esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Micro-Grid Description and Experimental Setup
### Digitiser Setup and Configuration  
A NI PXIe 1071 chassis with three PXI boards inside is used for the digitiser: two data acquisition boards PXIe-6124, each one with four simultaneous sampling analogue channels (16 Bits, 4 MS/s), and one PCIe-8381 for the PXI/PCI connection.  
The signals to be measured are fed to the digitiser using appropriate transducers to adjust their values to the input range of the digitiser. Hioki P9000 voltage probes and Signaltec CT200 AC/DC current transducers combined with MCTS II plug-on burden resistors are chosen, providing high accuracy, broad bandwidth and good temperature stability. The inputs of the digitiser have been assigned as follows:  
* Three voltages and one current from the three inverters, three-phases AC grid side;
* DC bus common voltage (measured in two channels with DC and AC coupling);
* DC current in one of the inverters (measured in two channels with DC and AC coupling).  
Part of the measurement system can be seen in Figure 2: NI PXIe 1071 chassis and Signaltec MCTS II plug-on burden resistors.  
Using two channels with different coupling for the DC signals allows for obtaining the entire signal (with all frequency variations) in one of them (DC coupled) and the AC component with a much higher resolution in the other channel (AC coupled). This signal distribution allows for obtaining waveforms from the AC and DC sides with a flexible trigger system capable of correlating events on both sides.  
The digitiser is configured to use distinct sampling frequencies due to the different measurement requirements for the AC and DC signals. The study of the DC signals is the final objective of the measurement campaign. Then, DC signals are captured at the acquisition board's maximum sampling rate (4 MS/s). AC signals are mainly used for triggering and evaluating PQ parameters. Then, a sampling frequency of 20 kS/s is enough for that purpose. This configuration is suitable for a synchronised acquisition because the high sampling rate is an integer multiple of the low sampling rate allowing for sharing the digitisation clock (PXI_Clk10 PXI clock), which links both triggers using integer dividers.  
Figure 1: Micro-grid outline and digitization setup.  
The acquisition system is completed with a computer with one NI-PCIe8381 acting as a PXI-PCIe bridge. The system is based on an Intel i5-11400 processor with six cores (12 threads) and 128 Gb RAM memory running Ubuntu 20.04.4 LT as an operating system.
## 5 Numerical Experiments  
To evaluate the performance of the computational cost reduction methods, we generated benchmark instances. Figure 7 shows an example of these instances. We distributed 100 ports and a depot uniformly in a region measuring 10 km\({}^{2}\). The number of bicycles at each port was set to a random number between \(-5\) and \(+5\) at intervals of 1. A negative value (\"\(-\)\") represents the delivery port, and a positive value (\"\(+\)\") represents the pickup ports. The capacity of the vehicles was 10, the average moving speed of the vehicles was 30 [km/h], the loading or unloading time of a bicycle was 2 [min] and the time limit (all vehicles start from the depot and return to it) was 180 [min]. Thereafter, the tabu tenure was set to 50 and the initial values of weights were set to \(\alpha(0)=1\) and \(\beta(0)=1\). The increasing \(\lambda\) and decreasing parameter \(\mu\) were set to 1.07 and 0.3, respectively.  
For the search methods 1A-S, 1B-S and 1C-S (before a feasible solution was obtained), we performed 50 trials per instance and performed 200 [s] of computation for trials. Meanwhile, for the search methods 2A-S, 2A-H, 2B-S, 2B-H, 2C-S and 2C-H (after a feasible solution was obtained), we started the calculation from 50 feasible solutions per instance and performed 200 [s] of computation for each solution. These numerical experiments were performed on a Mac mini with a 3-GHz 6-Core Intel Core i5 processor and 32 GB RAM.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Local Search** & **Type** & **2A-S** & **2A-H** & **2B-S** & **2B-H** & **2C-S** & **2C-H** \\ \hline \multirow{4}{*}{Or-opt} & Normal & & & & & & \\  & & & & & & & \\ \cline{2-8}  & Reverse & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{4}{*}{CROSS-exchange} & Normal & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \cline{2-8}  & One-sided reverse & & & & & & \\  & & & & & & & \\ \cline{2-8}  & Reverse & & & & & & \\ \cline{2-8}  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Search strategy after finding a feasible solution of the mBSSRP. The first column represents local search names, the second column shows the types of the Or-opt or the CROSS-exchange operations, the third to the eighth columns show the name of the search method and the bottom line indicates whether instances were solved with hard or soft constraints.
