esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Use of a Shared System Interface:
## 6 Discussion
### Limitations  
The first limitation of the present study resides in the fact that the measurement model was assessed through the SOGC dimension. Future research may focus on the operationalization of mutual gaze convergence and investigate its nomological network. For example, it may be examined how mutual gaze convergence is related to interpersonal processes during the collaborative use of a shared system interface. Secondly, we limited our study to dyads of users for simplicity of all aspects of the research. As mentioned earlier, the present work could be used to examine groups of three or more persons. Thirdly, this study does not investigate qualitative data related to dyad behaviors during the experimental task. Such data may provide more insights about mechanisms though which dyad members' gazes converge. For example, qualitative data may clarify how the decision processes during the dyad task (e.g., jointly deciding where to investigate on the screen) may have influenced convergence of dyad members' gazes. Fourthly, because of the exploratory nature of the present study, a small sample size was used. Nevertheless, as mentioned earlier, our very significant results are promising. Finally, this study does not control for gender and dyad familiarity, that is, whether dyad members knew each other at the time of the study. Indeed, literature suggests that group familiarity may influence group collaboration and team performance (e.g., [PERSON] et al. [66]; [PERSON] et al. [67]) and that males and females may display different gaze patterns (e.g., [PERSON] [68]). Nevertheless, related risk was mitigated: participants were recruited independently from each other, and dyad were formed randomly; moreover, to analyze data related to the collaborative task, we used linear regression with random intercept model, which accounts for random effects. Notwithstanding, the role of dyad familiarity in the present context of collaborative use of a shared system interface is worth investigating in future research.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 3 Results
### Comparison with the COSMIC Constellation  
The performance of the optimal 2D-LFC and 3D-LFC configurations composed of six satellites are further compared with the existing RO mission, COSMIC. Table 7 presents the detailed information about the number of 24 h simulated ROEs, the corresponding fitness values, and the mean 3 h-COVs of the two optimized LEO constellations as well as COSMIC. It can be seen that both of the two optimized configurations can observe more ROEs than COSMIC, and the number of ROEs observed by the optimal 2D-LFC is the largest among the three constellations. The fitness values of the two optimal constellations are both larger than that of COSMIC, while the mean 3 h-COV values of the two optimal constellations are both lower. This indicates that the spatial and temporal distributions of the ROEs observed by the two optimal configurations are more uniform than the ROEs observed by COSMIC. In particular, it should be noted that the performance of the optimized 2D-LFC is the best among the three LEO constellations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Satellites**} & \multicolumn{2}{c}{**2D-LFC**} & \multicolumn{2}{c}{**3D-LFC**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** \\ \hline
6 & 0.54143 & 0.015 & 0.56178 & 0.032 \\
7 & 0.53720 & 0.012 & 0.54028 & 0.017 \\
8 & 0.54756 & 0.012 & 0.55244 & 0.010 \\
9 & 0.54228 & 0.014 & 0.53344 & 0.014 \\
10 & 0.53065 & 0.011 & 0.54203 & 0.019 \\
11 & 0.54780 & 0.012 & 0.53644 & 0.018 \\
12 & 0.54112 & 0.014 & 0.54328 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The mean and the standard deviations of the COV values for 3 h-distributions of radio occultation events (ROEs) during one day for the optimal 2D-LFCs and 3D-LFCs composed of 6–12 satellites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **COSMIC** & **2D-LFC** & **3D-LFC** \\ \hline No. of 24 h ROEs & 10,340 & 12,386 & 12,250 \\ Fitness value & 0.51080 & 0.68692 & 0.68191 \\ Mean 3 h- COV & 0.62081 & 0.54143 & 0.56178 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison of the performances of the optimized six-satellite 2D-LFC, 3D-LFC with COSMIC.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 3 Our Method
### Network Architecture  
In order to solve the interference caused by the motion of the infrared image background caused by the motion of the infrared detector, this paper proposes a multi-frame infrared small target detection network aligned at the feature map level. As shown in Figure 3, the proposed network consists of three parts: a feature extraction and alignment module, temporal feature fusion module, and decoding module.
