esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Data Processing  
Based on the ENVI platform, this study used the support vector machine, maximum likelihood method, and BP neural network in supervised classification methods to classify and interpret the remote sensing images of Shanghai from 1990 to 2018 [61]. By comparing the overall classification accuracy and kappa coefficients of the three classification algorithms, this paper finally selects the BP neural network algorithm, which has high classification accuracy, to obtain land use types in Shanghai. Using the BP neural network algorithm of supervised classification, we divided the land use types of remote sensing images in seven years in Shanghai into cultivated land, forestland, grassland, water bodies, built-up land, and unused land (Figure 1, Table 2). The overall accuracy of the land use classification in the seven years reached over 87%, and the kappa coefficient was greater than 0.77, which met the requirements of this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Scatellite** & **Resolution Ratio** & **Date** \\ \hline
1990 & Landsat-5(TM) & 30 m & 13 July 1990 \\
1995 & Landsat-5(TM) & 30 m & 11 July 1995 \\
2000 & Landsat-5(TM) & 30 m & 6 June 2000 \\
2005 & Landsat-5(TM) & 30 m & 17 October 2005 \\
2010 & Landsat-5(TM) & 30 m & 24 May 2010 \\
2015 & Landsat-8(OLI) & 30 m & 3 August 2015 \\
2018 & Landsat-8(OLI) & 30 m & 26 July 2018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Remote sensing images used in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Cropland** & **Forest** & **Grassland** &
\begin{tabular}{c} **Water** \\ **Bodies** \\ \end{tabular} & **Built-Up** & **Unused** \\ \hline
\begin{tabular}{c} 0 \\ 0 \\ 0 \\ 0 \\ 0 \\ 0 \\ 0 \\ 0 \\ 0 \\ 0 \\ \end{tabular} \\
2000 &
\begin{tabular}{c} 13 July 1990 \\ 11 July 1995 \\ 16 June 2000 \\ 17 October 2005 \\ 24 May 2010 \\ 24 May 2010 \\ 3 August 2015 \\ 26 July 2018 \\ \end{tabular} \\
2018 &
\begin{tabular}{c} 13 July 1990 \\ 11 July 1995 \\ 11 July 1995 \\ 10 June 2000 \\ 17 October 2005 \\ 24 May 2010 \\ 2015 \\ 2018 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Percentage of each land use and its change over time.  
Figure 1: Map of land use in Shanghai in 1990, 1995, 2000, 2005, 2010, 2015, and 2018.
## 2 Materials and Methods
### Cough Data Processing
#### 2.2.3 Classification Modeling of Cough Sound  
A classification model was built using Gaussian Mixture Model-Universal Background Model (GMM-UBM). Based on this model, classification per diagnosis was performed by calculating the likelihood ratio (LR) [28]. The probability density function for the UBM was estimated using a Gaussian Mixture Model (GMM), for an optimal fit to the data. Both voluntary cough models (from asthmatic and healthy children) were then created from this UBM by adapting it towards a better fit for the normal-voluntary cough data and asthmatic cough data, respectively. These adaptations were achieved via a maximum a posterior (MAP) procedure [29; 30]. Here, we built such a classification model based on the MFCCs and CQCCs separately. Finally, LR results from each of these were combined, and referred to in this report as the fused model.  
In order to classify unknown audio samples, the corresponding cough audio features were compared against both the normal-voluntary cough model and the asthmatic cough model. Here, LR was calculated as  
\[\text{LR}=\frac{\text{p}\Big{(}\text{E}/\text{H}_{\text{Normal-voluntary cough}}\Big{)}}{\text{p}\Big{(}\text{E}/\text{H}_{\text{ Asthmatic cough}}\Big{)}}\]  
where \(\text{p}\Big{(}\text{E}/\text{H}_{\text{Normal-voluntary cough}}\Big{)}\) is the conditional probability of E (the evidence) given the hypothesis (\(\text{H}_{\text{Normal-voluntary cough}}\)) that a cough sample is a normal-voluntary cough, while \(\text{p}\Big{(}\text{E}/\text{H}_{\text{Asthmatic cough}}\Big{)}\) is the conditional probability of evidence given the hypothesis (\(\text{H}_{\text{Asthmatic cough}}\)) that a cough sample is asthmatic.  
LR >> 1 supports the normal-voluntary cough hypothesis while LR << 1 supports the asthmatic cough hypothesis. We further calculated the Log-Likelihood-Ratio (LLR) from this LR, as \(\text{LLR}=\log 10(\text{LR})\). Positive LLRs support the normal-voluntary cough hypothesis and negative LLRs support the asthmatic cough hypothesis. The LLR magnitude indicates the strength of support towards the respective hypothesis [31; 32].
