esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
(**b**)Transmission spectrum with \(d_{2}\) = 2020 nm. (**c**) Transmission spectrum  
with \(d_{2}\) = 2070 nm. (**d**) Transmission spectrum with \(d_{2}\) = 2120 nm. (**e**) Transmission spectrum with \(d_{2}\) = 2170 nm. (**f**) Transmission spectrum with \(d_{2}\) = 2220 nm. The transmission spectra and resonance wavelengths under different \(W_{1}\) and \(W_{2}\) are shown in Figure 4, and other parameters are the same as Figure 1. Distinctly, with the increase of \(W_{1}\) from 85 to 145 nm or the increases of \(W_{2}\) from 180 to 240 nm, both \(\lambda_{1}\) and \(\lambda_{2}\) appeared red shifted. For a clearer observation, we show two resonance wavelengths under different \(W_{1}\) and \(W_{2}\) in Figure 4c,d. When \(W_{1}\) increased from 85 to 145 nm, \(\lambda_{1}\) moved from 1547.33 to 1549.93 nm and \(\lambda_{2}\) moved from 1552.16 to 1558.13 nm. When \(W_{2}\) increased from 180 to 240 nm, \(\lambda_{1}\) moved from 1545.44 to 1549.2 nm and \(\lambda_{2}\) moved from 1549.41 to 1554.54 nm. Thus we could easily control the two resonance wavelengths by adjusting the parameters of \(W_{1}\) or \(W_{2}\). The reason for the red shift of the two resonance wavelengths is that as \(W_{1}\) or \(W_{2}\) increase, the fill factor increases, which changes the average refractive index of the grating layer [28]. The gap between the gratings G\({}_{11}\) and G\({}_{12}\) (gratings G\({}_{21}\) and G\({}_{22}\)) is also an important parameter, being defined as \"\(g\)\". The transmission spectrums under different \(g\) are shown in Figure 5 and other parameters are the same as Figure 1. Obviously, as \(g\) varied from 10 to 50 nm in steps of 10 nm, the resonance wavelength of EIT\({}_{1}\) (\(\lambda_{1}\)) appeared blue shifted, while the resonance wavelength of EIT\({}_{2}\) (\(\lambda_{2}\)) appeared red shifted. Two resonance wavelengths and two resonance wavelengths difference (\(\lambda_{2}\)-\(\lambda_{1}\)) when \(g\) changed from 10 to 50 nm are shown in Figure 5f. As \(g\) increased, \(\lambda_{1}\) moved from 1549.93 to 1547.19 nm and \(\lambda_{2}\) moved from 1552.55 to 1553.93 nm (as shown on the left vertical axis in Figure 5f),  
Figure 4: (**a**) The transmission spectra under different \(W_{1}\) and \(W_{1}\) is equal to 85 nm, 100 nm, 115 nm, 130 nm and 145 nm, respectively. (**b**) The transmission spectra under different \(W_{2}\) and \(W_{2}\) is equal to 180 nm, 195 nm, 210 nm, 225 nm and 240 nm, respectively. (**c**) Resonance wavelengths of two EIT peaks at different \(W_{1}\). (**d**) Resonance wavelengths of two EIT peaks at different \(W_{2}\). and \"\(\lambda_{2}\)-\(\lambda_{1}\)\" increased from 2.62 to 6.74 nm (as shown in the right vertical axis in Figure 5f). The resonance wavelength of the two EIT peaks could also be flexibly adjusted by adjusting the parameter \(g\).
# Production of Photovoltaic Electricity at Different Sites in Algeria
## 2 Materials and Methods  
### Presentation of Study Sites  
In order to design a photovoltaic installation capable of providing significant electricity production, we studied five sites in Algeria with different geographical and climatic coordinates, namely: Algiers, Batna, Sidi Bel Abbes, Ouargla and Bechar.  
