esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Chemometric Tools  
#### 2.2.1 Partial Least Squares Discriminant Analysis (PLS-DA)  
Partial Least Squares Discriminant Analysis (PLS-DA) [38; 39] is a widely applied tool in the context of discriminant classification. One of its major benefits is that it allows handling ill-conditioned data matrices (a condition often encountered working with spectral data) [40]. This approach, despite being a classifier, starts from the resolution of a regression problem defined between a predictor data matrix \(\mathbf{X}\) and a dummy response \(\boldsymbol{y}\)[41]. The dummy \(\boldsymbol{y}\) has a key role in the application of the PLS-DA algorithm; in fact, it encodes class information through binary ciphering, and it allows solving the classification problem by estimating the regression equation represented by Equation (1):  
\[\boldsymbol{y}=\mathbf{X}\boldsymbol{b} \tag{1}\]  
The solution is achieved by Partial Least Squares (PLS) [42; 43]. In Equation (1), \(\boldsymbol{b}\) represents the regression coefficients.  
When the investigated problem involves only two classes (as in the present work), \(\boldsymbol{y}\) is a binary vector whose elements represent whether the corresponding sample belongs to one class (\(y=1\)) or to the other (\(y=0\)). For example, in a two-category case, for six samples equally distributed between the two classes (the first three objects belonging to the first class and the remaining ones to the second category), the \(\boldsymbol{y}\) dummy would be \(\boldsymbol{y}=[\,1\,1\,1\,0\,0\,0]^{\mathrm{T}}\). Once the calibration model is built on the training samples (i.e., a set of objects whose class-membership is known), it is possible to classify unknown samples (\(\mathbf{X_{new}}\)) and estimate the predicted \(\hat{\boldsymbol{y}}_{new}\). Nevertheless, \(\hat{\boldsymbol{y}}_{new}\) will be made of real numbers, and, consequently, the class-membership cannot be directly deduced. Different classification rules have been proposed to face this issue (see, e.g., in [44; 45; 46; 47]; in the present work, the solution suggested by [PERSON] and collaborators, i.e., to apply linear discriminant analysis on the predicted response, has been applied [46].  
In the present work, PLS-DA has been (separately) applied on spectra collected on the shell (\(\mathbf{X_{1}}\)) and on the kernel (\(\mathbf{X_{2}}\)).
# Monolithically Integrated THz Detectors Based on High-Electron-Mobility Transistors
## 4 Implementation
### THz Focal Plane Array  
The THz focal plane array chip measures 5 mm by 5 mm and features a bow-tie structure. Figure 7 presents an image of the manufactured and assembled focal plane array, along with a detailed view of the first four rows of the first quadrant. The chip has adhered to a highly resistive silicon substrate, through which the THz radiation couples.  
The pixels are smaller than the individual detectors, measuring 360 um in diameter compared to 610 um. This relates only to the size of the structure; the guide of the metallic surface was executed according to the designs listed above. The detectors are arranged as follows: the entire focal-plane array consists of 12 \(\times\) 12 detectors, totaling 144. This array is divided into four quadrants, each containing 6 \(\times\) 6 detectors. The subdivision has the advantage that four pixels can be read out simultaneously; a detailed description can be found in Section 5. The distance from the center to the center of the detectors is 380 um in both spatial dimensions. All detectors in a row share a common drain and source connection, indicating that they are connected in parallel. The detectors in a column share a common gate connection, which means that they are also connected in parallel with respect to their source connections.  
Figure 6: Photograph of the fabricated GaN HEMT THz detectors with various antenna structures and a close-up of the central structures, respectively. Green boxes highlight the HEMT structures on each detector.