## 3 Boundary Control Design
\begin{array}{cccccc}g_{1}^{1}(1)&0&0&0&\cdots&0\\ g_{1}^{1}(2)&g_{1}^{1}(1)&0&0&\cdots&0\\ \vdots&g_{1}^{2}(2)&g_{1}^{1}(1)&0&\cdots&0\\ g_{1}^{N_{\tau}}(N_{\tau})&\vdots&g_{1}^{2}(2)&g_{1}^{1}(1)&\cdots&0\\ g_{2}^{1}(1)&g_{1}^{N_{\tau}}(N_{\tau})&\vdots&g_{1}^{2}(2)&\cdots&0\\ g_{2}^{2}(2)&g_{2}^{1}(1)&g_{1}^{N_{\tau}}(N_{\tau})&\vdots&\cdots&0\\ \vdots&g_{2}^{2}(2)&g_{2}^{1}(1)&g_{1}^{N_{\tau}}(N_{\tau})&\ddots&0\\ g_{2}^{N_{\tau}}(N_{\tau})&\vdots&g_{2}^{2}(2)&g_{1}^{1}(1)&\cdots&\vdots\\ \vdots&g_{2}^{N_{\tau}}(N_{\tau})&\vdots&g_{2}^{2}(2)&\cdots&0\\ g_{N}^{1}(1)&\vdots&g_{2}^{N_{\tau}}(N_{\tau})&\vdots&\cdots&0\\ g_{N}^{2}(2)&g_{N}^{1}(1)&\vdots&g_{2}^{N_{\tau}}(N_{\tau})&\ddots&0\\ \vdots&g_{N}^{2}(2)&g_{N}^{1}(1)&\vdots&\cdots&\vdots\\ g_{N}^{N_{\tau}}(N_{\tau})&\ddots&g_{N}^{2}(2)&g_{N}^{1}(1)&\ddots&g_{1}^{1}(1) \end{array} \right] \tag{20}\]  
where  
\[g_{N}^{N_{\tau}}=C_{d}A_{d}{}^{N_{\tau}-1}B_{d} \tag{21}\]  
and \(A_{d}\), \(B_{d}\), and \(C_{d}\) are the discrete form of \(A_{s}\), \(B_{s1}\), and \(C_{s}\) respectively. **Remark 1**. : The main challenge of this study is shortened as follows:  
1. How to deal with uncertainties: System parameters uncertainties have been discussed commonly by \(H_{\infty}\)ILC for lumped parameters system (LPS). No study discusses the uncertainties for DPS through \(H_{\infty}\)ILC. 2. How to obtain the dynamic of a string system in the form of a state-space system: The approximated solution for string system has been addressed regularly by the FD method. To the authors knowledge, no studies achieve the dynamic of string system in the form of state-space by using MOL. 3. How to overcome the outer disturbances: It is a challenge to acquire stability under distributed and boundary disturbances. Previous studies in \(H_{\infty}\)ILC have overcome the external disturbances for LPS, but little consideration to achieve global convergence for string system under \(H_{\infty}\) framework. 4. How to propose the \(H_{\infty}\)ILC formula for DPS: The string system is a function in time, position, and iteration. The \(H_{\infty}\)ILC design is suggested to ensure the boundedness and robust stability of the close-loop string system in presence of the iteration parameter uncertainties, iteration-varying distributed disturbance and iteration boundary disturbance.
## 4 The Pilot Study
### Results
The level of control in the situation, which is associated with a feeling of calmness (Figure 3b), and the optimal level of involvement in the situation [38], which is associated with a feeling of engagement (Figure 3c), were higher in the businessman and casual conditions compared to the beggar conditions (main effect on calmness and engagement: F [2; 30] = 3.13; \(p<0.05\); \(\eta^{2}=\) =20; F [2; 30] = 3.43; \(p<0.05\); \(\eta^{2}=0.21\)). This was also true in the stereotypical and socially expected situations, when an ingroup member was dressed as a businessman and an outgroup member was dressed as a beggar (significant interaction effect on calmness and engagement: F [1; 38] = 3.80; \(p<0.05\); \(\eta^{2}=\)14; F [1; 38] = 3.34; \(p<0.05\); \(\eta^{2}=0.14\)); on alertness: F [1; 38] = 1.90; \(p<0.08\)). Figure 3: **(a)** Alertness. The y-axis represents the mean of alertness across the three conditions (confederate social appearance) reported on the x-axis, the color of the bars represents the variable confederate group type. Bar Errors +/\(-\)2 SE; **(b)** Calmness. The y-axis represents the mean of calmness across the three conditions (confederate social appearance) reported on the x-axis, the color of the bars represents the variable confederate group type. Bar Errors +/\(-\)2 SE; **(c)** The y-axis represents the mean of engagement across the three conditions (confederate social appearance) reported on the x-axis, the color of the bars represents the variable confederate group type. Bar Errors +/\(-\)2 SE. Regarding self-assessed empathy, an ANOVA with the two independent variables described above revealed that personal distress increased significantly with the type of helper/helpee (main effect on distress: F [2; 38] = 3.43; \(p<0.05\); \(\eta^{2}\) = 0.18), since it increased in the beggar condition compared to the casual and businessman conditions (Figure 4a). Moreover, distress was higher when participants interacted with an outgroup member (with dark skin) in the businessman condition and an ingroup member (with the same skin color) in beggar condition (i.e., counter-stereotypical conditions). In contrast, the level of empathy is generally higher when participants interact with a businessman, especially if the latter is an ingroup member (main effect on empathy: F [2; 38] = 3.29; \(p<0.05\); \(\eta^{2}=0.10\); Figure 4b).
