long-range link (up to tens of kms), generally using a sub-GHz ISM band. This approach allows to cover a million-inhabitant city with a reduced number of base stations (e.g., in the order of three [36]). Base stations or gateways are connected by means of core networks to cloud servers which act as communication endpoints. The first deployments of SO UANs are being done without support for bidirectional communication. Whereas the protocols used in SO UANs are currently proprietary, a few SOs are pushing standardization efforts in organizations such as ETSI.  
As in the previous UAN classes, in SO UANs, the radio of the sensor nodes is duty-cycled, with a sleep mode power consumption generally in the order of a few microwatts, and an active state power consumption around one hundred milliwatts.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 3 Methodology
### Comparative Analysis of Selected Machine Learning Methods
#### 3.4.3 Autoencoder  
Autoencoder [44; 45] is a type of artificial neural network that is used to reconstruct input data. It can be used to detect anomalies because anomalies, as outlier observations, are more difficult to reconstruct than 'normal' data. The Autoencoder is trained on a dataset that contains mostly 'normal' observations. The networks learn to minimize the difference between the input data and the output (reconstruction) data. Once trained, the Autoencoder is used to reconstruct new data. For 'normal' data, the reconstruction error will be small because the network will have learned their representation. Observations for which the reconstruction error is large are considered anomalies. This means that the network will not have been able to reconstruct these data well, suggesting that they are different from the data on which it has been trained.  
In Figure 4, it can be observed that data points denoting anomalies have higher reconstruction errors than data points denoting 'normal' observations. This means that the Autoencoder had difficulty in accurately reconstructing these data, which is characteristic of anomalies.  
Figure 4: Anomaly detection using Autoencoder (own elaboration).  
Figure 3: Anomaly detection using Isolation Forest (own elaboration).
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
## 4 Results
### Pose Solving Practical Filling Experiments
Several experiments show that the camera calibration parameters obtained using this target are higher than those of the traditional checkerboard target; calculating the object's pose can be achieved with high accuracy. The system has a simple structure, fast speed, high precision and stability. The significance of the pose measurement method proposed in this paper is that the self-calibration of the camera can be realised without the help of external equipment, and then the high-precision pose data of the object to be measured can be obtained. The method in this paper can make the target for arbitrary installation, which further reduces the application requirements of the vision measurement method, simplifies the measurement steps while ensuring measurement accuracy, and broadens the application scope of monocular vision measurement. Conceptualisation, D.S., X.W. and Z.Z. ; methodology, Z.Z. and X.W. ; software, Z.Z. ; validation, D.S., X.W. and Z.Z. ; formal analysis, Z.Z. and X.W. ; investigation, Z.Z. ; resources, D.S. and X.W. ; data curation, Z.Z. ; writing--original draft preparation, Z.Z. ; writing--review and editing, X.W. and P.Z. ; visualisation, Z.Z. ; supervision, D.S., X.W. and P.Z. ; project administration, D.S. ; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript. This work is supported by the Key R&D Plan of Shandong Province (Major Science and Technology Innovation Project) No. 2023 CXGC010701; in part by the Project of 20 Policies of Facilitate Scientific Research in Jinan Colleges No. 2019 GXRC063; in part by the Project of Shandong Provincial Major Scientific and Technological Innovation, grant No. 2019 JZZY010444, No. 2019 TSLH0315; and in part by the Natural Science Foundation of Shandong Province, grant No. ZR2020 MF138. Data available on request due to privacy. The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.
## 3 Effects of Storage and Maintenance Modes on Battery Performance
### Effects of Charge-Discharge Modes on Battery Performance
#### 3.1.2 Effects of Charge-Discharge Modes on Battery Power  
With a present SOC of 80%, 50%, and 20%, the effects of different charge-discharge maintenance modes on battery charge-discharge power were evaluated. The results are shown in Figures 6-8. It can be seen that the _FDC_ maintenance mode presented itself as the best approach to maintain the power of Ni-MH battery modules.