esa-sceva/satcom-chunk-collection huggingface dataset:
# A Measure Based on Beamforming Power for Evaluation of Sound Field Reproduction Performance
## Appendix B Near-Field Compensated Higher-Order Ambisonics  
The reproduction method, 2.5D NFC-HOA, can generate a monopole source with a circular array of loudspeakers [30]. The distance and the angle of the monopole source can be controlled in the horizontal plane of the loudspeakers. The loudspeakers are modeled as monopoles.  
The NFC-HOA filter can be written with complex cylindrical harmonics as follows. For each monopole source located at \((R_{v},\phi_{v})\) (in polar coordinates). Ambisonic signals were encoded up to an order \(N\) and decoded to an array of \(L\) loudspeakers located at \((r_{s},\phi_{s}^{(l)})\). The filter for the \(l\)th loudspeaker \(Q_{NFC}^{(l)}\) can be written as:  
\[Q_{NFC}^{(l)}=\sum_{n=-N}^{N}W_{n}H_{n}^{NFC(R_{s},\phi_{s})}e^{in\Phi_{v}}e^{ -in\Phi_{s}^{(l)}}. \tag{11}\]  
\(H_{n}^{NFC(R_{v},\Phi_{v})}\) is the NFC filter [6]:  
\[H_{n}^{NFC(R_{s},\Phi_{s})}=\frac{h_{n}^{(1)}(kR_{v})}{h_{0}^{(1)}(kR_{v})} \frac{h_{0}^{(1)}(kr_{s})}{h_{n}^{(1)}(kr_{s})}, \tag{12}\]  
where \(h_{n}^{(1)}\) is the spherical Hankel function of the first kind, and \(W_{n}\) is the regularization function [26]:  
\[W_{n}=\frac{2}{\left|H_{n}^{NFC(R_{v},\Phi_{v})}\right|^{2}+1}. \tag{13}\]  
The number of the coefficients for a given \(N\) in Equation (11) is \(2N+1\). It is well known that the number of loudspeakers should be greater than \(2N+1\) for an accurate reproduction [5]. In this experiment, 16 loudspeakers were used, which allow for a maximum order of \(N=7\).
## 3 Results and Discussion
### Kinetic Characteristics
The intraparticle diffusion model between the adsorption capacity for the Y(III) adsorbed at time t (q\({}_{t}\)) and the square root of the time (t\({}^{0.5}\)) is a linear association and presented by the following equation:  
\[q_{t}=K_{id}t^{0.5}+I, \tag{8}\]  
where K\({}_{id}\) is the initial rate constant of intraparticle diffusion (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-0.5}\)), t (min) is the time of the adsorption process, and I gives information about the boundary layer thickness. The K\({}_{id}\) value is attained from the slope of the plot in Figure 2d. The obtained parameters are tabulated in Table 2. The value of R\({}^{2}\) was 0.79, suggesting that the intraparticle diffusion process was not a rate-limiting step for the AHIBC adsorbent. Furthermore, the value of R\({}^{2}\) reveals a significant degree of boundary layer control since the diagram of q\({}_{t}\) versus t\({}^{0.5}\) did not pass across the origin. If a trendline in a diagram of q\({}_{t}\) against t\({}^{0.5}\) reached the origin, the \begin{table}
\begin{tabular}{c c c} \hline
**Kinetic Models** & **Value** & **Parameters** \\ \hline Pseudo-first-order & 212.96 & q\({}_{e}\) (mg\(\cdot\)g\({}^{-1}\)) \\  & 0.081 & k1 (min\({}^{-1}\)) \\  & 0.85 & R2 \\ \hline Pseudo-second-order & 171.23 & q\({}_{e}\) (mg\(\cdot\)g\({}^{-1}\)) \\  & \(4.36\times 10^{-4}\) & k2 (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)) \\  & 0.99 & R2 \\ \hline  & 25.45 & \(\alpha\) (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1}\)) \\  & 0.022 & \(\beta\)t (g\(\cdot\)mg\({}^{-1}\)) \\  & 0.92 & R2 \\ \hline  & 14.953 & kid (mg\(\cdot\)g\({}^{-1}\cdot\)min\({}^{-1/2}\)) \\  & 37.433 & I (mg\(\cdot\)g\({}^{-1}\)) \\  & 0.79 & R2 \\ \hline Experimental capacity & 171.32 & q\({}_{exp}\) (mg\(\cdot\)g\({}^{-1}\)) \\ \hline \end{tabular}
\end{table} 
Table 2: Kinetic models for Y(III) adsorbed on the AHIBC adsorbent. intraparticle diffusion was deemed a rate-limiting step. The calculated data indicated that the intraparticle diffusion model would not be the only rate-limiting step.
