esa-sceva/satcom-chunk-collection huggingface dataset:
# Forest Fire Detection Using New Routing Protocol
## 5 Methodology of LAR-Based Reliable Routing Protocol (LARRR)
### Finding the Route
Based on the tuning of our simulation, the best value of this period is selected at 1.5 s, where the overhead will increase in the network if this period is selected more than 1.5. Otherwise, there will be not enough route requests in the network if this period is selected less than 1.5. However, nodes used in this simulation are usually provided with restricted resources. Thus, it is not required to keep all offered routes, where nodes will keep the best route only through the TOP. The highest temperature a node can tolerate is 130 \({}^{\circ}\)C, where the node will totally burn and die at higher temperatures. The flowchart of the LARRR protocol is depicted in Figure 8.  
Furthermore, Figure 9 displays the diagram of the LARRR protocol for selecting the best route with respect to the fitness value. According to Figure 9B, the \(B\) node becomes unsafe. In other words, it senses a high temperature. Thus, all neighbors of the \(B\) node (i.e., \(D\), \(E\), \(F\), and \(G\) nodes) change their parent to the \(B\) node in order to use and exploit the energy of the \(B\) node and save the power of the other nodes. Hence, the network lifetime will increase. In Figure 9C, the temperature of the \(A\) node is also increased. Therefore, the parent of nodes \(D\) and \(E\) is changed to the \(A\) node. Meanwhile, the buffer of node \(B\) is nearly filled by the data. Thus, the parent of the \(G\) node will change to the \(C\) node in order to avoid dropping packets from the \(B\) node. Figure 9D shows that \(A\), \(B\), and \(C\) nodes all became unsafe, while Figure 9E shows that the \(B\) node is already failed and burned because its temperature reaches 130 \({}^{\circ}\)C. In this condition, the \(F\) node has to select either the \(A\) node or the \(C\) node based on temperature and the node's buffer. Thus, the \(F\) node selects the \(A\) node because the temperature and buffer of the \(A\) node are higher than those of the \(C\) node. Consequently, the proposed LARRR protocol takes into account the nodes, which sense high temperatures and also the buffer of nodes. This leads to avoiding the bottleneck problem occurring and dropping many data packets. Figure 8: Flowchart of LARRR protocol. Figure 9: The diagram of the proposed LARRR protocol.
## 3 Results
### The Primary Hypothesis  
The primary hypothesis tested whether positional performance in the Max zone would be worse than in the SubMax zone across at least four of the six positional parameters. The results, presented in Table 2, indicate the following: Median values for posterior tilt decreased significantly in the Max zone (7.68, IQR 3.45-11.91) compared to the SubMax zone (4.18, IQR 0.72-7.64); (\(p<0.05\)). Similarly, NAH forward rotation showed a significant decrease in the Max zone (3.04, IQR 0.85-5.23) compared to the SubMax zone (2.39, IQR 0.75-4.02); (\(p<0.05\)). Median values for NAH down tilt decreased from 2.87 (IQR \(-0.16\)-5.90) in the Max zone to 2.29 (IQR 0.39-4.20) in the SubMax zone, and AH down tilt decreased from 4.73 (IQR 1.03-8.43) to 3.52 (IQR 0.47-6.57), but these differences were not statistically significant (\(P=0.162\) and \(P=0.278\), respectively). In contrast, median values for anterior tilt increased significantly from 1.10 (IQR 0.22-1.97) in the Max zone to 1.58 (IQR 0.63-2.53) in the SubMax zone (\(P=0.019\)), and AH forward rotation also increased significantly from 1.19 (IQR 0.46-1.93) in the Max zone to 1.32 (IQR 0.46-2.17; \(P=0.045\)). Overall, four out of six positional parameters showed worse performance in the Max zone compared to the SubMax zone. However, only two differences (posterior tilt and NAH forward rotation) were statistically significant (\(p<0.05\)).  
Figure 6: Scatter plot for individual subjects in all directions in the Max and SubMax zones. Note: Red dash lines mean different water levels below the top of the box.  