###### Abstract  
Authentication has been adopted in many areas, but most of these authentication schemes are built using traditional cryptographic primitives. It is widely believed that such primitives are not resistant to quantum algorithms. To deal with those quantum attacks, lattice-based cryptography was introduced by [PERSON] in 1996. To the best of our knowledge, the existing lattice-based authentication schemes are based on a lattice-based public key encryption called NTRU: a ring-based public key cryptosystem, proposed by [PERSON], [PERSON], and [PERSON] in 1998. However, these schemes only support the case of a single user. In view of the aforementioned issue, we propose the first lattice-based group authentication scheme. The proposed scheme is secure against replay attacks and man-in-the-middle attacks. Moreover, compared with the existing lattice-based authentication schemes, ours provides the most efficient method to agree upon a session key among a group of users after mutual authentication.  
group authentication; lattice-based cryptography; quantum attacks; information security 1
## 5 Experiments and Results
### Experimental Setup  
We integrated the hardware-implemented SPU into the scalar OpenRISC processor system of OR1200 for constructing the CPU-SPU collaborative SoC for real-time program execution monitoring and instruction fault self-repairing. The main frequency of the CPU core is set as @100 MHz, and the internal clock signal of integrated SPU satisfies the synchronization with the processor. The hardware configurations of I-Cache and D-Cache support the different sizes of 2, 4, 8, and 16 KB.  
We first configured the processor with a typical depth size of 8-KB I-Cache and 8-KB D-Cache, in which the internal structures consisted of the 512 cache line blocks. We developed the embedded system in Verilog hardware description language (HDL), performed the logic synthesis and implementation in Xilinx ISE Design Suite 14.7, and completed the hardware implementation on the Xilinx Virtex-5 FPGA development board. The GNU Cross  
Figure 11: The timing diagram of one BB execution with integrity validation and instruction recovery in the worst situation.  
Compilation Toolchain or32-elf-gcc matching with the OR1200 instruction set architecture (ISA) was utilized to generate InsCodes.  
Moreover, we configured some necessary controllers for some off-chip peripherals, such as the DDR2 SDRAM, parallel flash, serial ports, and Ethernet. In the system initialization stage (Boot Process), the SoC bitstream is programmed from the flash memory onto FPGA at power-up; then, the bootloader (U-Boot) boots a Linux kernel to mount the root file system for execution. We defaulted that the SPU monitors the program execution and recoveries the instruction fault during the whole application life cycle.
## 3 Indoor Positioning Systems
### Hybrid Indoor Positioning Systems
#### 3.6.4 RSSI-IMU-Vision Hybrid Systems  
[PERSON] et al. combined different technologies to improve the locating of people in indoor environments [115]. The authors started by indicating a person's position through a combination of radio frequency-based technologies such as Wi-Fi, RFID, and Bluetooth Low-Energy (BLE). The combination of technologies recognized one or more people in an open area of a building and provided an ID for everyone. Due to the complexity of the environments, other sensory models were added to the model. Inertial technologies, which include features such as a magnetometer, gyroscope, and accelerometer, allowed the system to know the direction taken by the user while traveling. The computer vision allowed the system to provide more detail when identifying possible destinations that the user could reach. The distribution of technologies was defined as follows: The scenario received the Wi-Fi network nodes and the fixed cameras of the closed-circuit TV system. The user received a device that contained an RFID tag and active BLE beacons. The built model had the advantage of being scalable for the number of users monitored, and the error margins did not exceed 1.20 m.  
[PERSON] et al. identified that the existing solutions do not identify locations with sufficient speed to allow continuous interactions, especially in heterogeneous and dynamic environments [20]. The focus of their work was to establish a system that had as a characteristic rapid, implicit, non-intrusive, and ubiquitous recognition of users or places of interest in indoor navigation. The proposed system has hybrid features, combining a 3D computer vision system (stereovision), inertial sensors, and RFID beacons, which allows one to quickly recover the path in a displacement performed at the speed of human walking (about 1.1 m/s). The results described in this work indicate that the system was able to simultaneously identify four individuals with 96% accuracy and seven with 95% accuracy.