# A Low-Profile Dual-Polarized Magneto-Electric Dipole Antenna for 5G Applications
## 3 Results  
To verify the performance of the designed MED, a prototype of the proposed MED was fabricated and measured. The gain and radiation patterns of the fabricated MED were measured in an anechoic chamber; see Figure 6a. Simulated and measured reflection coefficients (S\({}_{11}\) and S\({}_{22}\)), along with the measured isolation (S\({}_{12}\)) between the two ports, are depicted in Figure 6b. We can find that the operating bandwidth of the proposed MED was 36.8% (from 3.1 to 4.5 GHz), within the operating frequency range; the isolation between the two ports was about \(-\)22 dB. In Figure 6c, the measured gain of port 1 varied between 8.1 and 10.1 dBi, and that of port2 varies between 7.9 and 10.2 dBi, which also agreed well with the simulation. As a result of the symmetric geometry of the proposed design, almost identical gains were realized for the two ports.  
Figure 5: Parametric analyses of different parameter dimensions: (**a**) length of the electric dipole; (**b**) height of the magnetic dipole; (**c**) length of the magnetic dipole; (**d**) height of the feeding structure; (**e**) height of the meandering slots; (**f**) length of the meandering slots.  
Table 3 lists the comparison with other proposed low-profile MED antennas. It can be concluded that the antenna proposed in this study is simpler than these pre-existing low-profile MEDs. At the same time, the independent design and processing of the vertical magnetic dipole makes the overall structure of MED more stable; meanwhile, a lower processing cost was achieved. The aforementioned advantages make the proposed antenna more competitive in practical applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **E\(-\)Plane@port1** & **H\(-\)Plane@port1** & **E\(-\)Plane@port2** & **H\(-\)Plane@port2** \\ \hline
3.3 GHz & 57 & 53 & 56 & 54 \\ \hline
3.8 GHz & 47 & 48 & 47 & 48 \\ \hline
4.3 GHz & 45 & 44 & 47 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of measured 3 dB beamwidth. (Units: deg).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Low-Profile** & **Low-Profile** & **Designed** & **Overall** & **Gain** \\  & **Design** & **Structure** & **Polarization** & **Volume** & **Gain** \\ \hline
[11] & no & no & Single-polarization & \(1\times 1\times 0.24\,(\lambda_{0})^{3}\) & 8 dBi \\ \hline
[21] & \
\begin{tabular}{l} Aperture coupled \\ feed \\ \end{tabular} & complex &
\begin{tabular}{l} Dual- \\ polarization \\ \end{tabular} & \(0.58\times 0.58\times 0.15\,(\lambda_{0})^{3}\) & 8.5 dBi \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other low-profile MEDs. (\(\lambda_{0}\) is the wavelength at the center frequency).
# Performance Estimation of Intensity Accumulation Display by Computer-Generated Holograms
## 2 Method  
### Binary Computer-Generated Holograms  
Figure 1 illustrates the setup for calculating binary CGHs. First, the complex amplitude of the object light at the hologram plane is calculated by  
\[E_{o}(x,y)=\mathrm{F}^{-1}\big{\{}\mathrm{F}\{O(x,y)\}T(f_{x},f_{y};z_{0}) \big{\}}, \tag{1}\]  
where \(O(x,y)\) is the object function (the amplitude transmittance of object), which is assumed to be two-dimensional (2D) for simplicity; \(T(f_{x},f_{y};z)\) is the free-space transfer function, \((f_{x},f_{y})\) are spatial frequencies corresponding to spatial coordinates \(x\) and \(y\); \(z_{0}\) is the distance from the object to the hologram plane; and finally \(\mathrm{F}\{\cdot\}\) and \(\mathrm{F}^{-1}\{\cdot\}\) denote the 2D Fourier transform and 2D inverse Fourier transform, respectively. In the digital calculation of free-space propagation, the workspace of both the object plane and the hologram plane must be much larger than the object; otherwise, aliasing occurs [21]. Subsequently, the light field inside the window region of the hologram plane is cropped and binarized by  
\[H_{b}(x,y)=\mathrm{B}\{E_{o}(x,y)w(x,y)\}, \tag{2}\]  
where \(w(x,y)\) is the window function, and \(\mathrm{B}\{\cdot\}\) is the binarization operator, defined by  
\[\mathrm{B}\{A\} = 1\ for\ \mathrm{Re}\{A\}>0 \tag{3}\] \[= \ \ \ 0\ otherwise.\]  
In reconstruction, the binary hologram is backpropagated from the hologram plane to the object plane. There are two problems in the calculation of binary CGHs. First, if the object function is spatially smooth, the binary CGH will reconstruct a high-pass filtered version of the image and the low spatial-frequency information is lost [22]. Therefore, the object function should be pre-processed before the forward propagation. The processing methods will be introduced in the next section. The second problem is that both the field  
Figure 1: Setup of CGH calculation.  