Spillage prevention rates increased progressively with the distance of the water level from the box top, as shown in Table 3. At the highest water level (1 cm), only 16.9% of all participants avoided spilling in the Max zone and 25.3% of all participants avoided spilling in the SubMax zone. At the lowest water level (5 cm), spillage prevention rates improved to 91.6% in the Max zone and 95.8% in SubMax zone. The SubMax zone consistently showed slightly higher spillage prevention rates than the Max zone at all water levels. The Max zone exhibited worse positional performance in four out of six parameters compared to the SubMax zone. Spillage prevention rates were lower in the Max zone at all water levels.
# Comparison of 2- and 3-Dimensional Cultured Periodontal Ligament Stem Cells; a Pilot Study
###### Abstract  
This study compared the characteristics of periodontal ligament stem cells (PDLSCs) cultured using 3-dimensional (3D) versus conventional 2-dimensional (2D) methods. PDLSCs were cultured in either a 3D culture with a non-adhesive culture plate (Stemfit 3D\({}^{\text{(g)}}\)) or a conventional 2D culture using a 6-well plate. Morphology, viability, proliferation ability, and osteogenic differentiation were analyzed to characterize the differences induced in identical PDLSCs by 3D and 2D culture environments. In addition, gene expression was analyzed using RNA sequencing to further characterize the functional differences. The diameter and the viability of the 3D-cultured PDLSCs decreased over time, but the shape of the spheroid was maintained for 20 days. Although osteogenic differentiation occurred in both the 2D- and 3D-cultured PDLSCs, compared to the control group it was 20.8 and 1.6 higher in the 3D- and 2D-cultured cells, respectively. RNA sequencing revealed that PDLSCs cultured using 2D and 3D methods have different gene expression profiles. The viability of the 3D-cultured cells was decreased, but they showed superior osteogenic differentiation compared to 2D-cultured cells. Within the limitations of this study, the results demonstrate that the structure and function of PDLSCs are influenced by the cell culture method.  
periotodental ligament stem cell; 3-dimensional culture; spheroid; morphology; osteogenic differentiation; RNA sequencing +
Footnote †: journal: Applied Sciences  
**Keywords:** periodontal ligament stem cell; 3-dimensional culture; spheroid; morphology; osteogenic differentiation; RNA sequencing +
Footnote †: journal: Applied Sciences
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 5 Antenna Poses Estimated from Captured Images
### Approximate Pose Estimation for Initialization  
#### 5.1.1 Axial Alignment  
The strong axiality of the antenna shape originates from the fact that most directional antennas are approximately cuboid in shape. We use the concept of principal axes to describe the axiality of both the antenna model and the antenna projections in images. We define the 3D principal axis of the model as the 3D line segment that crosses the centroid of the model, is oriented in the direction along which the model extends the farthest and is bounded by the mesh (as illustrated in Figure 8a); similarly, the 2D principal axis of a projection is the 2D line segment that crosses the centroid of the 2D silhouette, is oriented in the direction along which the silhouette extends the farthest and is bounded by the contour (as illustrated in Figure 8a).  
The first step of our coarse pose estimation procedure is to find a pose for which the 3D and 2D axes are aligned. First, we detect the 2D/3D principal axes from the images and the 3D model. There are many ways to achieve this, for example, by applying principal component analysis (PCA), or independent component analysis (ICA) to the contour points and the 3D model vertices, or by finding the (rotated) bounding box of the contour/model.  
Once the 2D axes have been found for all viewpoints, we recover the 3D principal axis in \(VF\) from the end points of the 2D axes using triangulation methods (e.g., [58]). Let the recovered 3D axis be denoted by \(\overline{E_{0}^{\prime}E_{1}^{\prime}}\), and let the 3D principal axis of the model be denoted by \(\overline{E_{0}E_{1}^{\prime}}\); then, we have:  
\[R_{\perp}^{\prime},T^{\prime}=\underset{R,T}{argmin}\sum_{k=1,2}\left|RE_{k}+ T-E_{k}^{\prime}\right|^{2}, \tag{8}\]  
where \(R_{\perp}^{\prime},\ T^{\prime}\) is the pose to be estimated. Equation (8) is also known as the generalized Procrustes problem and can be efficiently solved analytically [59].  