# Optimal Data Collection Time in LoRa Networks--A Time-Slotted Approach
## 6 Evaluation and Discussion of the Results
### Simulation Setup
We generate 50 instances per simulation run and the average results are presented along with the 95% confidence intervals. The evaluation was conducted considering fixed as well as variable buffered data sizes. In the first use case, all nodes have the same amount of data to transmit (\(\sim\)10 kB), which represents the buffered data per certain period of time from a periodic data generation application. This use case is a typical scenario in precision agriculture or environmental monitoring applications where the nodes periodically wake up, take a measurement, and then return to sleep mode. In the second use case, the nodes have a different amount of data to deliver whose size varies between 200 bytes and 18 kB (i.e., 2 to 180 transmissions). \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Nodes & 100–1000 \\ (comparison with the model) & 10–50 \\ Terrain side & 1000 m \\ Gateway coordinates & (500 m, 500 m) \\ Channel bandwidth (BW) & 500 kHz \\ Preamble Symbols & 8 \\ Coding Rate & 4/5 \\ Payload size & 100 bytes \\ Buffered Data per node & 10,000 bytes (unless specified) \\ (comparison with the model) & 1000 bytes or 500–1500 \\ Path Loss model (see [6]) & \(\overline{L_{pl}}(d_{0})=95\) dB (experimental) \\  & \(d_{0}=40\) m, \(\gamma=2.08\), \(\sigma=3.57\) \\ Guard time (\(\tau\)) & 10 ms for up to 50 nodes, \\  & 40 ms otherwise \\ Receiver Sensitivities & [\(-116\), \(-119\), \(-122\), \(-125\), \\ (per SF for BW500) & \(-128\), \(-129\)] dBm \\ Tx power \& consumption & 14 dBm, 75 mA [31] \\ Rx consumption & 45 mA [31] \\ Alba transmission rate (\(\theta_{max}\)) & derived from Equations (2)–(5) \\ Radio duty cycle (\(C\)) & \(\leq\)1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. This use case represents data generation that is triggered by an event whose detection wakes up nodes to capture the event. We would also like to stress that for the sake of simplicity as well as of energy efficiency all the packet sizes of all SFs are fixed and large enough. As it has been shown [16], it is more energy efficient to use large packet sizes for all SFs to perform the data collection. This is due to the increased overhead that small packet sizes cause at the MAC layer, thus, delaying the data collection process and increasing energy consumption. On the other hand, in a network with high error rate, very large packet size would cause a high loss of data or high energy cost in case of re-transmissions. Hence, in this work a packet size in the midpoint between the lowest (8 bytes) and the highest (255 bytes) packet size is chosen.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 4 Results of the Micropump Simulation
### Static and Modal Analysis of the Actuator (FQPB)  
From the literature overview of available devices in the market, meant for the purpose, it was found that such devices work in the range from 2.5 to 5 V, requiring less battery power. With the aim of reducing the actuation voltage, static and modal analyses were carried out with 2.5 V. A deflection of 32.2 nm was observed under static analysis.
# A Wideband Eight-Port MIMO Antenna with Reduced Mutual Coupling for Future 5G mm-Wave Applications
## 3 Results and Discussions
### Reflection Coefficient  
The eight-port MIMO antenna was printed and developed to examine the simulated results with the measured results. The results show that the antenna achieved a vast bandwidth of 18 GHz, varying from 28 to 46 GHz, with enhanced isolation of 30 dB, as confirmed in Figure 16. Figure 16 demonstrates that both results are nearly the same, with tiny differences due to small defects in the production process and losses of the SMA connectors. When port 1 is excited, the remaining seven ports are individually terminated with a 50 \(\Omega\) load. Figure 16a illustrates \(\ket{S_{11}}\) and \(\ket{S_{21}}\) results of the designed eight-port MIMO antenna. The \(\ket{S_{11}}<-10\) dB from 28 GHz to 46 GHz, and \(\ket{S_{21}}<30\) dB, as is clear from Figure 16a. Figure 16b depicts the effect of antenna elements 1 on elements 3 and 4 after stimulating port 1. The MIMO antenna had a measured and simulated isolation level of \(<\)40 dB in the operational bandwidth, with the exception of \(\ket{S_{31}}\) at less than 23 dB. The \(\ket{S_{41}}\) is \(<40\) dB over the entire working range. To check the effect of antenna 1 on antennas 5 and 6, the \(\ket{S_{51}}\) and \(\ket{S_{61}}\) responses are studied in Figure 16c, and Figure 16c confirms that \(\ket{S_{51}}<40\) dB and \(\ket{S_{61}}<45\) dB. To examine the MIMO antenna further, the effect of antenna 1 on antenna 7 and antenna 8 is given in Figure 16d, and it shows that  
Figure 14: Current distribution of the proposed MIMO antenna at 28 GHz, (**a**) port 1 excited and (**b**) port 7 excited.  