The geographical and temperature data of the five selected sites are presented in Tables 1 and 2.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods  
### Study Area  
This study focuses on Henan Province, a primary winter wheat cultivation area in China, located between 31 deg23' to 36 deg22' N and 110 deg21' to 116 deg39' E (Figure 2). The region encompasses diverse land-use types, ranked in descending order by proportion: arable land, forest land, built-up land, water bodies, grassland, and unused land [35]. The climate is characterized as subtropical and temperate monsoon, featuring distinct seasonal variations, with an average annual temperature ranging from 12 to 16 degC and annual precipitation between 500 and 900 mm [36]. In the study area, winter wheat is typically sown in October and harvested from late May to early June of the following year [37]. Precipitation in both spring and winter benefits winter wheat growth and other early spring crops. However, natural precipitation alone is insufficient to meet the growth requirements of winter wheat, prompting local farmers to adopt groundwater extraction for irrigation as an additional water source [38].  
Figure 1: Workflow of winter wheat yield prediction.  
Figure 2: Distribution map of land-use types in Henan Province in 2021.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 4 Twin System Modeling and LSTM-DQN-APF Algorithm
### Algorithm Validation Test and Analysis
#### 4.3.3 Experiment 2: Verify the Accuracy of LSTM-DQN-APF Prediction
The figure clearly indicates that the trajectory predicted by the LSTM-DQN-APF model most closely aligns with the actual trajectory, exhibiting the minimum error and the highest accuracy.
# Design of a Digital Twin Training Centre for an Industrial Robot Arm
## 4 KUKA KR5 Robot Unit Re-Creation and Control  
Previously the creation and structure of the HMI was shown in detail, however, movement of the KUKA KR5 requires forward kinematics-inverse kinematics as well. Forward and Inverse kinematics are extremely important in our case, because these guarantee that we know the position of the object at any given time. Furthermore, the base coordinates of the TCP (Tool Center Point) also become known, meaning that based on angle data of the axes, we can derive the TCP's position in the three-dimensional space. Forward (direct) kinematics gives us information about the TCP's position based on the current angle of the axes. For this, knowledge of the KUKA KR5's physical parameters, such as number of axes, axis limits, work area, and physical size, are required. Direct kinematics problems may be solved by using the Denavit-Hartenberg (DH) matrix. The matrix itself is a simple link-joint description method, usable with any robot arm configuration, independent of complexity. Every joint is defined iteratively based on a transformation from the previous joint. Let us select a joint with the name n. Following this, both n, and n + 1 joint get a local reference frame. The local frame of reference is described by the axis z, which can be translational or rotational (shown in Figure 11). Inverse kinematics, unlike direct kinematics, is a method that requires more complex calculation methods. In this case, the position is known, and we are looking for the appropriate joint parameters [53]. Figure 11: Designed virtual user interface. Symbols used in the DH table have predefined meanings, which are as follows: 0: Rotation along Z axis; d: Movement along Z axis (distance); a: Joint offset (length of normal); \(\alpha\): Angle difference of two consecutive joints (joint twist) [52]. It is important to note that the robot's geometric model describes its spatial position. The kinematics model meanwhile describes problems regarding velocities. For this, the Jacobi-matrix must be used, where joint speeds and end-effector speeds are differentiated. Joint speeds are: - Rotational joint (angular velocity around a rotational axis) - Translational joint (linear velocity around the axis of movement) End-Effector speeds are: - Angular velocities around \(x,y,z\) - Velocities along \(x,y,z\)[54].
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Integrating PSO and HE for Image Segmentation  
This detailed exploration unravels the intricacies of seamlessly integrating particle swarm optimization (PSO) with histogram equalization (HE) to elevate image segmentation performance. This integration's primary aim is to optimize HE parameters dynamically through the adaptive capabilities of PSO and to foster an intricate interplay between algorithmic components.
## 3 Results and Discussion
### Entrance Section  
#### 3.1.1 Friction Factor  
In our previous study [10], it was shown that different diffusion coefficients can have a large influence on the development length of the SID profile, but not on the fully developed SID profile. It is expected that the friction factor that is used for both the drag force and the SID force has similar effects; therefore, this was tested first. In our model, we use the function: \(f\big{(}\alpha_{p}\big{)}=\big{(}1-\alpha_{p}\big{)}^{2}\big{(}1-\widetilde{ \alpha_{p}}\big{)}^{2}\) for the SID force (based on [PERSON] et al. [14]), and: \(f_{ij}^{D}=\big{(}1-\alpha_{p}\big{)}^{n}\) for the drag force (standard in STAR-CCM+), and we investigated the sensitivity of the model for the functions that are used.  