# Key Technologies for THz Wireless Link by Silicon CMOS Integrated Circuits
## 3 Features and Applications of 300 GHz Band Communication
### Application Example of 300 GHz Band Communication  
In IEEE 802.15.3d, the standard for channel allocation in the 300 GHz band has been established [28]. In this standard, channels are allocated to frequency bands close to 70 GHz from 252 GHz to 321 GHz (Figure 17), which are composed of frequencies already allocated for communication and frequencies expected to be allocated to communication in the future. To maintain compatibility with the conventional 60 GHz band standard, a plurality of channel bands that are multiples of 2.16 GHz, as shown in Figure 17a,b, are allocated to this frequency band. On the other hand, considering that the data rate of wireless communication is close to optical communication, it is preferable to consider compatibility with wired communication standards. For example, the 28 Gbit/s standard is widely used in NRZ (non-return to zero) transmission called CEI-28G (Common Electrical Interface 28 Gigabit-per-second) for electrical interfaces of FPGAs (field programmable gate arrays) and optical fiber. Assigning these two NRZ signals to the I/Q signals used for quadrature phase shift keying (QPSK) results in 56 Gbit/s wireless communication. Since the symbol rate is 28 Gbaud (gigabaud), when it is modulated with a roll-off coefficient of 0.2, it becomes a bandwidth of 33.4 GHz. According to the IEEE 802.15.3 d standard, a channel suitable for this band is not currently defined. However, allocating 34.56 GHz, which is 16 times 2.16 GHz, to one channel is suitable for connecting the 28 Gbps NRZ wired signal to the 56 Gbps QPSK radio signal. As long as the channel bandwidth is 34.56 GHz, as shown in Figure 17c, two channels can be allocated to the entire frequency band specified by IEEE 802.15.3 d. Assigning channels in this way, the channel on the low frequency side from 252 GHz to 287 GHz has a long distance of about 2 km as the atmospheric attenuation becomes 10 dB, which is suitable for medium- to long-distance communication.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 3 Pattern-Reconfigurable Antennas
### Pattern-Reconfigurable Antennas Based-On Electrical Switches  
For pattern reconfigurable antennas based-on electrical switches, the reconfigurability is achieved by connecting the adjacent radiators or parasitic elements to the main patch, switching between two types of the antennas combined in the main structure. In Reference [81], the four-element circles are located at 0\({}^{\circ}\)/+360\({}^{\circ}\), +90\({}^{\circ}\), +180\({}^{\circ}\) and +270\({}^{\circ}\) and are connected by four arms to the center. The antenna's patterns are reconfigured to four different directions of 0\({}^{\circ}\), +90\({}^{\circ}\), +180\({}^{\circ}\), and +270\({}^{\circ}\) by using four RF switches which are placed on the arms between the center and the radiation circles. The antenna's conductive parts are made of ShieldIt Super textile and are placed on Felt as a substrate. A similar approach has been used with the same research group in [96]. The antenna consists of a centre patch and four radiating elements located at 0\({}^{\circ}\)/+360\({}^{\circ}\), +90\({}^{\circ}\), +180\({}^{\circ}\) and +270\({}^{\circ}\) which are connected to the center by four PIN diodes. The same material in [81] is employed in this antenna as well. The radiation pattern of antenna is doughnut-shaped and the maximum gain is at 0\({}^{\circ}\)/+360\({}^{\circ}\), +90\({}^{\circ}\), +180\({}^{\circ}\) and +270\({}^{\circ}\) based on switches' statuses. The antenna in [82] comprises of a circular monopole patch, three parasitic patches, and two parallel symmetrical parasitic branches embedded with two PIN diodes which operates between 2.40 GHz and 2.48 GHz. The antenna's pattern is reconfigured to the beam in the direction 30\({}^{\circ}\), 330\({}^{\circ}\) in the azimuthal planes by controlling the two diodes. The antenna is fabricated on Rogers 5880 substrate with \(\varepsilon_{r}\) = 2.2 and a thickness of 0.127 mm.  