Figure 7: As shown in the leftmost box, the position of the 3D principal axis is recovered from multi-view contours (green), and the 3D antenna model (gray) is aligned with the recovered axis and rotated to find a coarse estimate of the antenna pose. As shown in the left column of the middle box, there are discrepancies between the antenna contours and the projections from the approximate pose (blue), and as shown in the right column, the approximate pose can be globally refined to reduce the contour discrepancy with respect to the resulting refined pose (pink). In the rightmost box, the final overall estimation result is shown as the transformation from the model frame (gray) to the reference frame (pink).
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 8 Experimental Verification
### Experimental Results of the Proposed Dual-Mode Quadrature Oscillator in Figure 3  
The VDGA-based resistorless dual-mode QO circuit in Figure 3 was designed to achieve an oscillation frequency of \(f_{osc}\) = 63.66 kHz. As per Equation (21), the designed component values for \(C_{1}\) = \(C_{2}\) = 2 nF yielded \(g_{mkl}\) = 0.8 mA/V. Figure 23 shows the measured transient waveforms for the quadrature voltage outputs \(v_{osc1}\) and \(v_{osc2}\). It is observed that the quadrature oscillation outputs exhibit a 92.8\({}^{\circ}\) phase difference. The Lissajous figure of the two output voltages is shown in Figure 24 to determine the correlation between the quadrature outputs. Figure 25 further illustrates the corresponding spectrum frequency of \(v_{osc1}\)
## 3 Control Strategy in Fast Frequency Response
### Novel Coordinated Control Strategy (NCCS)
In the case of a system with large inertia, the initial RoCoF appears smaller when a disturbance occurs compared to a system with a small inertia, and the recovery speed of RoCoF is also slow, so \(\alpha\) was determined as a large value, and vice versa. In addition, the value of \(\alpha\) can be changed according to the amount of FFR that the ESS can output. Figure 6 shows relationship between RoCoF and the constant \(\alpha\). In this study, the optimal value was determined through repeated preliminary simulations. The second frequency dip occurs when limited resources are rapidly exhausted or during the recovery period in the wind turbine generator. To prevent the second dip, there is a way to reduce the amount of additional power generated by the wind turbine, but this approach does not utilize resources efficiently, so the wind turbine generator should produce the maximum additional power after reaching the frequency deadband. Instead, the output of the BESS is set to a frequency deadband different from that of the wind turbine and reacts when it reaches the specified RoCoF. In this way, the initial RoCoF can be increased, but a second frequency dip can be avoided, and resources can be used efficiently. Figure 5: Concept used to improve frequency nadir. Figure 7 illustrates a flowchart of the proposed NCCS. The NCCS indicates coordinated control between the wind turbine generator and BESS for FFR control in the case of a frequency drop, and efficient resource utilization for the purpose of increasing the frequency nadir. First, when the frequency falls beyond a certain value, the FFR of the PMSG is activated, and when the deceleration period reaches the specified time, the output is reduced through torque control of the converter. When the rotor speed recovers through the acceleration section and the operating point reaches the MPPT curve, the operation is followed by MPPT. In the case of the BESS, the response is not due to the frequency deadband, but to the RoCoF. When a specific RoCoF is reached, the FFR of the BESS operates. This is not an RoCoF proportional control method, but a constant power control method. This is to improve the frequency nadir by operating the BESS when RoCoF is low, that is, when it reaches the frequency nadir.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 4 Results and Discussion
### Result Analysis with XAI Techniques
#### 4.3.4 Accumulated Local Effects (ALE) Explanation
This finding aligns with educational theories that stress the importance of sustained engagement for deeper learning and adaptability. It suggests that extended instructional periods may provide students with more opportunities to assimilate information, engage with challenging concepts, and develop critical thinking skills, all of which are crucial for adaptability in rapidly changing educational landscapes. Policy-wise, this supports arguments for restructuring school schedules to allow for longer class periods, potentially leading to improved educational outcomes. Age:  
The relationship between 'Age' and adaptability is less direct, as shown by the minor fluctuations across adaptability levels. However, the general trend indicates that 'Low' adaptability decreases with age. This could reflect the development of coping mechanisms and resilience as students mature, a concept supported by developmental theories. The varied adaptability across age groups could inform the design of age-specific curricula and support services, tailoring educational strategies to the developmental stage of the student cohort. Figure 8: ALE plots for the three most influential features, (**a**) Class Duration, (**b**) Age, and (**c**) Financial Condition, on student adaptability predictions. Financial Condition:  
The steep positive slope for 'High' adaptability with improved 'Financial Condition' underscores the pivotal role of economic stability in educational success, as posited by numerous studies linking financial security with better academic performance. The plot highlights a stark reality: students from more affluent backgrounds are likely to be more adaptable, possibly due to greater access to resources, extracurricular activities, and learning support. This insight has profound policy implications, emphasizing the need for equity-focused financial initiatives such as scholarships, grants, and resource allocation to schools serving economically disadvantaged communities. The ALE plots also reveal the multidimensional nature of student adaptability, validating educational theories, influencing classroom and curriculum design, and emphasizing the need for comprehensive policies to address diverse factors contributing to student adaptability.
