esa-sceva/satcom-chunk-collection huggingface dataset:
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 4 Line-Defect Cavities
### Resonance Condition  
Figure 8 shows a top view of an LN cavity. \(L\) is the cavity length, and \(r_{L}\) (\(r_{R}\)) is the left (right) mirror's reflection coefficient. These mirrors are broadband and generally have a high reflectivity [73]. We expand the field in the basis of the two guided counter  
Figure 7: Bloch modes of the line-defect waveguide. (**a**) Magnitude and (**b**) phase of the lateral electric field component of the forward-propagating Bloch mode along the centerline of the waveguide. Each color corresponds to a different value of the wavenumber, which is reported in (**a**) in units of \(2\pi/a\). (**c**) Magnitudes of the spatial Fourier harmonics \(b_{q}\). (**d**) Magnitudes of the ratio between the spatial harmonic of order \(-1\) and the average (left). Group index (right).  
propagating Bloch modes. In the absence of optical gain and disorder, the Bloch modes are only coupled at the mirrors. In this case, one finds  
\[E(z)=\bar{F}e^{+ikz}e_{+}(z)+\bar{B}e^{-ikz}e_{-}(z) \tag{10}\]  
where while \(\bar{F}\) (\(\bar{B}\)) is the amplitude of the forward (backward) Bloch mode. We set the point \(z=0\) at the center of the cavity. Therefore, the left (right) mirror is located at \(z=-L/2\) (\(z=L/2\)) and the boundary conditions read  
\[\bar{F}e^{+ikz}L/2e_{+}(L/2)r_{R}=\bar{B}e^{-ikz}L/2e_{-}(L/2) \tag{11a}\] \[\bar{B}e^{-ikz}(-L/2)e_{-}(-L/2)r_{L}=\bar{F}e^{+ikz(-L/2)}e_{+}(-L/2) \tag{11b}\]  
The backward mode, \(e_{-}(z)\), is the complex conjugate of \(e_{+}(z)\)[78]. By using this property and requiring that the equations have a solution other than the trivial solution \(\bar{F}=\bar{B}=0\), one finds  
\[r_{L}r_{R}e^{+2i\left[\phi_{+}\left(\frac{L}{2}\right)-\phi_{+}\left(-\frac{L }{2}\right)\right]}e^{+2 ikz}L=1 \tag{12}\]  
where \(\phi_{+}(z)\) is the phase of \(e_{+}(z)\). With our choice of a unit cell, \(\phi_{+}(z)\) is zero at the unit cell input and output (see Figure 7b). Therefore, one finds \(\phi_{+}(\pm L/2)=0\). Since \(L\) consists of an integer number of unit cells (see Figure 8) and if the phase of the left and right mirror is equal to either zero or \(\pi\), from Equation (12) one obtains  
\[k_{z}-\pi/a=m\pi/L \tag{13}\]  
in agreement with [47; 75]. Here, \(m\) is an integer. Equation (13) provides the detuning of the wavenumber \(k_{z}\) from the band edge for the longitudinal resonant mode of order \(m\). As the cavity length increases, this detuning diminishes. Correspondingly, the frequency of the resonant mode decreases, by following the dispersion relation of the underlying line-defect waveguide (see Figure 2a, red line). Importantly, FDTD simulations of LN cavities have revealed that the resonant modes do obey Equation (13) to good approximation [47]. Therefore, we take this agreement as an indication that the phase of the left and right mirror reflection coefficient in Figure 8 may be approximated with either zero or \(\pi\). In Section 4.4, we show that zero is the right approximation.
# Ground Target Detection and Damage Assessment by Patrol Missiles Based on YOLO-VGGNet
###### Abstract  
Patrol missiles are a common type of unmanned aerial vehicle, which can be efficiently used for reconnaissance and sensing. In this work, target detection and the damage assessment of typical mobile ground targets by patrol missiles are studied, and a new method, combining the YOLO v3 with the VGG networks, is proposed for the problem. Specifically, with YOLO v3 as the precursor, the proposed method can detect, classify, and localize ground targets accurately and quickly. Then, the image blocks of detected targets are fed into the lightweight VGG networks, which can evaluate their damage level coarsely. Meanwhile, based on class activation mapping (CAM) and deconvolution, we further analyse the activation intensity of clustered convolution kernels, which helps determine whether the targets' key components are destroyed. Unlike traditional image change detection methods, which require images before and after a strike for comparison, the proposed method learns the target model through extensive training and can assess the target's damage status in a timely and online manner. Compared to previous learning-based methods, our detailed analysis with convolutional feature visualization of the damaged targets and their components gives a more interpretable perspective. Finally, Unity simulation experiments prove the proposed method's effectiveness, which improves the accuracy of damage level assessment by 16.0% and 8.8% compared with traditional image-change-detection-based methods and the two-CNN learning-based method. The convolutional feature clustering method evaluates the status of the targets' key components with an accuracy of 72%.  