Figure 5 shows that a difference in friction factor does not influence the fully developed profile, but it does change the length needed to reach this profile. This was also observed by [PERSON] et al. who changed the exponents in their function for \(f\big{(}\alpha_{p}\big{)}\)[16]. In Figure 5, it can be seen that the position at which the particle volume fraction reaches its' equilibrium value is much shorter when using \(f\big{(}\alpha_{p}\big{)}\) as compared to \(f_{ij}^{D}\), and that is even more strongly so when used in conjunction with the SID force that as expected has a bigger influence on the development profile when compared to the drag force.  
Figure 5: (**a**) Particle volume fraction versus the channel height and (**b**) the particle volume fraction in the center of the channel versus the channel length for different friction factors for both the drag and Shear induced diffusion (SID) force: \(f\big{(}\alpha_{p}\big{)}\) for SID and \(f_{ij}^{D}\) for drag (black solid line); \(f\big{(}\alpha_{p}\big{)}\) for both SID and drag (grey solid line); \(f_{ij}^{D}\) for both SID and drag (black dashed line); and, \(f_{ij}^{D}\) for SID and \(f\big{(}\alpha_{p}\big{)}\) for drag (grey dashed line)  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Particle Diameter (\(\mu\)m)**} & **Grid Cell Number** \\ \hline Channel height & 50 \(\mu\)m & 2 & 23 \\ Channel height & 100 \(\mu\)m & 2 & 47 \\ Channel height & 100 \(\mu\)m & 4 & 23 \\ Pore length & 20 \(\mu\)m & 2 & 9 \\ \hline \end{tabular}
\end{table}
Table 3: Overview of the different dimensions used in the parameter studies.  
The choice of the friction coefficient is thus only of influence on the development length, but as [PERSON] and coworkers already mentioned that there are many different expressions for the friction factor in literature [14]. For the process that we eventually want to design, it is important to compare various situations, and we express our results in Section 3.3 relative to the entrance length.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 2 Related Work  
The state-of-the-art in automated aquaculture systems is somehow limited to the last years, when the capabilities of IoT systems and the increasing power of AI have allowed the creation of systems capable of monitoring, predicting states, and controlling the whole processes described in the cycle between the fishes and the plants.  
In [6], the authors developed a system to monitor water quality, through Potential of Hydrogen (pH), sending the information with GSM in an Arduino-based architecture. Also in [7], the authors focused their efforts on the development of a low-cost, low-power, wireless sensor network and Arduino-based IoT system for groundwater monitoring. This system used LoRa for communications.  
In [8], authors oriented their efforts to develop a system for water quality monitoring in remote locations (with total dissolved solids, electrical conductivity (EC), pH, and temperature sensors with LoRa-enabled communications and GPS). They also used LoRa for localization purposes and performed consumption measurements of their system.  
In [9], the authors developed an interdigital FR4-based capacitive sensor, together with the whole IoT Arduino-based system (with LoRa and WiFi communications) to monitor nitrate concentration in water-monitoring studies.  
In [10], the authors show an IoT system similar to the purpose of this project, applying the architecture for aquaculture purposes. This work shows a number of parameters lower than in our application, and their connectivity is based on WiFi (with ESP8266).  
Additionally, the on-going H2020 project FutureEUAqua [11] is developing tools oriented to monitor, control, and manage the conventional aquaculture of major fish farming industries, promoting innovations in different productive areas. This project has a monitoring part, with wireless sensors in the fish farm, but the number of sensors is limited to dissolved oxygen (DO), salinity, chlorophyll, blue-green algae, and turbidity (based on real-time monitoring devices by InnovaSEA company [12]).
## 3 Comparison of Backbone-like and Mesh-like Clustering Strategies
### Modeling Parameters and Scenario Definition
#### 3.2.3 Application Traffic Models  
Three applications were modeled in order to evaluate both protocols, which were chosen to allow representing any kind of vehicular application traffic.  