Figure 15: (**a**) Front and (**b**) back view of the fabricated antenna.  
the proposed MIMO antenna's \(|\,\mathrm{S}_{71}\,|<35\) dB and \(|\,\mathrm{S}_{81}\,|<36\) dB. All antenna elements are identical, giving the same return loss response across all ports.
## 8 Code Validation
### Validation of the Element Formulation  
The analysis of the element formulation is based on the measurement of a pondered error, described as follows:  
\[E^{i}_{rr}=\delta\gamma_{i}\frac{\gamma i}{|\gamma|}. \tag{68}\]  
where \(E^{i}_{rr}\) is the pondered error determined for the component \(i\) of a generic vector \(\gamma\) and \(\delta\gamma_{i}\) is the difference between the component determined by ABAQUS(r) and its code counterpart. \(E^{i}_{rr}\) is determined as the product of \(\delta\gamma_{i}\) by the ratio between the component \(\gamma_{i}\) and the magnitude of the vector \(\gamma\).  
This metric is preferred over the direct comparison of \(\delta\gamma_{i}\), since the latter is highly influenced by floating point errors. One of the reasons for this influence is that, while ABAQUS(r) operates internally with double-precision, the output received by the code is in a single-precision format. This difference reduces the number of decimal places considered in each value and could lead to disproportional artificial errors in the vector components that contribute the least to the magnitude of the vector or in vectors whose magnitude tends towards zero. The second reason is that \(\delta\gamma_{i}\) tends to estimate large error values for  
Figure 4: Complete three-dimensional representation of the geometry considered for the bonded support and its boundary conditions. The exterior surface of the region shown in red is considered fixed. The complete geometry is provided in the Mendeley Dataset [94]. (**a**) Detailed view of the bonded region. (**b**) Detailed view of the loads applied in the lower surface.  
vector components that have a significantly smaller magnitude when compared to the magnitude of the vector.  
Table 2 summarizes the average pondered error observed for each integration point of four variants of the L-bracket numerical model described in Section 7.2, each with a different element type. For the particular case of using a 3D element (element type C3D8 in ABAQUS(r)), a thickness of 1.0 mm was considered for the L-bracket geometry. Since the largest average pondered error observed is equal to 2.0%, it can be concluded that the element formulations included in the code provided were implemented successfully for the case selected. Furthermore, for each element type, the table indicates the percentage of integration points with a pondered error less than 1.0%.  
Although not included in the present work due to its extension, the interested reader is informed that the Dataset [94] associated with this research includes a detailed comparison of every integration point, considering both the pondered and regular error measurements.
## 3 Methodology
### Model Performance Evaluation  
Despite the utilization of real hospital datasets within the design, which trained and calibrated the proposed deterioration prediction model, the framework had to be compared with well-established deterioration modeling techniques that have been extensively used to predict and model the degradation occurring within assets and their underlying components for numerous decades. Therefore, this section describes the verification procedure formulated in order to validate the proposed model and prove its capability of accurately predicting the future condition of hospital building assets by means of a comparison between the results obtained by employing a Weibull distribution function-based deterioration model with the results attained by the application of the proposed model. As previously outlined, 20% of the datasets collected from hospital buildings' inspection reports were put aside in order to be used for verification and validation purposes, and the detailed procedure that followed is explained in this section.  
The cumulative degradation distribution of the oxygen gas systems in hospital buildings based on the Weibull function was calculated according to Equation (9).  
\[\mathrm{F(x)}=\ 1\ -\ \mathrm{e}^{-\ \left(\frac{\mathrm{x}}{\mathrm{p}} \right)\ \alpha} \tag{9}\]  
where \(\mathrm{\beta}\) is the scale parameter of the distribution function; \(\alpha\) is the shape parameter. The values assumed for \(\mathrm{\beta}\) and \(\alpha\) were 3.92 and 16.24, respectively [37].  