Keywords:damage assessment; target detection; feature layer visualization; class activation mapping; deconvolution; convolutional feature clustering +
Footnote †: journal: Medical Imaging
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.2 Carrier Frequency Offset (CFO)  
The functionality of the CFO block is to cancel the frequency offset that introduces the ICI and impacts the orthogonality of subcarrier. The frequency offset occurs due to the following:  
* Frequency difference between the transmitter and receiver oscillators.
* Oscillator instabilities.  
Figure 8: Illustration of the sign-based buffers partitioning and the single-symbol buffer. **(a)** The derivation of buffers’ sign-pattern from code cover. (**b**) The operation of the single-symbol buffer.  
It is divided into two types; the integer carrier frequency offset, \(\varepsilon_{ICFO}\) that comes from the coarse synchronization block, and the fractional carrier frequency offset \(\varepsilon_{FCFO}\) that comes from the fine synchronization block. The received signal can be defined as follows:  
\[b(n)=\frac{1}{N}\sum_{k=0}^{N-1}A(k)G(k)e^{\frac{i2\Pi(k+\varepsilon)n}{N}}+w(n )\quad\,n=0,1,2,\ldots N-1 \tag{22}\]  
The \(A(k)\) is the modulated data, \(N\) is the number of subcarrier used, \(G(k)\) is the channel gain at \(k\)th subcarrier, \(w(n)\) is the AWGN noise, and \(\varepsilon\) is the frequency offset, which is determined in (23) as  
\[\varepsilon=\varepsilon_{ICFO}+\varepsilon_{FCFO} \tag{23}\]  
The effect of the offset on the received symbol can be canceled by multiplying (22) by the exponential raised to the phase conjugate to the estimated offset as follows:  
\[\hat{b}(n)=b(n)e^{\frac{-i2\Pi(\varepsilon)n}{N}} \tag{24}\]  
where \(\hat{b}(n)\) is the corrected symbol. As a complex exponential represents rotation of \(b(n)\), the CORDIC design is utilized with its rotation mode [22], and the phase accumulator is used to accumulate the offset each cycle. In the CORDIC implementation, 15 stages and the recursive pipelined design are implemented. Moreover, that design has lower area and consumes lower power than that in [23; 24].
## 4 Discussion
### Limitations and Future Directions
#### 4.4.3 Estimation in Multi-Occupant Environments  
This experiment demonstrated the potential of ESP32 CSI to accurately track the RR of a single individual. However, tracking the RRs of multiple individuals in a shared space poses significant challenges due to the overlap in CSI signals caused by movement, complicating individual separation. This study specifically targets individuals living alone, such as the elderly, who can greatly benefit from reliable self-monitoring tools.
## 2 Digital Platform--DNS with SWAT Module  
The digital platform--DNS was developed at the Polish Institute of Meteorology and Water Management--National Research Institute (IMGW-PIB). The decisive factor in its development was the ability to use the best features of models, methods, and databases concentrated in one digital space (Figure 1). Initially, the digital platform--DNS was used to simulate the transport of nutrients in basins with limited hydrological and qualitative monitoring data. This was made possible by combining the SWAT model and e.g., MIKE 11 [63], as well as IMWM-PIB monitoring databases and data-processing modules for supplementing missing measurement data, which was described in detail in [64; 65; 66; 67]. SWAT has quickly become the most important module of the digital platform--DNS. It is a physical, semi-distributed continuous-time model that runs on a daily, monthly, or yearly time step. Its basic feature is the possibility of continuous simulation of hydrological processes (evapotranspiration, surface runoff, percolation, return flow, groundwater flow, transmission losses in the canal, retention in ponds and reservoirs, channel routing, and field drainage). However, SWAT can also simulate the transport of soil particles, both in the land and riverbed phases [68; 69]. This feature quickly proved to be useful when trying to track sediment particles in the mountain basin in the Polish part of the Western Carpathians [70; 71; 72; 73]. The key to a reliable simulation of sediment transport in SWAT is the appropriate quantity and quality of parameters and input data. Their task is to best represent the basin area with its natural and anthropogenic processes in the digital model space. The advantage and disadvantage of this tool is that it requires both the knowledge of a large number of parameters and the collection of appropriate input data [38; 74].  