Bidirectional Videoconference Application--A\({}_{Vd}\)BI  
Using the OPNET application definition module, a videoconference application using UDP was deployed between two nodes for which the distance in IP hops is known and constant when the application is active. The application initiator node first sends a control packet to the destination, which replies with another control packet. Upon reception, both nodes exchange a stream of application packets of constant format during 50 s and explicitly close the transmission with a control packet. Application packets are 6000 bytes long and are transmitted at the rate of 10 packets per second. Such application can be used to model any vehicular application that involves an explicit start of exchanges, and where the two nodes send each other the same amount of data subject to delivery rate, end-to-end delay, and jitter constraints. It could be used in a platooning application in which each vehicle gives access to what it senses through its camera and microphone sensors to the other members of the platoon.  
Monodirectional Videoconference Application--A\({}_{Vd}\)MO  
The videoconference application is now monodirectional: a single node sends a stream of application packets to the other node. In order to maintain the same level of traffic load, the stream carries twice as many bytes in application packets (12,000 bytes long). For exactly the same distance in IP hops and road scenario, the source and destination nodes are the same as those chosen for the A\({}_{Vd}\)BI application, as are the application start time and duration. Such application can be used to model an application in which a remotely controlled vehicle sends live data from its camera and microphone sensors to a controller vehicle.  
Monodirectional Packet Stream Application--A\({}_{Si}\)MO  
Using the OPNET application demand module, a stream of packets is specified between two nodes, for which transmission requires no control packets before it starts and after it stops. For exactly the same distance in IP hops and road scenario, the source and destination nodes are the same as previously chosen. The packet size and frequency are defined to match those of the monodirectional videoconference application (Table 3). Such application allows performing the same kind of comparisons as with previous applications, except that the delivery rate, end-to-end delay, and jitter constraints do not apply. It can be used to model any vehicular application in which an amount of data is sent freely in a best effort by any vehicle to another.  
Figure 4: Number of nodes in the road section during simulation for medium (S2) and high (S3) node density scenarios.
Finally, [27] details a compact frequency scanning antenna array that offers enhanced sector scanning from 73\({}^{\circ}\) to 132\({}^{\circ}\) and gains between 15.5 and 17 dBi, suitable for radar and communication technologies. In addition to reducing beam misalignment losses, circularly polarized (CP) antennas also help to mitigate polarization losses between the transmitting and receiving antennas [28], enhancing overall system performance. In this paper, we propose a series-fed and beam-tilted CP antenna design intended for efficient wireless power reception on airborne UAVs. The primary focus lies in tilting the main lobe of the proposed antenna, a feature influenced by both the curvature of the UAV and the attachment location of the receiving antennas. This beam tilt of the main lobe facilitates easy alignment between the main lobe direction of the receiving array antenna and that of the ground-based transmitting array antenna. The radiating element of the proposed series-fed antennas is a patch with truncated corners, which enables CP radiation. U-shaped bend transmission lines are employed to connect and feed each radiating patch in a series. The length of the U-shaped bend transmission line, which controls the phase difference between the adjacent radiating patches, determines the beam-tilted angle of the proposed antennas. A coaxial connector feeds the antennas and does not require a complex feeding network. Broadside and beam-tilted antennas with tilt angles of \(15^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\) were each designed, fabricated, and measured. The center frequency and highest gain frequency of the transmitting array antenna are 5.84 GHz. Therefore, the proposed receiving array antennas are also designed to operate at a central frequency of 5.84 GHz. Simulations were performed using CST Studio Suite. The organization of the paper is as follows: Section 2 provides a description of the unit cell and the proposed antennas, as well as introducing an impedance-matching network and tilted beam. Section 3 compares the measured results of the proposed antennas with the simulated results. Finally, Section 4 presents the conclusions of this study.