truncation due to the window function and binarization will result in speckle noise. In this paper, our focus is on speckle noise suppression by intensity accumulation. The image reconstructed by intensity accumulation is expressed as  
\[I_{r}(x,y)=\sum_{n=1}^{N}|E_{r}^{n}(x,y)|^{2}=\sum_{n=1}^{N}\left|\mathrm{F}^{-1 }\big{\{}\mathrm{F}\{H_{b}^{n}(x,y)\}T(f_{x},f_{y};-z_{0})\big{\}}\right|^{2}, \tag{4}\]  
where \(E_{r}^{n}(x,y)\) is the reconstructed field of the \(n\)-th binary hologram \(H_{b}^{n}(x,y)\), and \(N\) is the total number of holograms for the same object. Finally, we use peak-signal-to-noise ratio (PSNR) as the metric to estimate the quality of the reconstructed image. PSNR is defined as  
\[PSNR=20\log_{10}\frac{I_{MAX}}{I_{RMSE}}, \tag{5}\]  
where \(I_{MAX}\) and \(I_{RMSE}\) are, respectively, the maximum signal and the root mean square error of \(I_{r}(x,y)\) in comparison with \(|O(x,y)|^{2}\).
## 7 Application Layer
### Delay- and Disruption-Tolerant Networking (DTN)  
As identified in Section 2, one of the roles of application layer in the adaptive framework is to improve the protection of services against uncertainties of wireless communication. One of the main challenges facing _IoT_ applications is the common risks of delay and disruptions due to the unpredictable nature of wireless medium. There are many possible sources for communication delay and disruption. For example, a very sparsely deployed sensor network application based on TCP may suffer from the long round trip time. Also, shadowing and fading effects may also result in disruptions [13]. Different from with cabled Internet, the end-to-end connectivity can no longer be taken for granted in the context of _IoT_[13].  
Delay- and disruption-tolerant Networking (DTN) [121; 13; 12] aims to provide an adaptive and robust communication mechanism that is overlaying all lower layers. The distinctive feature of DTN is its philosophy of _eventual connectivity_[12]. Specifically, DTN does not assume stable end-to-end paths or the ability of retransmission. Instead, it embraces the store-and-forward approach, with which data transmission is conducted on the basis of _bundles_, rather than individual packets [121]. The bundle protocol operates at the application layer, and does not require certain lower layer technologies. Bundles are stored at each hop and only forwarded when there is an opportunity; they are addressed using _endpoint identifiers_ (EIDs).  
The DTN technology has already been adopted by sensor networks in areas such as underwater acoustics, habitat monitoring and astronomical object tracking [121], where delay- and disruptions are to be expected. Two examples are presented in [122], designed for lake water quality and motorway noise monitoring, respectively.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 2 State of the Art in Alpha-Expansion-Based Graph-Cut Optimization
An example of alpha-expansion-based optimization performed for 8 depth levels is presented in Figure 1. At the top, the results of each optimization can be seen. The result of the last optimization (for 7 th depth level), enlarged at the bottom, is the final depth map. After all the iterations are performed, the process of optimization can be performed again, in the latter cycle of iterations [14], so the 'final depth map' (Figure 1) becomes the input to another optimization. Not all nodes have to be a part of each iteration. They can be turned off in chosen iterations in order to provide temporal consistency [14], or if some input depth was provided into the optimization and it should be unchanged during the estimation. Still, these nodes can influence the depth assigned to other nodes. Therefore, the cost  
Performing the first, coarse optimization leads to an initial, low-detail depth map if used for geometry estimation (Figure 2A). This rough estimation is the start of the next cycle of iterations in which the number of labels (depth levels) required to be examined for each segment is reduced from \(size(d)\) to only \(\pm n/2\) labels (Figure 2B). Therefore, the number of pixels active in these iterations is significantly decreased, and their set is  
\[P_{on}\Big{(}\overline{d}_{p}\Big{)}=\Big{\{}r:\Big{|}\overline{d}_{p}-r\Big{|} <n\Big{\}}. \tag{4}\]  
By leveraging the result of coarse optimization, the algorithm focuses only on the refinement of previous labeling, reducing unnecessary computations and potentially reaching better solutions. Therefore, this algorithm decreases the complexity of optimization twofold. Firstly, it limits the number of labels to be analyzed in the first cycle, and secondly, it optimizes smaller graphs in the second cycle. As one of the reviews has shown [20], this scheme provides a satisfactory trade-off between the computational complexity and the resulting quality of depth maps when compared to other global optimization-based methods. Unfortunately, as our initial experiments have shown (Section 4.2), this scheme results in slight reduction accuracy of estimated depth maps. Figure 1: An example of alpha-expansion-based depth estimation. Figure 2: An example of hierarchical alpha-expansion-based depth estimation. The currently active nodes are shown as the blue area. (**A**) First cycle of graph cut (coarse optimization). (**B**) Second cycle of graph cut (refinement optimization).