SWAT was developed in the USA and many of its default parameters must first be adapted to the local conditions of the analyzed basin and be kept within a realistic range of uncertainty. In turn, the input data should describe the basin's topography, soil characteristics and distribution, land cover, and meteorological conditions in the basin area at an appropriate level of detail. The basic input data required to build this tool are:  
* A digital elevation model (DEM) for information on the watercourse stream network and length, drainage pattern of the watershed, channel width within the watershed, slope, and reach length;
* A map of hydrographical divisions in order to divide the basin area into sub-basins;
* land use maps and agotechnical data for determining land cover classes;
* A soil map--detailed data on soil types to identify soil classes and their physical properties such as available water content, soil texture, soil bulk density, hydraulic conductivity, and organic matter;
* Meteorological data for precipitation, temperature, humidity, wind speed and direction, and solar radiation.
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 5 Numerical Experiments, Results, and Discussion
### Two Signals in One Frequency
However, if there are two highly correlated signals, both MUSIC and PWD estimations are biased, and ML techniques are better choices by far. All of the techniques improve when smoothing is utilized, which is depicted in Figure 4. In addition, the performance of all techniques would be improved if frequency-domain smoothing were applied in a manner similar to time-domain smoothing. Furthermore, for lower frequencies (\(f\leq 900\) MHz), the DoA estimation performance is poor. If correct windowing is used at the lowest frequencies, the estimate suffers the same problems as that at low frequencies. Therefore, an SAA with a larger radius should be used for multiple coherent signals in low frequencies. As shown here, the DoA estimation performance of many electromagnetic waves with a rigid sphere is a function of the frequency analyzed. In addition, the estimation accuracy in a particular frequency is affected by the sphere radius, as depicted by CRLB. For the present SAA radius of 8.4 cm, the most accurate performance was achieved at 16 GHz. If similar performance is required, for example, at about 4 GHz, then a larger radius is needed. Therefore, better performance is achieved in lower frequencies as the radius becomes larger. Finally, in order to fulfill advancing technology standards, particular elements are positioned in the systems. The distance between the elements becomes smaller, which  
Figure 6: RMSE versus SNR comparison test between MUSIC, PWD, DML, SML, and WSF. leads to strong mutual coupling with poor impedance matching and radiation performance. Incorporating the mutual coupling effect existing between elements, experimentally measured data, which are the ground truth to test any procedure, were used to evaluate all of the methods. Hence, experimental measurement data were further used for performance evaluation and analysis. The SAA was situated at the center of the anechoic chamber, and the source was positioned at 74 DoAs, which were obtained from different combinations of 4 different elevations and 18 different azimuths. We chose the azimuths from 5 degrees to 365 degrees with 20 degrees as a step size. For detailed information on the measurement architecture, readers are referred to the previous paper [40], where the measured data were first published. Gross error (GE) performance evaluation metrics were equally employed to evaluate the methods. The comparison of performance between MUSIC, PWD, DML, SML, and WSF is shown in Figure 7. The WSF exhibits better performance. Both SML and DML show a high degree of similarity to WSF, but WSF and DML perform better than SML. This may be because SML converges to a local minimum but does not converge to the global minimum. PWD and MUSIC have worse performance compared to the other methods. Therefore, DML and WSF are good for the analysis of multiple signals, particularly in single-frequency analysis.
## 6 Conclusions  
This study introduces a groundbreaking strategy to enhance the resilience of ground logistics networks by incorporating the construction of an underground logistics network. This dual-function infrastructure acts proactively against natural disasters like floods and typhoons while providing a reliable alternative for logistics operations during such events. Empirical validation through numerical experiments highlights the effectiveness of implementing an underground logistics network. Resilience exhibits sensitivity to variations in budget and pipeline capacity within a specific range, stabilizing beyond a critical threshold. Determining the optimal budget and pipeline capacity becomes crucial for balancing resilience enhancement and cost-effectiveness in practical planning, avoiding unnecessary expenditures.  