## 4 Performance Analysis
### Results and Discussion
#### 4.3.3 Ablation Experiments  
Based on two NLP tasks of the NER and the RC, the ablation experiments pay attention to the contribution of MKR, WI, WS, WD in the MDA. The Ra-RC model on the BITEmrNER dataset in the NER task and the RoBERTa + CasRel on the BITEmrRC dataset in the RC task are selected as models for the ablation experiments. In addition, the experiments successively remove each of the four methods to obtain the effect of each method. Hence, it can be seen from Table 7 that if the MKR method in the MDA is removed, F1 score significantly reduces in two tasks by 2.16% and 2.36%, respectively. Therefore, the results of F1-score verify that the MKR method can effectively augment the text data by utilizing the medical knowledge graph to replace the keywords. However, after removing the methods of WI, WS, and WD, the F1-scores show that the methods have little influence on the performance of the models. Thus, the improvement of the three methods can be explained by abandoning the semantic information of the keywords. In the end, the results of the F1-score are improved by using the data augmentation of MDA, which indicates that the MDA can significantly improve the performance of the deep learning models in NLP tasks.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 6 Experimental Results for a Maritime Surveillance Application
However, the DEC-MF-2/4 only yields a slight loss that does not prevent the possibility to track all the vessels navigating in the considered sea area. Moreover the decentralized approach (see Figure 11b) provides the additional capability of suppressing the ghost target tracks and the unwanted plots formations that are present in Figure 11a when operating with the centralized approach. Such sequences of plots are usually yield by unremoved sidelobes of the signal AF in the presence of strong target returns. In addition they can arise from a frequency reuse by different transmitters in neighboring areas, there including the emitters of a single frequency network (SFN) transmission mode. When multiple frequency channels are adopted, the undesired structures might appear at different locations of the resulting CAF maps. This is typically the case if the DVB-T channels employed exploit a different transmission mode and/or different modulation parameters [9]. Moreover it is unlikely that different channels are affected by the same interfering transmitter. Incidentally we observe that these aspects can be fruitfully exploited when selecting the DVB-T channels to be simultaneously employed in order to enhance the diversity also in term of AF characteristics and interfering scenario severity. Apparently, when integrating the results at multiple frequency channels, provided that a certain degree of diversity can be guaranteed, possible unwanted outcomes will be filtered out regardless of the exploited MF approach. Obviously, a decentralized scheme could be much more effective even operating with few channels thanks to the two stages thresholding approach that does not retain the dynamic range of the single channels. In contrast, for a similar result to be obtained with a centralized scheme, the number of channels to be integrated should properly increase and this could represent a limitation in practical cases. Figure 11: Detection results over the range-velocity plane for the best MF configurations: (**a**) CEN-MF-3; (**b**) DEC-MF-2/4 with \(W=3\).
# Simulation Analysis and Experimental Verification of the
## 4 Analysis
### Locking Force Analysis  
In order to measure the locking force more conveniently, pressure sensors were added between the lead screws and the MP. The locations of the sensors are shown in Figure 8. Since these sensors themselves have a certain mass and volume, adding the sensors is equivalent to changing the original structure connection. In order to understand and analyze the transformation of the locking force of the MP during the locking process more clearly, the relationship between the output torque of the motor and the locking force of the MP was obtained, and the consistency of the locking force with and without the sensor was verified under the same torque condition. [PERSON] was used to carry out the rigid-flexible coupling analysis of this process.  
The output torque value of the left motor and the locking force at the position 8 shown in Figure 8 were taken as the analysis objects, and the sensor was set as the flexible body to analyze the force relationship between the L/R mechanism and the MP. The comparison diagram of the time history curve of motor torque and locking force under the conditions of sensor addition and no sensor addition was obtained, respectively, as shown in Figures 9 and 10. It can be seen from the figure that the value of the locking force reaches about 2000 N at 180 s, meeting the preset value of the locking force obtained by frequency response analysis. At this time, the motor locking torque is 21.55 Nm. From the Figure 11, the error value of the locking force is within \(\pm\)1.9% in the two states. It can be judged that  
Figure 8: The position of the pressure sensor.  
Figure 7: Stress nephogram of the main structure.  
it has little influence on the relationship between the locking force and the locking moment whether sensors are added or not. In other words, the numerical value measured by the sensor torque can be used to simulate the situation without the sensor.