## 2 Materials and Methods
### Drought Conditions Estimates  
To present the characteristics of individual months in terms of drought occurrence over the study period (2019-2020), the SPEI Global Drought Monitor database was used [43]. Standardized precipitation evapotranspiration index (SPEI) is a multiscale drought index that combines precipitation and temperature data based on normalization of water balance estimation [44]. Detailed methodology for calculating and applying this indicator is presented in the literature [45; 46; 44]. In addition, complete description of the used data and metadata can be found online [[[https://spei.csic.es/home.html](https://spei.csic.es/home.html)]([https://spei.csic.es/home.html](https://spei.csic.es/home.html)) access on 1 February 2021].
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction
### Traffic State Feature Extraction Methods  
Traffic feature extraction technology based on ETC data embodies the transformation process from data to information and from information to knowledge. This process requires not only advanced algorithmic support but also a deep understanding of traffic operation patterns. The multi-level nature of traffic state feature extraction reflects both the complexity of transportation systems and the richness of ETC data, with each level from microscopic individual vehicle behavior to macroscopic network operations having its unique characteristics and value.
## 2 Materials and Methods
### Data-Driven Modeling of the Gas Kick Type Identification
#### 2.2.1 DTW Algorithm  
For the three time series, \(X=\{x_{1},x_{2},\cdots,x_{i},\cdots x_{k}\}\) is the simulated outlet flow rate series of the gravity displacement gas kick, \(Y=\{y_{1},y_{2},\cdots,y_{i},\cdots y_{m}\}\) is the simulated outlet flow rate series of the underbalanced pressure gas kick, \(Z=\{z_{1},z_{2},\cdots,z_{i},\cdots z_{n}\}\) is the actual measured outlet flow rate series during drilling, and \(k\), \(m\) and \(n\) are the lengths of the time series \(X\), \(Y\), and \(Z\).  
The DTW algorithm uses the dynamic programming principle (DPP) to nonlinearly match the similar features of time series \(X\) and \(Z\), \(Y\) and \(Z\). The shortest distances, \(L_{\text{X-Z}}\) and \(L_{\text{Y-Z}}\), between time series \(X\) and \(Z\), \(Y\) and \(Z\) are obtained, and \(L_{\text{X-Z}}\) and \(L_{\text{Y-Z}}\) are the similarity scales of time series \(X\) and \(Z\), \(Y\) and \(Z\). If the similarity scale \(L_{\text{Y-Z}}\) is smaller than \(L_{\text{X-Z}}\), it indicates that the matching degree of time series \(Y\) and \(Z\) is higher than the matching degree of \(X\) and \(Z\). That is, the outlet flow curve measured and the outlet flow curve obtained from the underbalanced pressure gas kick simulation, are more similar. Therefore, the gas kick type can be identified as an underbalanced pressure gas kick, otherwise it is a gravity displacement gas kick.  