Three performance indicators were used to measure the efficiency of the proposed model against the Weibull-based model, namely: the mean absolute error (mae), root-mean square error (RMSE), and mean absolute percentage error (MAPE). The three aforementioned tests were performed by means of employing the following Equations (10)-(12).  
\[\mathrm{MAE}\ =\frac{1}{\mathrm{N}}\ \sum_{\mathrm{i=1}}^{\mathrm{N}}\ \lvert\ \mathrm{A_{i}}-\mathrm{C_{i}}\ \rvert \tag{10}\]  
\[\mathrm{RMSE}\ =\ \sqrt{\frac{1}{\mathrm{N}}\ \sum_{\mathrm{i=1}}^{\mathrm{N}} \left(\ \mathrm{A_{i}}-\mathrm{C_{i}}\ \right)\ ^{2}} \tag{11}\]  
\[\mathrm{MAPE}\ =\frac{100}{\mathrm{N}}\ \times\ \sum_{\mathrm{i=1}}^{\mathrm{N}}\ \frac{\ \lvert\ \mathrm{C_{i}}-\mathrm{A_{i}}\ \rvert}{\mathrm{A_{i}}} \tag{12}\]  
where \(\mathrm{A_{i}}\) is an indicator for the actual condition value observable for component i within the hospital inspection process; \(\mathrm{C_{i}}\) is an indicator for the predicted value as per the prediction methodology used, either the Weibull-based distribution function, or the proposed model for component i; N is the total number of observations available for condition values of the oxygen gas systems within different hospitals that were filtered and specified for model validation purposes.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 4 Case Study: Shangmao Landslide
### Landslide Monitoring Data  
Two GNSS monitoring stations were installed on the deformation area of the landslide. The GNSS data were collected every hour, and the rainfall data were collected from the deployed rainfall station every 24 h.  
As Figure 14 shows, the deformation of the Shangmao landslide has obvious step accelerations, temporally coincident with the periods of continuous rainfall or after a large rainfall. So, the landslide deformation is closely related to the rainfall.
## 4 Discussion
### Uncertainties, Errors and Accuracies  
In this study, our evaluation of MOD17A2H GPP assumed that the values of GPP derived from EC flux tower sites are reliable, even though it is well known that the flux-derived GPP values encompass many uncertainties [48]. The ecosystem respiration (R\({}_{\text{eco}}\)) is an essential variable when the flux-derived GPP value is calculated. However, accurate estimation of R\({}_{\text{eco}}\) is difficult, and misestimation can produce random and systematic errors in estimating GPP. Moreover, mismatches in scale between MODIS pixels and tower flux footprints lead to further uncertainties. Additionally, researchers have discovered structural errors in the MODIS GPP algorithm [49; 50; 51]. For example, it was found that the MODIS GPP algorithm cannot correctly identify the contribution of leaves in both insolated and shaded locations to the calculation of total GPP when compared with GPP estimates from a two-leaf process-based model [52].
## 4 Results and Discussion
### Working Memory Data
Mean \(\pm\) standard deviation values are displayed for four classification performance measures: precision, recall, F1 score and accuracy. The subjects are ordered from the highest to lowest accuracy. Figure 8 displays the most relevant connectivities (average for all subjects), according to the relevance vector \(\bar{\varrho}\), discriminated by the frequency band pair. The background topoplots show the average nodal relevance, which corresponds to the relevance of the total information flow of every node. This is defined as the sum of all the \(\varrho_{p}\) in \(\bar{\varrho}\) associated with all directed interactions originating from and targeting a specific node or EEG channel [22]. We note that, in general, there is high nodal relevance for interactions where the phases of oscillations in the \(\theta\) band drive the amplitudes of oscillations in the \(\alpha\) and \(\beta_{l}\) bands. However, the highest nodal relevance is achieved by the phase-amplitude interactions from \(\alpha\) to \(\beta_{l}\) band activity. As expected, the relevant connectivities are distributed in a similar way in terms of frequency. Spatially, they tend to involve long-range interactions linking frontal, temporal and parietal regions. In particular, for the phase-amplitude interactions from \(\alpha\) to \(\beta_{l}\), there are multiple bidirectional connections between channels on yhr frontal and prefrontal areas and channels on yhr parietal and parieto-occipital areas. There are also several relevant long-range connections targeting the right temporal region. These results coincide with those previously obtained from the within-frequency, phase-based connectivity analysis carried out on the same database [22], as well as with studies that identified the presence of fronto-parietal and fronto-temporal interactions during cognitive tasks that activate visuo-spatial working memory [6; 29; 49].