Moreover, this study emphasizes the practical value of the proposed model and algorithm for network planners. The integration of budget constraints and underground pipeline capacity considerations empowers planners to strategically enhance ground logistics network resilience. The results contribute nuanced insights into how specific decisions on budget and pipeline capacity impact logistics network resilience, guiding informed decision-making.  
In conclusion, this research not only presents an innovative method for boosting ground logistics network resilience but also offers practical insights for network planning under budgetary constraints and infrastructure considerations. Comprehensive numerical experiments attest to the robustness of our approach, providing actionable insights for real-world applications.
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 2 Methodology
### PWARX Model  
The PWARX (piecewise affine autoregressive exogenous) model is widely used as a mathematical model for identifying hybrid systems and is given by  
\[y_{k}=\begin{cases}\theta_{1}^{\intercal}\begin{bmatrix}r_{k}\\ 1\end{bmatrix}+e_{k},&\text{if }r_{k}\in\mathcal{X}_{1}\\ \vdots&\vdots\\ \theta_{s}^{\intercal}\begin{bmatrix}r_{k}\\ 1\end{bmatrix}+e_{k},&\text{if }r_{k}\in\mathcal{X}_{s}\end{cases} \tag{1}\]  
for \(k=1,2,\ldots,N\). The input, output, and noise at time \(k\) are given by \(u_{k}\in\mathbb{R}^{m}\), \(y_{k}\in\mathbb{R}^{p}\) and \(e_{k}\in\mathbb{R}^{p}\), respectively, whereas the regressor vector \(r_{k}\in\mathbb{R}^{n}\) is given by \(r_{k}=[y_{k-1}^{\intercal}\,y_{k-2}^{\intercal}\,\ldots\,y_{k-n}^{\intercal} \,u_{k-1}^{\intercal}\,u_{k-2}^{\intercal}\,\ldots\,u_{k-n}^{\intercal}]^{\intercal}\), where \(n=pn_{y}+mn_{u}\), and \(m\), \(p\), \(n\) are the dimensions of the real coordinate space of \(u_{k}\), \(y_{k}\), \(r_{k}\), respectively, while \(n_{y}\), \(n_{u}\) are non-negative integers. \(\mathcal{X}_{i}\) for \(i=1,2,\ldots,s\) denotes the convex polyhedral subsets of the regression space \(\mathcal{X}\subseteq\mathbb{R}^{n}\). \(s\) represents the number of sub-models and \(\theta_{i}\in\mathbb{R}^{(n+1)\times p}\) for \(i=1,2,\ldots,s\) are the model parameters to be estimated.  
It should be noted that the PWARX model used in this work is a modified version of the general PWARX model presented above, in the sense that flexibility is allowed by variable selection in the number of variables from one sub-model to another; as such, the number of estimated parameters \(\theta_{i}\in\mathbb{R}^{(n+1)\times p}\) in each sub-model can be different.  
Figure 1: Overview of the proposed Framework.
## 4 Experiment Setup and Performance Analysis
### Experiment Results and Analysis in the Park Scenarios
#### 4.2.2 Performance Comparison of Diversity Scheme with Different Antenna Placement Strategies  
To compare the transmission gain of single cluster using eight antennas reception scheme with two clusters using four antennas reception scheme, we conducted the second experiment in the researched park. Limited by the size of the park, we reduced the transmitting power of the RF module to 0 dBm. When the sensor node was located in Location1 and Location3, some antennas in the sink node were in LOS transmission. When the sensor node was located in Location2 and Location4, all antennas in the sink node were in NLOS transmission. Especially when sensor node were in Location4, the sensor node was completely obscured by the building. The experiment compares the performance of two clusters reception scheme with single cluster reception scheme using the same number of antennas in the researched park scenarios. The experimental results are shown in Figure 10.  
Figure 8: The sink node and the sensor node in the park scenarios.  
It can be seen from Figure 10 that both two clusters using four antennas reception scheme and single cluster using eight antennas reception scheme can improve transmission performance in the park scenarios. When the sensor node is located in Location1, the transmission between the sensor node and some antennas in sink node are in LOS, and the FER of the single cluster using eight antennas reception scheme placed on the two local convergence nodes is \(2\times 10^{-3}\) and \(4\times 10^{-3}\) respectively. Under the circumstances, the macrodiversity reception scheme with two antenna clusters can achieve error-free transmission. When the sensor node is located in Location2, there are some obstacles with small occlusion area between the sensor node and sink node, the FER of single cluster using eight antennas reception scheme at LCN1 is \(1.5\times 10^{-2}\), while the macrodiversity reception scheme can still guarantee a better transmission performance (FER = \(2\times 10^{-3}\)). When the sensor node is located in Location4, the sensor node is completely blocked by obstacles, and most of the antennas in the sink node cannot work. Under the circumstance, the performance of the macrodiversity reception scheme is similar to the single cluster using eight antennas reception scheme, and is close to the single antenna reception scheme.  