# Hybrid Beamforming in Massive MIMO for Next-Generation Communication Technology
## 3 MIMO and Massive MIMO
\[T_{h}=B\] (5) where \(T_{h}\) is the throughput, \(B\) is the bandwidth, and SINR is the signal-to-interference-plus-noise ratio. Assuming more separate routes than a standard MIMO system, a massive MIMO that has an exceptionally high number of transmit antennas might potentially even attain a greater system performance. * _Energy efficiency_ The proportion of the network throughput (estimated in bits per second) to the system's overall use of power (estimated in watts) is known as energy efficiency (EE) in MIMO systems. \[EE=\frac{T}{W}\] (6) where \(T\) is system throughput and \(W\) is the total power consumption of the setup. To attain a sufficient level of performance, a MIMO system's efficiency in using its power resources is measured by EE. A stronger EE suggests improved power management and use, whereas a reduced EE shows inefficiencies. Adaptive modulation and coding, power distribution, and beamforming are a few examples of approaches that can be used to maximize EE in MIMO setups. According to [30], the transmitted power (p\({}_{\text{t}}\)) and number of antennas (n\({}_{\text{t}}\)) are inversely related as a result of coherent combining. The transmitted power will decrease considerably as n\({}_{\text{t}}\) rises; hence, power per antenna should be inversely proportional to n\({}_{\text{t}}\). In [31], the authors kept the transmitted power constant, and it is demonstrated that throughput is exactly proportional to the quantity of transmitting antennas. The authors in [32] describe the minimal power usage (in milliwatts), concluding that energy efficiency improves with an increase in the number of antennas at the transmitter and receiver. * _Cost efficiency_ Massive MIMO technology is cost-effective because it uses low-power-consumption components like power amplifiers and has the potential to dramatically lower the amount of radiated power (by a factor of a thousand) [33]. * _Simple Signal Processing_ Massive MIMO reduces interference effects, fast fading, and thermal noise, simplifying signal processing [34]. Massive base station antenna arrays offer crucial \"channel hardening\" properties. When fading channels act more predictably, the huge MIMO channel matrix gets close to the predicted values, or the count of antenna elements gets close to infinity [10].
## 4 Methodology  
The optimal coordination of OCRs is carried out following the flowchart depicted in Figure 2. Note that the first three steps involve the Digsilent software, while the last step consists of executing a genetic algorithm (GA). The process consists of six steps which are described below. Step 1: Digsilent Power Factory software is used for modeling the test network, using input data. Step 2: Operative scenarios (OS) for the test network are defined and configured. Step 3: Short-circuit currents are obtained using Digsilent Power Factory software for different fault locations and for every OS. Step 4: The simulation results obtained in Step 3 are used as input data for the PCA technique. Using the procedure described in Section 3.1, the main components \(P\) of the simulation data are obtained. The PCA technique modifies the representation of data variables to improve the performance of the K-means clustering algorithm. Step 5: The main components \(P\) correspond to the input parameters of the K-means algorithm. The K-means algorithm performs the clustering of the operative scenarios to facilitate the protection coordination of the microgrid. Step 6: The simulation results obtained in Step 3 and the clusters acquired in Step 5 are the input parameters of the GA to find the setting of the protection coordination. In this case, the protection coordination model (given by Equations (1) to (9)) is solved for each cluster with the proposed GA; then, a set of coordinating parameters is generated for each OS.  