In Reference [85], a bent dipole and a loop are combined in one structure and the radiation patterns of the two antennas are canceled or compensated by using two diodes. This antenna was fabricated on flexible polyimide with \(\varepsilon_{r}\) = 3.6 and loss of 0.01. The beam direction is steered to +50\({}^{\circ}\), 0\({}^{\circ}\), and -50\({}^{\circ}\) in the azimuth direction at 2.5 GHz (Figure 4). In Reference [97], a top-loaded monopole antenna and a loop antenna are combined in a one structure. Three antennas with different top-loading angles are proposed and two diodes are placed on antenna. For antenna with angle of 90\({}^{\circ}\), the beam is steered to two directions of -90\({}^{\circ}\) and +90\({}^{\circ}\). The antenna is fabricated on FR-4 substrate and operates at 2.4 GHz for Fitbit Flex Wristband.  
Figure 4: Fabricated prototype of the pattern-reconfigurable antenna on flexible polyimide in [85].
# Utilizing Sentinel-2 Satellite Imagery for LULC and NDVI Change Dynamics for Celephu, Bhutan
## 2 Materials and Methods
### Image Classification Methods
#### 2.3.1 The SCP and Random Forest Classifier
If the results are unsatisfactory, the user can refine the training areas, extract additional features, or adjust the classification parameters to improve the classification accuracy. It is important to note that while SCP does not strictly follow the traditional training and test set approach, it does leverage custom training data to build a classification model and iteratively refine it to allow for accurate image classification and land cover mapping. Colour composites (Figure 3) were utilized during the creation of training data to enhance the interpretation and discrimination of land classes. By combining different bands, colour composites provide a visually appealing representation of the land cover features, allowing for better identification and selection of ROIs. Essentially, the different colour composite images highlight unique spectral signatures that correspond to the different land cover classes, allowing for accurate delineation and representation of each class within the training dataset. In particular, the use of colour compositions facilitated the identification of representative spectral signatures of each class for training the supervised RF classifier. The RF uses the labelled training data resulting from the generated ROIs to accurately train a model to classify the associated pixels in the image under each predefined land class.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 1 Introduction
### Notation  
In this paper, \([\mathbf{a}]_{i}\) and \([\mathbf{A}]_{i,j}\) denote the \(i^{\text{th}}\) element of a vector \(\mathbf{a}\) and the \((i,j)^{\text{th}}\) entry of a matrix \(\mathbf{A}\), respectively. Estimated values are denoted with the hat symbol \(\widehat{\cdot}\). The amplitude of a complex number \(z\) is denoted by \(|z|\), \(z^{*}\) is the complex conjugate of \(z\), and \(\angle z\) is its phase. The Hadamarad product is denoted by \(\odot\).
## 2 Methods
### Fall Simulation
#### 2.2.2 Joint Motion Simulation  
First, joint torques of the entire experimental movement, including the harness-supported phase, were calculated through inverse dynamics computations. Given that the supporting harness was omitted in the simulation, the inverse dynamic simulation overestimated the joint torques. Therefore, a forward dynamics analysis with an upper limit on the exerted torque was then performed. The torque acting on each joint was formulated as in Equation (1).  
\[\mathbf{\tau}=\mathbf{K_{p}}(\mathbf{\theta}^{ref}-\mathbf{\theta})+\mathbf{K_{d}}(\mathbf{\dot{\theta} }^{ref}-\mathbf{\dot{\theta}})+\mathbf{b}(\mathbf{\theta},\mathbf{\dot{\theta}}) \tag{1}\]  
This is the sum of control torque calculated using Proportional-Differential controller and gravity and inertia torque. The first and second terms were calculated based on the reference joint pattern, which was the joint movement obtained in the experiment. The third term, which consists of gravity and inertial torque, was calculated using the multibody theorem. The fixed-step, stiff solver (ode14x) with a step time of 0.01 s was used for kinematic calculations.  
The determination of the proportional (\(\mathbf{K_{p}}\)) and derivative (\(\mathbf{K_{d}}\)) gains was executed through a methodical trial and error approach. Specifically, we performed a forward dynamics analysis on every experimental trial conducted, without setting any limits on joint torques. This comprehensive analysis aimed to identify gain values and their combinations that consistently resulted in high reproducibility across trials. Gains \(\mathbf{K_{p}}\) and \(\mathbf{K_{d}}\) are presented in Table 2. Owing to the torque limit that reflected the physical limit of humans, the joint pattern could not be completely reproduced under the physical conditions of the elderly and young.  
Due to insufficient understanding of the fall mitigation mechanism, it is challenging to establish an algorithmic posture control policy. Thus, in this study, we focused on the ankle, knee, and hip joints of the supporting leg and lumbar joint, which substantially influence the descent velocity of the body. The forward dynamics method was applied to these joints. In contrast, the joint patterns observed in the experiments were directly applied to the other joints. During the fall movements, participants spread their arms to the sides and moved them forward and downward to support their bodies. This motion appears reasonable even if the harness support does not exist. Thus, the effect of harness support on the upper limbs was ignored in this study.