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
## 2 Theoretical Background
### The Role of the RF Spectrum in Aviation  
The radio frequency spectrum is the foundation for aviation communication and navigation. The VHF band supports air-to-ground voice communication, enabling pilots and air traffic controllers to coordinate operations effectively. The Ultra High Frequency (UHF) band is used for functions such as radar, transponder communication, and (ILS). These systems ensure safe take-off, cruising, and landing, particularly in poor visibility or adverse weather conditions. RFI can couple into Aircraft systems through two primary mechanisms: front-door and back-door coupling. Front-door coupling refers to interference that enters through designed input ports, such as antennas or receiver terminals. This type is often filtered but still susceptible to strong nearby emissions. In contrast, back-door coupling occurs through unintended paths, including power cables, structural seams, or signal lines, where electromagnetic waves bypass front-end protection. Both coupling types can disrupt onboard avionics, especially if shielding or grounding measures are inadequate. RFI impacts aviation systems through several pathways:  
* Adjacent Channel Interference: Signals from neighboring frequency bands bleed into aviation channels, reducing signal clarity.
* Harmonic Interference: Harmonics from non-aviation frequencies can coincide with aviation bands, creating noise and disrupting system performance.
* Receiver Desensitization: Strong external signals can overwhelm receivers, making them less sensitive to desired signals.
* Multipath Propagation: Reflected signals from terrain, buildings, or other surfaces can create signal distortion and loss.
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 9 Conclusions and Perspectives  
As DEAs are emerging soft actuators, they are historically surveyed and some of the important research highlights are reviewed in this paper. The working principle and modeling of the actuator are described in the introductory sections. This review categorized and summarized various applications with DEAs as soft robot hands, locomotive robots, and wearable robots. Still, there are more potential application areas including cell manipulation in biology [142], micro-pumps and valves in micro-fluidic systems [143], active vibration isolation in mechanical structures [144], and light weight loudspeakers in acoustic devices [145; 146]. Beyond their application to actuators, dielectric elastomers can be used as capacitive material for soft pressure and strain sensors [147]. By integrating the functional characteristics of both actuators and sensors, the self-sensing dielectric elastomer is able to be designed [148]. The development of dielectric material, compliant electrodes, and fabrication technology will continuously open new and innovative application areas for DEAs.  
Although dielectric elastomer is a great candidate for artificial muscles, there are still challenges for the widespread use of DEAs. For example, a high driving voltage requires an additional power circuit [1]. This high voltage could be the most crucial issue, especially in wearable applications for safety, although it requires a very low current. As shown in Section 2, the output response of DEAs demonstrates a proportional relation with input voltage and an inverse proportional relation with their thickness. Therefore, we need to decrease the thickness of the DEA for maintaining an output under a lower driving voltage, and multiple stacking of thin films has been considered [7]. It is expected that performance of a multiple stacked actuator will be improved with the development of new fabrication methods. Low contraction force caused by material softness also needs to be improved. Contrary to the human muscle, DEAs only provide expansion force based on its working principle [1]. It usually contracts by material restoration force. Thus, there are limitations of use since there is a limitation of contraction force. New design approaches for generating high contraction force are required. Durability is also a significant issue due to the soft feature of the DEA. Since DAE is a kind of polymer, it has similar weakness to conventional polymers which are not resistive to sharp cutting, high pressure, UV (ultraviolet) light, heat and etc. Therefore, the durability of a DEA will take the same steps as new dielectric material development.