The calculation process of the DTW algorithm consists of two parts: (1) calculating the distance between each element in time series \(X\) and \(Z\), forming the distance matrix \(D_{m\times n}\) (Equations (7) and (8)); (2) using dynamic programming, calculating the cumulative distance matrix \(T_{m\times n}\), based on the distance matrix \(D_{m\times n}\), and finding the optimal dynamic warping path in \(T_{m\times n}\), to obtain the dynamic warping distance between time series \(X\) and \(Z\). The specific calculation steps are as follows [24].  
\[D_{m\times n}=\begin{bmatrix}d(x_{1},z_{1})&\cdots&d(x_{1},z_{j})&\cdots&d(x_ {1},z_{n})\\ \cdots&\ddots&\vdots&\ddots&\vdots\\ d(x_{i},z_{1})&\cdots&d(x_{i},z_{j})&\cdots&d(x_{i},z_{n})\\ \cdots&\ddots&\vdots&\ddots&\vdots\\ d(x_{m},z_{1})&\cdots  
where \(t(x_{m},z_{n})\) is the dynamic warping distance \(L_{\text{X-Z}}\) of time series \(X\) and \(Z\).
# Development and Characterization of a Flexible Soundproofing Metapanel for Noise Reduction
## 3 Results  
### Structure of Flexible Soundproofing Metapanel  
In this study, we designed the geometrical structure and dimensions of a Flexible Soundproofing Metapanel (FSM) for effective noise suppression in the target band (2000-5000 Hz), a range particularly significant for human auditory comfort. Figure 3a shows the geometrical structure of the FSM, consisting of a box-shaped frame and a vibrating membrane. The membrane is securely attached to the open face of the frame using an adhesive, creating a coupled system where membrane vibrations significantly influence the dynamic response of the radiating surface. However, at frequencies near the membrane's resonance, a localized increase in acoustic transmission occurs due to the amplified motion of the membrane. Figure 3b illustrates the geometrical structure and dimensions of the FSM unit cell, indicated by a dotted cuboid. In this paper, we aimed to achieve high Sound Transmission Loss (STL) in the target frequency band by optimizing the geometric parameters \(L_{\text{x}}\), \(L_{\text{y}}\), \(L_{\text{z}}\), \(t_{\text{f}}\), and \(t_{\text{m}}\) so that each layer has different resonance frequencies.
## 4 Security Analysis and Performance Assessment
### Performance Analysis  
To enhance the reference value of the protocol proposed in this paper, we adopted the widely used AES standard for subsequent encryption. AES is considered one of the most secure and efficient symmetric encryption standards available today, supporting three key lengths: 128 bits, 192 bits, and 256 bits. Accordingly, we measured the time required for the protocol to complete all steps under different relative speeds. The time overhead for key generation under various AES security levels is illustrated in Figure 8.  
By combining the results from Figure 7, we observe that the key generation speed is influenced by not only environmental noise but also relative speed. Strong noise significantly impacts the protocol's performance. When the SNR approaches zero, the noise in the received signal can obscure the channel's varying characteristics, leading to higher  
Figure 7: Curves showing changes in key generation rate with SNR at different relative speeds.  
estimation errors in the channel gain at the receiver. These errors increase the mismatch rate of the generated key bits. High quantization bits are particularly sensitive to noise, resulting in a greater impact on key generation accuracy.  
Under the same SNR conditions, lower relative speeds extend the channel's correlation time. This forces the AIS to wait longer after each probing to ensure de-correlation between consecutive probings, thereby increasing the overall time overhead. In practice, due to the characteristics of TDMA systems, time slot allocation increases time overhead. In contrast, full-duplex systems may allow PLKG to achieve better performance by reducing such overhead.  
Furthermore, we utilized the NIST SP 800-22 standard [34] to test and analyze the security and reliability of the keys generated by the proposed protocol. Key sequences that fail these tests may possess vulnerabilities that could be exploited. NIST SP 800-22 provides a series of statistical tests to evaluate the quality of random number sequences produced by pseudorandom number generators or cryptographic algorithms. These tests are designed to ensure that the generated random numbers meet specific randomness criteria and are resistant to predictions and attacks. If the \(p\) value exceeds the predefined significance level \(\alpha\), the test is considered successful.  
In our protocol, we ensure key entropy by waiting for the probing intervals between successive probings. Additionally, implementing equal-probability quantization in MAQ further guarantees the balance of key bit frequencies, enhancing the overall security of the generated keys. Table 4 lists the \(p\) values of several test results for different length keys generated by the NIST SP 800-22 protocol at different SNRs.  
Figure 8: The time cost of generating keys of different bit numbers at different relative speeds.