# Unified Performance Analysis of MIMO Mixed RF/FSO Relaying System
## 2 System and Channel Model
 \tag{9}\]  
Further, we use ([16] [Equation (07.34.17.0011.01)]) to simplify the expression. Finally, we get CDF of \(\gamma_{FSO}\) as the following:  
\[ \begin{split}& F_{FSO}^{p}(\gamma_{th}){=}\sum_{n=0}^{\infty} \sum_{i=0}^{n}\Lambda(v,n,i)\frac{2^{3\beta_{s}+2i-Lg-2L-a_{s}-\frac{1}{2}}}{ \pi^{3/2}\Gamma(a_{s})\Gamma(\beta_{s})}L^{L}g^{L}\\ & G_{4L+1,4L+5}^{4L+4,1}\left(\begin{array}{c}\frac{1}{16}\left( \frac{\alpha_{s}\beta_{s}}{A_{0}\omega}\frac{MN}{F}\right)^{2}\frac{\gamma_{th }}{\hat{\gamma}}\bigg{|}_{\frac{2\beta_{s}+i}{4},\frac{2\beta_{s}+i+1}{4},\frac {2\beta_{s}+i+2}{4},\frac{2\beta_{s}+i+3}{4},(\frac{Lg}{4},\frac{1+Lg}{4}, \frac{2\beta_{s}+i}{4},\frac{3\beta_{s}+i}{4})^{\otimes L},0}^{\otimes L}\\ \end{array}\right)\\ \end{split}  \tag{10}\]  
Due to the complicated form of the above formulas, in order to analyze the main determinants of the approximate performance of the entire system under the high SNR region, we have considered the case where the pointing error can be neglected in the following analysis. When the pointing error can be neglected, that is to say \(A_{0}=1,g\rightarrow\infty\), we assume the link of MIMO FSO is characterized by \(\Gamma\Gamma\) turbulence. Considering the complexity of the calculation, we simplify the system model. We currently do not consider the impact of misalignment on the system. For MN statistically independent and identically distributed \(\Gamma\Gamma\) random variables, the probability density function (PDF) is as follows [17]  
\[ \begin{split} f_{FSO}(\gamma_{FSO})&=\frac{(\alpha_{s }\beta_{s})^{(\alpha_{s}+\beta_{s})/2}\gamma_{FSO}^{((\alpha_{s}+\beta_{s})/4-1 )}}{2h_{a}^{(\alpha_{s}+\beta_{s})/2}\gamma_{FSO}^{(\alpha_{s}+\beta_{s})/4-1 }\Gamma(\alpha_{s})\Gamma(\beta_{s})}\\ &\times G_{0,2}^{2}\Bigg{[}\frac{\alpha_{s}\beta_{s}}{h_{a}}\sqrt {\frac{\gamma_{FSO}^{T}}{\hat{\gamma}_{FSO}}}\bigg{|}_{\frac{\alpha_{s}-\beta_ {s}}{2},\frac{\beta_{s}-\alpha_{s}}{2}}\bigg{]},\end{split}  \tag{11}\]  
We can derive the CDF of the \(\gamma_{FSO}\) as  
\[F_{FSO}(\gamma_{FSO})=\frac{2^{\alpha_{s}+\beta_{s}-2}}{\pi\Gamma(\alpha_{s}) \Gamma(\beta_{s})}G_{1,5}^{4,1}\Bigg{[}\frac{(\alpha_{s}\beta_{s})^{2}}{16} \frac{\gamma_{FSO}}{\hat{\gamma}_{FSO}}\bigg{|}\Delta\Omega,0\Bigg{]}, \tag{12}\]  
where \(\Delta\Omega=\frac{\alpha_{s}}{2},\frac{\alpha_{s}+1}{2},\frac{\beta_{s}}{2}, \frac{\beta_{s}+1}{2}\).
## 2 Materials and Methods
### Image Acquisition and Processing
\begin{table}
\begin{tabular}{c c} \hline
**Dimensions, Weight** & (8.20 \(\times\) 6.70 \(\times\) 6.75) cm length width and height, 357 g \\ \hline
**Pixel Size Multispectral and Thermal Sensor** & 3.45 μm and 12 μm \\ \hline
**Multispectral Resolution, Aspect Ratio** & 2064 \(\times\) 1544 pixels (3.2 MP \(\times\) 5 imagers), 4:3 \\ \hline
**Thermal Resolution** & 160 \(\times\) 120 pixels (0.01 K) \\ \hline
**Multispectral and Thermal Focal Length** & 8 mm and 1.77 mm \\ \hline
**Multispectral and Thermal FOV** & 48\({}^{\circ}\)\(\times\) 36.8\({}^{\circ}\) and 57\({}^{\circ}\)\(\times\) 44.3\({}^{\circ}\) \\ \hline
**GSD at 60 m Altitude** & Multispectral: 2.6 cm, Thermal: 41 cm \\ \hline
**Spectral Bands: Multispectral and Thermal Cameras** & Blue (475 nm), Green (560 nm), Red (668 nm), Red Edge (717 nm), Near Infrared (842 nm) and LWIR (11 μm) \\ \hline
**Spectral Bandwidth: Multispectral and Thermal Cameras** & Blue (32 nm), Green (27 nm), Red (14 nm), Red Edge (12 nm), Near Infrared (57 nm) and LWIR (6 μm) \\ \hline
**Bit Depth** & Multispectral: 12-bit, Thermal: 14-bit \\ \hline
**Thermal Sensitivity, Thermal Accuracy** & Less than 50 mK, \(+/-\) 5 K \\ \hline \end{tabular}
\end{table} 
Table 2: Detailed technical specifications of the sensor model Micasense ALTUM. FOV: field of View. GSD: ground sample distance. lated in this study, was conducted in accordance with the established methodology of [44]. This method involved segmenting the tree canopies from the background by utilizing the CHM and, following this, temperature histograms were computed specifically using pixels derived from pure pistachio vegetation.