# 1-Bit Reconfigurable Transmitarray Antenna with Out-of-Band RCS Reduction +
## 4 Experimental Verification
### Antenna Prototype and Measurement Setup
Second, the influence of the multipath effect should be reduced as much as possible when measuring the RCS reduction performance of the antenna. Therefore, a window function was added to the echo signal to filter out the multipath signal.
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 4 Construction of Access Control Model  
In this section, we propose the construction of an access control model based on logistics business collaboration semantics. Firstly, ontology construction process was described, followed by logistics business collaboration ontology and semantic access policy set. Finally, the use of inference machines to optimize access control rules was discussed.
In this paper, a method combining a nonlinear disturbance observer and NTSM control is proposed for an underactuated USV, to deal with the trajectory tracking control problem under external environmental disturbances. In addition, in order to demonstrate the effectiveness and performance of the derived control strategy, a straight-line trajectory and a circular trajectory are proposed for simulation verification. The main contributions of this paper are:1. An NTSM controller integrating a nonlinear disturbance observer is proposed to solve the trajectory tracking problem under the environmental disturbances of wind, waves, and currents. The stability of the observer and controller is proved strictly using the Lyapunov theory. 2. The non-singular terminal sliding mode control method proposed in this paper results in a dynamic system with a better performance than that of the classic sliding mode controller. To improve the robustness of the control system, a disturbance observer is also designed to observe unknown disturbances.
## 4 New Skyhook and Displacement-Velocity Control Method
### Design and Analysis of New Displacement-Velocity Controller
Given that the DV control works more effectively in the region of the second-order resonance frequencies, particularly the frequency region associated with the velocity region (0.025-0.1 m/s, Figure 2a), we divided this region into two regions (0.025-0.05 m/s) and (0.05-0.1 m/s) for the new DV control. Figure 6 presents a comparison of the transmissibility of vibrations from the rail to the train body under the three control methods. Overall, the conventional DV controller exhibited superior vibration isolation performance at high frequencies compared with the passive controller. However, its effectiveness was lower in three frequency regions: (1) low frequencies (less than 1.5 Hz); (2) near the unstable frequency point (6.6 Hz); and (3) near the resonance frequency of the bogie (14.6 Hz). Compared with the conventional DV controller, the new DV controller exhibited higher vibration isolation performance at low frequencies, eliminated the unstable phenomenon observed at approximately 6.6 Hz, and substantially mitigated vibrations at frequencies near the resonance frequency of the bogie. Figure 5: Damping contribution coefficient for the new DV control according to the vibration velocity of the carbody. Figure 7 presents the displacement, velocity, and acceleration of the train body under the three types of control. Overall, the carbody displacement under the new DV control was lower than that under conventional DV control across the entire frequency domain [Figure 7a]. The new DV controller notably reduced the vibration velocity of the carbody at both low and high frequencies but did not result in the instability at approximately 6.6 Hz that was observed with the conventional DV controller [Figure 7b]. The trends in carbody acceleration were similar to those in carbody velocity under the three control algorithms [Figure 7c]. Compared with the conventional DV controller, the new DV controller significantly reduced the vibration velocity and acceleration of the carbody by 35.11% and 28.34%, respectively, at the frequency of 14.6 Hz. Figure 7: The RMS of (**a**) displacement, (**b**) velocity, and (**c**) acceleration of the carbody under sinusoidal signal input with amplitudes of \(\pm\)5 mm.
## 3 Results
### Sorbents Characterization
#### 3.1.4 FTIR Characterization  
The vibrational spectrum of the sorbents was investigated using FTIR spectroscopy technique, in order to determine characteristics of each surface. The vibrational spectrum of activated charcoal is presented in Figure 1.  