Figure 9: The relationship between performance of macrodiversity reception scheme using different number of antennas and transmission distance. (**a**) The relationship between PER of macrodiversity reception scheme using different number of antennas and transmission distance. (**b**) The relationship between FER of macrodiversity reception scheme using different number of antennas and transmission distance.
# Vibrational Analysis of Building Structures with Irregularities
## 3 Method of Solution
### Deterministic Modeling
The impact of air within the cavity--important for low- to mid-frequency vibrations [27; 33]--is modeled using the Helmholtz equation, expanded in the Fourier cosine series over the \((x,y)\) plane, assuming acoustically hard cavity walls. Pressure \(p(x,y,z)\) is expressed with the coefficients \(\Gamma_{mn}^{(1)}\) and \(\Gamma_{mn}^{(2)}\) in the following equation:  
\[p(x,y,z)=\sum_{m,n=0}^{N}\Bigl{(}\Gamma_{mn}^{(1)}e^{\gamma mz}+\Gamma_{mn}^{ (2)}e^{-\gamma mz}\Bigr{)}a_{m}(x)\beta_{n}(y),\]where \(\gamma_{mn}=\sqrt{k_{m}^{2}+\kappa_{n}^{2}-k^{2}}\) and \(k=\omega/c\), \(c\) being the speed of sound, with boundary conditions coupling this pressure to the upper and lower plates, as follows:  
\[\left.\frac{\partial p}{\partial z}\right|_{z=0}=\rho\omega^{2}w_{1}(x,y), \quad\left.\frac{\partial p}{\partial z}\right|_{z=h}=\rho\omega^{2}w_{3}(x,y). \tag{5}\]  
Using the example of a double leaf structure comprising three layers of floor, joists, and ceiling, the final system matrix for the structure is assembled to express the Lagrangian for Fourier coefficients, as in the following:  
\[\mathcal{L}=\frac{1}{2}\mathbf{c}^{\mathsf{T}}\left[ \begin{array}{ccc}M_{1} &I_{12}&J_{13}\\ J_{21}&M_{2}&J_{23}\\ J_{31}&J_{32}&M_{3}\end{array} \right]\mathbf{c}+\mathbf{f}^{\mathsf{T}}\mathbf{c}. \tag{6}\]  
As mentioned above, the material stiffness sub-matrices \(M_{i}\) are diagonal for homogeneous materials. The off-diagonal sub-matrix \(J_{ij}\) represents the coupling between material layers \(i\) and \(j\), such as glue, screws, or nails joining the floor plate to the joint beams, or air coupling within the cavities. Coupling energy contributions from elastic connectors (e.g., rubber clips) can be similarly integrated, representing the dynamic relationship between structural elements. The interested reader is directed to [15] for the complete details. The normal equations induced by the Lagrangian in (6) are the linear system in Equation (2). This deterministic model offers a powerful framework that is capable of capturing realistic coupling effects between structural components (see [15; 27]), making it an effective approach for low-frequency analysis.
# Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si
## 4 Simulation Results  
Figure 10 shows the results of the numerical simulation for the reflection coefficient \(|S_{11}|\) at the antenna driving point, plotted versus the frequency. Some dips (quasi-resonant frequencies) with \(|S_{11}|\) about 7 dB or better are observed in the region between 2.2 and 6 GHz. The dip frequencies, besides 2.4 and 5.9 GHz, are approximately 3.2 GHz, 3.9 GHz, 4.4 GHz, 4.7 GHz, and 5.2 GHz. The dips' position and depth depend on several design  
Snapshots of simulated surface currents at 2.4 GHz and 5.8 GHz are shown in Figure 12a,b, respectively. Also, the coupling effect is visualized in the additional detail view of Figure 12a. In the case of 2.4 GHz (Figure 12a), a maximum surface current value is observed along the two outer lines of the spiral, while on the inner lines, the surface current is up to approximately half the maximum value. A smooth overall current distribution is observed. As shown in Figure 12b, the surface currents for 5.8 GHz are smaller due to a higher operating frequency.