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
###### Abstract  
This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.  
PI controller; bio-inspired; speed control; PMSM; FPA; SCA; BBO; CSA; BA Article
# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
###### Abstract  
With the success of the commercial operation of the maglev train, the demand for real-time monitoring and high-performance control of the maglev train suspension system is also increasing. Therefore, a framework for performance monitoring and performance optimization of the maglev train suspension system is proposed in this article. This framework consists of four parts: plant, feedback controller, residual generator, and dynamic compensator. Firstly, after the system model is established, the nominal controller is designed to ensure the stability of the system. Secondly, the observer-based residual generator is identified offline based on the input and output data without knowing the accurate model of the system, which avoids the interference of the unmodeled part. Thirdly, the control performance is monitored and evaluated in real time by analyzing the residual and executing the judgment logic. Fourthly, when the control performance of the system is degraded or not satisfactory, the dynamic compensator based on the residual is updated online iteratively to optimize the control performance. Finally, the proposed framework and theory are verified on the single suspension experimental platform and the results show the effectiveness.  
suspension system; data-driven residual generator; performance degradation; performance optimization
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 5 Network Architectures
### Convolutional Neural Networks  
Traditional CNN architectures use multiple blocks of successive convolution and pooling operations for feature learning and down-sampling along the time and feature dimensions, respectively. As an alternative, [PERSON] et al. used atrous CNNs, which are based on dilated convolutional kernels [54]. Such kernels allow achieving a comparable receptive field size without intermediate pooling operation. [PERSON] et al. showed that ASC systems can be improved if the receptive field is regularized by restricting its size [55].  
In most CNN based architectures, only the activations of the last convolutional layer are connected to the final classification layers. As an alternative, [PERSON] et al. followed a multi-scale feature approach and further processed the activations from all intermediate feature maps [56]. Additionally, the authors used the Xception network architecture, where the convolution operation is split into a depthwise (spatial) convolution and a pointwise (channel) convolution to reduce the number of trainable parameters. A related approach is to factorize two-dimensional convolutions into two one-dimensional kernels to model the transient and long-term characteristics of sounds separately [19; 57]. The influence of different symmetric and asymmetric kernel shapes were systematically evaluated by [PERSON] et al. [58].  
Several extensions to the common CNN architecture were proposed to improve the feature learning. [PERSON] and [PERSON] adapted the spatial pyramid pooling strategy from computer vision, where feature maps are pooled and combined on different spatial resolutions [59]. In order to learn frequency-aware filters in the convolutional layers, [PERSON] et al. proposed to encode the frequency position of each input feature bin within an additional channel dimension (frequency-aware CNNs) [44]. Similarly, [PERSON] et al. added the first and second order time derivative of spectrogram based features as additional input channels in order to facilitate detecting transient short-term events that have a rapid increase in magnitude [60].
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 3 Results
### Main Variables Affecting Soil Toxicity to Trifolium pratense  
In order to visualize the relationship between the variables considered in this study, principal component analysis (PCA) was performed to determine which properties and elements control the toxicity in the tested soils. Total metal concentrations and growth indicators (Pt, Rt, and PR) were not considered to avoid creating noise in the analysis. The parameters considered explain 82.2% of the data.  