# Detection and Classification of Different Weapon Types Using Deep Learning
## 3 Materials and Methods
### CNN Model  
In object recognition applications, the most used deep learning algorithm is the CNN algorithm [32; 33]. This algorithm showed outstanding success in the ImageNet Large-Scale Visual Recognition Competition held in 2012 and has been used in the development of new models in many areas since then [21]. In the current study, a new model (Figure 2) is proposed based on the VGG-16 model (Figure 3) [21]. The model shown in Figure 2 consists of a total of 25 layers (convolution (\(n=7\)), pooling (\(n=4\)), dropout (\(n=4\)), rectified linear units (ReLU) (\(n=7\)), flatten (\(n=1\)), fully connected (\(n=1\)), and classification (\(n=1\)) and 337,671 parameters.  
Figure 1: Sample dataset of different classes: (**a**) assault rifles; (**b**) bazookas; (**c**) grenades; (**d**) hunting rifles; (**e**) knives; (**f**) pistols; (**g**) revolvers.  
Figure 2: Developed convolution neural network model (f: filter size, s: stride, p: padding, FC: fully connected).  
The number of layers when designing a model network and the parameter values used in these layers significantly affect the training time of the neural network, the cost of processing, and the choice of device to be applied. In this study, the model we proposed was preferred for the problem of weapon detection and recognition due to the small number of layers compared to the classic VGGNet model, the ability to train on low-cost computers, the low training time, and the high success accuracy.  
In the model presented in Figure 2, two convolution layers and a maximum pooling layer are applied to the input image in gray color format. In the pooling layer, the two-step 2 \(\times\) 2 filter matrix is shifted over the input matrix to create a new matrix value. Maximum pooling holds the new matrix value by taking the maximum value of each block to better estimate the image. In addition, the ReLU activation function is used in convolution layers. This is a non-linear function that replaces all negative pixel values with a zero value, which ensures that the network is more efficient and faster [34]. Using a 25% dropout layer after each pooling layer, memorization is prevented [35]. Convolution, pooling, and forgetting processes are repeated with different parameters and different filter channel values in later layers. Then, neurons are formed into an array by flattening and fully connecting layers. As a result of the straightening process, 2048 neurons are obtained by forming a fully connected layer. In the last layer of the model is the classification layer with the softmax activation function. In this layer, the output value is obtained by classifying the number of classes determined in the dataset. As a result of the classification, seven different weapon types take a value in the range of 0-1 and the weapon type with the highest value presents the estimated value.
## 1 Introduction
### Objectives and Hypotheses  
The main objective of this study is to test a set of simple process-oriented modifiers for plant water control on GPP, together with the other equations and algorithms in the _DIMONA_ PB model, for simulating peatland productivity. Another objective is to demonstrate the potential of and benefits from using the _DIMONA_ online platform for research and modelling purposes. In this study, the _DIMONA_ PB model is driven by meteorological forcing of I\({}_{\text{swr}}\), T\({}_{\text{air}}\), RH, v\({}_{\text{w}}\), and soil hydrology instead of precipitation. The related conceptual and modelling hypotheses are as follows:  
**Hypothesis 1**.: _As the water table (WT) depth, \(d_{\text{WT}}\), was found to be a reliable predictor of soil water contents, \(\theta\), at various depths in unsaturated peat [26], \(d_{\text{WT}}\) together with \(I_{\text{surr}}\), \(T_{\text{air}}\), RH, and \(v_{\text{w}}\) can be used to accurately reproduce peatland ecosystem and plant productivities. Thus, we hypothesize that the DIMONA PB model driven by site observations of \(d_{\text{WT}}\) and meteorology will reliably simulate peatland productivities (i.e., GPP) through the soil- plant water continuum by circumventing combined uncertainties in local soil and plant hydrological properties._  
**Hypothesis 2**.: _Following [27], \(d_{\text{WT}}\) can be predicted by near-surface \(\theta\) in peat; thus, peatland ecosystem and plant productivities can be modelled accurately by inputs of near-surface \(\theta\) together with \(I_{\text{surr}}\), \(T_{\text{air}}\), RH, and \(v_{\text{w}}\). We hypothesize that near-surface peat \(\theta\) observations measured can drive reliably simulations of peatland productivities._  
**Hypothesis 3**.: _The simple process-oriented modifiers for the effects of plant-water relations on GPP driven by observed \(d_{\text{WT}}\) and near-surface \(\theta\) capture the specifics of hydrological effects on peatland GPP._
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 2 Materials and Methods
### Hardware Analysis  
#### 2.1.1 Data Reading  
For motion sensing, the MPU6050 accelerometer, widely used in Internet of Things (IoT) applications, was selected [11]. This device communicates via the I2C (Inter-Integrated Circuit) protocol, operates on a 3.3 V supply, and provides output data for both acceleration and gyroscope measurements. Each of these outputs is represented along three axes--X, Y, and Z--resulting in six individual data values [12].