## 1 Introduction  
Fractional-order systems (FOSs) have garnered substantial attention in scientific research due to their inherent capability to model memory and hereditary properties. These properties are pivotal in accurately capturing the dynamics of natural and engineered systems, which are often characterized by non-local interactions and long-term dependencies. Unlike integer-order derivatives, fractional derivatives provide a robust mathematical framework to describe such behaviors, enabling the analysis of complex phenomena over multiple time scales. Applications of fractional calculus (FC) have been extensively documented in areas such as anomalous diffusion, viscoelastic materials, control systems, and electrochemical processes, highlighting its interdisciplinary relevance [1, 2, 3, 4, 5, 6, 7, 8, 9]. From a control perspective, fractional-order (FO) models excel in capturing systems with inherent memory effects, offering advantages over traditional integer-order approaches. This has been demonstrated in diverse engineering domains, including robotics, signal processing, and materials science [10, 11, 12, 13, 14, 15, 16]. Furthermore, fractional derivatives inherently encapsulate the influence of historical states on the current dynamics, providing a more comprehensive representation of system behavior, particularly in systems with hereditary or viscoelastic properties. Stability analysis of FOSs has become a cornerstone in advancing their practical utility. Stability is crucial not only for understanding the inherent dynamics of these systems but also for designing robust control strategies that accommodate uncertainties, time delays, and actuator constraints. This study aims to contribute to the ongoing dis course by addressing stability challenges in FOSs and proposing novel solutions tailored to real-world applications.
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 2 Related Studies
The methods used to test their potential are linear logistic regression (LogReg) and nonlinear random forest (R.F.) [21]. The analysis examined events with a 48 h magnitude window, at least 250 km from the observatory, and with a magnitude greater than four on the Richter scale. The LogReg method defines the magnitude of the signal within 10 min of the recording intervals as the main seismic reporting parameter. The R.F method has been shown to produce promising results, which will be improved by continuously enriching the operational data with new data. Many classifications, data sets, locations, and approaches have been used in earthquake predictions. In this study, the Duzce Province, geographically located in the Marmara region of Turkey, with GPS coordinates of 40\({}^{\circ}\)49\({}^{\prime}\)59\({}^{\prime\prime}\) N and 31\({}^{\circ}\)10\({}^{\prime}\)0\({}^{\prime\prime}\) E was selected. The Duzce Province was selected as the study area because it is a province with intense earthquake activity. The purpose of this study is to determine earthquakes' magnitudes and their occurrence probability in the future using earthquake magnitude data, including the Moon's distance from the Earth, the b value and d value of the earthquakes, the depth and time information of the region between the years 1990 and 2022, by applying the RNN method.
# Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet
## 4 Results
### Ablation Experiment  
In this paper, ablation experiments were performed to verify that the joined attention mechanism and to multi-task impact the prediction of gene mutations. The settings of the experimental items are shown in Table 2.  
Figure 6 showed the comparison of the prediction results between the base model ResNet and the model with the attention mechanism on the test dataset; the prediction accuracy of the original ResNet model was 72.1%. The model's accuracy was 82.6% after adding the squeeze and excitation attention mechanism. The combination of ResNet with the SE attention mechanism significantly improved the accuracy when compared to the original model. The attention mechanism uses the global sensory field to dynamically adjust the importance of each channel in the feature map. The mechanism exploits the correlation information between the feature maps to assist in the extracting of CT image features of pulmonary nodules. Adding the squeeze and excitation attention mechanism can effectively improve the accuracy of lung nodule classification and model performance.  
Figure 7 showed the model accuracy of single-task prediction to predict EGFR and KRAS and the model accuracy of multi-task prediction. The results showed that the multi  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Setting** & **Acc** \\ \hline a & ResNet & 72.1\% \\ b & ResNet+Attention mechanism & 82.6\% \\ c & ResNet+Single-task & EGFR 76.1\% KRAS 74.0\% \\ d & ResNet+Multi-task & EGFR 87.8\% KRAS 86.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of different combinations of ablation experiments.  
Figure 5: Mut-SeResNet: (**a**) the accuracy of Mut-ResNet in predicting EGFR and KRAS; (**b**) the loss of Mut-ResNet in predicting EGFR and KRAS genes.  
Figure 6: Experiment a, b, and d: (**a**) base Resnet and ResNet network with only the attention mechanism added; (**b**) multi-task training in the ResNet network.  
task prediction results were higher than the single-task prediction. The training accuracy for the single-task prediction of EGFR was 84.9%, and the testing accuracy was 76.1%; the training accuracy for predicting KRAS was 78.2%, and the testing accuracy was 74.0%. The multi-task prediction model predicted EGFR with 87.8% accuracy and KRAS with 86.7% in test dataset. The accuracy of single-task prediction was much lower than that of multi-task prediction. The tasks influenced and promoted each other when the multi-task prediction was performed.  
When compared to only adding the squeeze and excitation attention mechanism and multi-task training, the Mut-SeResNet model achieved better prediction performance and higher accuracy.