The two main dimensions were used for the graph (Figure 5). The different soils studied (i.e., RA, RA4B, RA8B, and NC) are grouped in different parts of the diagram. The increase in Dim 1 and decrease in Dim 2 coefficients significantly differentiate RA soil from RA amended soils, by the increase in the dose of biochar applied. NC soil is significantly separated from RA soil by the increase in Dim 1 coefficient with no differences in Dim 2 coefficient. The large variability visible for the RA soil and NC soil is due to the presence of some outliers that influence the analysis.  
From the PCA results, it can be observed that the increase in biomass (both aerial and root) is directly related to the increase in pH and inversely related to the bioavailable forms of Cu and the EC. The location of the NC samples in relation to these variables indicate the high influence of the non-contaminated soil in the aerial and root biomass.  
The germination rate (Ge) and the number of plants two months after planting (PI) are directly related to the increase of As_b and inversely related to soluble and bioavailable Zn and soluble Cu. The location of the RA soil and RA amended soils in relation to these variables indicate the positive effect of the biochar amendment in relation to the increase of plant germination and plant growth and the decrease of soluble forms of Cu and Zn and bioavailable forms of Zn.  
Figure 5: Principal component analysis (PCA) of the main variables in this study. The two components represented (Dim 1 and Dim 2) explain 82.2% of the data. The ellipses represent the variability of the treatments (RA, RA4B, RA8B, NC).
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 5 FedOpt Evaluation
### Computation Overhead  
In the end, we discuss the computation cost of FedOpt on MNIST and CIFAR-10 datasets as shown in Figures 7 and 8, respectively. We only consider the running time of the cipher-text operation to prove our main contribution. By considering the security requirement, we select plain-text \(\xi_{1}=2^{16}\) with the security parameter of \(\sigma=128\) bits, and analyse the computational cost per each user II and the cloud server on each phase as mentioned in Section 4.3. In specific, in each subfigure, as demonstrated in Figures 7 and 8, the computational cost increases linearly with the increasing number of gradients because FedOpt encrypts every single packet in each aggregation. Therefore, the computational overhead over the encryption process is related to the total number of gradients regardless of number of users. Furthermore, increased security (higher security parameter \(\sigma\)) leads to the inefficiency. In this regard, as shown in Figure 7, FedOpt achieves 74% and 53% at the encryption phase, 72% and 45% at the aggregation phase, and 86% and 31% at the decryption phase, less computational overhead than FedAvg and PPDL, respectively, on MNIST dataset. Similarly, Figure 8 shows the computation overhead on CIFAR-10 dataset where FedOpt achieves 61% and 52% at the encryption phase, 43% and 31% at the aggregation phase and 72% and 48% at the decryption phase, less computational overhead than FedAvg and PPDL. The overall computational overhead for users at the encryption phase with the security parameter of \(\sigma=128\) bits is about \(\times 2.8\) slower than the baseline configurations because FedOpt requires fewer addition and multiplication operations. Similarly, the overall computational overhead for the cloud server with the security parameter of \(\sigma=128\) bits is about \(\times 9.3\) slower than the baseline configurations. This less computational overhead at the server-end is because FedOpt decrypts every single packet in each aggregation, where the number of decryption process linearly  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **MNIST (Accuracy = 91.3)** & **CIFAR-10 (Accuracy = 87.6)** \\ \hline Baseline & 2218/2218 MB & 35653 MB/35653 MB \\ \hline FedAvg \(epochs=50\) & 119.65 MB/119.65 MB & 2589.5 MB/2589.5 MB \\ \hline FedAvg \(epochs=100\) & 84.73 MB/84.73 MB & 1665.7 MB/1665.7 MB \\ \hline PPDL \(epochs=50\) & 98.63 MB/311.6 MB & 1472.2 MB/4739.2 MB \\ \hline PPDL \(epochs=100\) & 63.74 MB/432.2 MB & 958.3 MB/6342.4 MB \\ \hline FedOpt \(epochs=50\) & 10.2 MB/102 MB & 109.23 MB/1090.3 MB \\ \hline FedOpt \(epochs=100\) & 14.6 MB/146 MB & 172.3 MB/1723 MB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication bits required for upload and download to achieve the targeted accuracy.  
increases with the increasing number of gradients. Therefore, the proposed FedOpt is able to support the learning scenarios with large numbers of users.