# Routing Protocols for UAV-Aided Wireless Sensor Networks
###### Abstract  
Recently, unmanned aerial vehicles (UAVs) attracted significant popularity in both military and civilian domains for various applications and services. Moreover, UAV-aided wireless sensor networks (UAWSNs) became one of the interesting hot research topics. This is mainly because UAWSNs can significantly increase the network coverage and energy utilization compared to traditional wireless sensor networks (WSNs). However, the high mobility, dynamic path, and variable altitude of UAVs can cause not only unforeseen changes in the network topology but also connectivity and coverage problems, which can affect the routing performance of the network. Therefore, the design of a routing protocol for UAWSNs is a critical task. In this paper, the routing protocols for UAWSNs are extensively investigated and discussed. Firstly, we classify the existing routing protocols based on different network criteria. They are extensively reviewed and compared with each other in terms of advantages and limitation, routing metrics and policies, characteristics, difference performance factors, and different performance optimization factors. Furthermore, open research issues and challenges are summarized and discussed.  
wireless sensor network; unmanned aerial vehicle; UAV-aided wireless sensor network; routing protocol; energy efficiency +
Footnote †: journal: Applied  
_Applied_
# Optimal Energy Management within a Microgrid: A Comparative Study
## 5 Results
### Performance Comparison between Different Methods  
This section presents the performance of the IP, GA-IP, and patternsearch-IP methods for solving the optimization problem given by the model in Equations (26)-(29). That performance is presented based on a comparison of the fitness value achieved at the optimal solution of Equations (26)-(29) and the CPU run time spent for three different microgrid setups. The microgrid under study is the one given in Section 5.2. The first and second setups consider the irradiance profiles for a summer day and a winter one and assume that all components of the microgrid illustrated in Figure 7 are online. The third setup considers the irradiance for a summer day, but assumes that the distributed generation (wind and PV) and storage (batteries and electric vehicles) systems of the microgrid are offline. The performance of the three suggested methods obtained for the three microgrid setups is as follows.  
Figure 15 provides the value of the fitness Equation (26) achieved by the IP, GA-IP, and patternsearch-IP methods at the optimal solution of the optimization problem in Equations (26)-(29) for the three different microgrid setups. It is clearly observed that for each microgrid setup, the three methods have achieved practically the same value of the fitness function. These results suggest that they perform similarly in terms of effectiveness, i.e., they all obtain the same fitness function value when solving the optimization problem in Equations (26)-(29). On the contrary, their performance in terms of computing time is substantially different, as discussed below.  
Figure 16 shows the CPU time spent by the IP, GA-IP, and patternsearch-IP methods for solving the optimization problem in Equations (26)-(29), for each one of the three different microgrid setups. As can be seen, GA-IP is the fastest algorithm for all the cases, followed by the patternsearch-IP algorithm. Please note that the GA-IP method is 57% (20%) faster than the IP method (patternsearch-IP method). Then, the results may suggest that hybrid algorithms are able to obtain solutions of the same quality as the ones provided by the IP method, but consuming less computing time.  
However, it is important to point out that the CPU time spent by the hybrid algorithms may importantly depend on the optimization problem size. Then, bearing in mind the aforementioned, the size of the studied microgrid in this work, and the obtained results in this section, it must be concluded that the CPU time performance of hybrid methods shown here might be expected for small-scale microgrids.  
Figure 14: Energy cost of power with and without Distributed Generation (DG) on a summer day and a winter day.