Several methodologies can be used to solve the set of equations given by (1)-(9). In this case, a GA was selected since this type of technique has proven to be effective in obtaining the OCR coordination scheme as indicated in [29; 30; 31]. The GA starts with a random generation of candidate solutions that must go through the stages of selection, crossover, and mutation until a given number of generations has elapsed. A detailed description of the implemented GA can be consulted in [32]. Nonetheless, other techniques could be used for obtaining the OCR coordination scheme as reported in [33; 34; 35].  
Figure 1: Flowchart of the K-means clustering algorithm.
## 3 Methods
### Feature Extraction
#### 3.2.4. Galvanic Skin Response (GSR)  
Galvanic skin response (GSR), also known as electrodermal activity (EDA), is a measurement of the electrical conductance of the skin, which fluctuates with physiological and psychological stimuli. These changes are predominantly due to the activity of sweat glands that are innervated by the sympathetic nervous system. When arousal levels increase, so does sweat production, leading to higher skin conductance. GSR sensors monitor these electrical changes, and the acquired data are then analyzed to deduce features indicative of physiological arousal and the corresponding emotional reactions. The application of GSR extends across disciplines such as psychology, neuroscience, and human-computer interaction, contributing to the study of emotions and stress. The interplay between GSR readings, sweat gland activity, and the workings of the autonomic nervous system provides researchers with valuable insights into the nuances of human behavior and affective experiences.  
The GSR readouts shown in Figure 4 are associated with five different emotional states.
# Design, Manufacture and Wind Tunnel Test of a Modular FishBAC Wing with Novel 3D Printed Skins
## 7 Wind Tunnel Test
### Displacements and Actuation Power Measurements  
Displacements under both actuation loads and freestream airflow were measured using the same two-camera point-tracking system described in Section 6. Additionally, three (one per actuator) LEM(r) 6 A CASH 6-NP Hall Effect current sensors are used to determine their power consumption. These sensors measure the strength of the magnetic field induced by the current flow through the actuator leads and generate an output voltage that is directly proportional to the magnitude of current--at a rate of \(312.6\,\mathrm{mA}\,\mathrm{V}^{-1}\). A detailed explanation of this methodology can be found in [24]. Since the actuator voltage supply was also recorded, the power draw of each actuator can be calculated as the product of amperage and voltage.  
Figure 23 shows the transverse displacements of the trailing edge (spanwise) when the freestream velocity and angle of attack are \(30\,\mathrm{m}\,\mathrm{s}^{-1}\) and \(0^{\circ}\), respectively. It is observed that the transverse displacements show very similar trends as in when the aerodynamic loads are absent (see Figure 19), which suggests that the achievable displacements are not limited by the amount of torque available, but by the servo's operating range, which determines the pulley angle rotation at each tendon. Additionally, Figure 24 shows variations in actuator current draw as a function of actuation input angle and at different pitch angles. It is observed in this figure that the power draw does not significantly vary when aerodynamic loads increase, implying that the structural stiffness is significantly more dominant in the overall load than the aerodynamic stiffness.
## 3 Localised Information Retrieval with Query Resolution
### Localised Sensing Data Collection with the Sink Proxy  
Localised Sensing Data Collection (LSDC) consists of three steps: local data delivery, data aggregation, and aggregated data delivery to the sink node. An example of the LSDC scheme is illustrated in Figure 3. On receiving a query message, the corresponding sensor nodes send the sensing data back to the sink proxy in a local region. This is achieved using the reverse path obtained from the query geocast initiated by the sink proxy. The sink proxy, such as node 9 shown in Figure 3, collects the sensing data locally before forwarding it to the sink node. The sink proxy aggregates the sensing data by placing multiple sensing data into one packet. To efficiently aggregate sensing data and deliver the aggregated data to the sink node, the sink proxy sends the aggregated data according to the following two conditions: (a) The number of aggregated sensing data equals the largest number that the packet can contain; (b) The number of aggregated sensing data equals the number of the total corresponding nodes, which is calculated from the sink node. If either of these two conditions is satisfied, the sink proxy delivers the aggregated packet to the sink node. The sink proxy sends aggregated data to the sink node by unicasting, which can be obtained from the query dissemination. Finally, the sink node collects all data that correspond to the query. The collected data are then delivered to the user who initiated the query.