The broad absorption band at 3600-3200 cm\({}^{-1}\), with a maximum at about 3440 cm\({}^{-1}\), due to the O\(\backslash\)H stretching \(\
u\)(O\(\backslash\)H) indicate the presence of hydroxyl groups from alcohols, phenols or carboxyls, adsorbed in the activated carbons. The band at 2800-3000 cm\({}^{-1}\) could be assigned to stretching of methyl groups \(\
u\)(C\(\backslash\)H). The spectrum shows a pronounced band at 1631 cm\({}^{-1}\), that reveals the C=C stretching vibration in the structure of the activated carbon. The band at 1000-1300 cm\({}^{-1}\) usually has been assigned to C-O stretching in alcohols, phenols, acids, ethers and esters groups [20,21] The peak at 1162 cm\({}^{-1}\) indicates the stretching mode of hydrogen-bonded P = OOH groups from phosphates or polyphosphates. The peak at 1081 cm\({}^{-1}\) could be determined by the symmetrical vibration in polyphosphate chain P-O-P and P\({}^{+}\)-O\({}^{-}\) in acid phosphate esters [22]. The FT-IR spectroscopy result indicates that the activated charcoal is rich in surface functional groups.  
\begin{table}
\begin{tabular}{l l} \hline Pore diameter & Pore distribution, \% \\ \hline
5–10 Å & 0 \\ \hline
10–15 Å & 9.67 \\ \hline
15–25 Å & 25.85 \\ \hline
25–50 Å & 28.41 \\ \hline
50–100 Å & 10.81 \\ \hline
100–300 Å & 25.26 \\ \hline
300–1000 Å & 0 \\ \hline
1000–10,000 Å & 0 \\ \hline
10,000–75,000 Å & 0 \\ \hline \end{tabular}
\end{table}
Table 6: Pore size distribution of natura Na-montmorillonite [7].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Chemical Composition** & **ZrO\({}_{2}\)** & **TiO\({}_{2}\)** & **CaO** & **K\({}_{2}\)O** & **Fe\({}_{2}\)O\({}_{3}\)** & **Na\({}_{2}\)O** & **Al\({}_{2}\)O\({}_{3}\)** & **SiO\({}_{2}\)** \\ \hline Raw clay weight \% & 0.083 & 0.607 & 1.94 & 1.97 & 3.37 & 7.33 & 13.4 & 71.3 \\ \hline \end{tabular}
\end{table}
Table 7: Chemical analysis of natural mineral clay [7].  
The vibrational spectrum of natural Na-montmorillonite is illustrated in Figure 2.  
The presence of hydroxyl groups from Fe-OH-Al and Al-OH-Al is indicated by the OH broadband which exhibits the maximum at the wave number of approximatively 3409 cm\({}^{-1}\). The presence of bending H-O-H vibration is suggested by the band that arises at 1632 cm\({}^{-1}\). At the wave number of approximatively 899 cm\({}^{-1}\), is observed the band corresponding to Al-Al-OH. Absorption bands at 773, 1116 and 1013 cm\({}^{-1}\) originating from the stretching and bending vibrations of SiO\({}_{2}\) tetrahedral indicate that the bending mode of Si-O is present in the silicate structure [23]. SiO stretching vibrations observed at 773 cm\({}^{-1}\) support the presence of quartz. The wave number of approximatively 1428 cm\({}^{-1}\) indicate the presence of calcium combined with carbonate species and reveals the existence of calcite [7].
## 2 Materials and Methods
### Characterization of the Imaging Performance
#### 2.5.3 Image Contrast  
Phantom Ph2 contained three anechoic structures in a speckle-generating medium that consisted of cylindrical 5-mm-diameter holes, invariant along the y-axis. To assess the detectability of this cyst size, we chose the simplest and most general contrast estimator used in ultrasound imaging [30]:  
\[C0_{dB}=10 log_{10}\frac{\mu_{c}}{\mu_{s}}=10 log_{10}\frac{\frac{1}{N}\sum_{i= 1}^{N}c_{i}^{2}}{\frac{1}{N}\sum_{i=1}^{N}s_{i}^{2}}, \tag{9}\]  
where \(\mu_{c}\) is the mean signal power in the cyst area and \(\mu_{s}\) is the mean signal power in the speckled background area. The cyst statistics and the background statistics are assessed on the same number of pixels \(N\) = 1000, equivalent to about 30 resolution cells to guarantee reliable statistics. The variables \(c_{i}\) and \(s_{i}\) denote the value of ith pixel of the cyst area and of the background area, respectively. The background area was chosen distant from the three holes. As previously, the contrast was computed using the slice at y = 0 and the two closest uncorrelated slices (xz images). Each slice was a disk with a diameter limited to 25 \(\times\)\(\lambda_{\rm c}\).  
As for estimation of the LSF based on \(Cc\), a contrast curve was plotted by considering different and non-overlapping cyst areas of size \(N\), all centered on the longitudinal axis of the cylindrical hole. The center-most area was a disk and the areas beyond this disk were concentric rings.