esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G-MEC Testbeds for V2X Applications
## 2 Background
### Aspects and Features of 5G  
5G is growing as the new reference architecture in the telecommunication industry. The 5G network introduces modular Network Functions (NFs) for the control plane and user plane in both the Access Network (AN) and Core Network (CN) [15]. NFs provide specific network capabilities according to the application requirements. 5G can support different vertical sectors and industries with machine- and human-type communication  
Figure 1: Map and layout of this paper.  
and is expected to provide native support for MEC with its low latency and high network efficiency.  
The main usage scenarios of 5G are divided into three categories [16]. The first is eMBB, which provides a data rate higher than 4G accompanied by moderate latency improvements. eMBB supports the development of use cases such as 360-degree streaming video, Augmented Reality/Virtual Reality (AR/VR) media, and UltraHD applications. mMTC provides connectivity to an enormous number of devices. 5G will replace the previous technologies such as LTE-M, Low-Power Wide Area Network (LPWAN) technologies, and NarrowBand Internet of Things (NB-IoT). URLLC requires the 5G core deployment to reduce end-to-end (E2E) latency, accommodates emerging services and applications with high-reliability requirements, and enables the support of applications such as remote control and autonomous driving.  
ITU-R M.2410 [17] presents the 5G requirements for eMBB, mMTC, and URLLC applications. The user plane latency required by eMBB and URLLC is 4 ms and 1 ms, respectively. In addition, eMBB requires a 20 Gbps downlink data rate and mMTC requires a connection density of \(10^{6}\) devices per km\({}^{2}\).  
The core of the 5G system is the service-based architecture (SBA), which defines the interactions between NFs responsible for the control plane and data plane procedures. Figure 2 depicts the non-roaming 5G system reference architecture. The bottom layer of the architectural elements is involved in transporting the user data, whereas the upper layer includes the essential NFs within the control plane. The NFs within the 5G reference architecture are Access and Mobility Management Function (AMF), Session Management Function (SMF), Network Slice Selection Function (NSSF), Network Repository Function (NRF), Unified Data Management (UDM), Policy Control Function (PCF), Network Exposure Function (NEF), Authentication Server Function (AUSF), and User Plane Function (UPF). 3 GPP TS 23.501 [18] presents the functionalities and responsibilities in detail. In the 5G system, the separation of user plane functions and control plane functions leads to flexible deployment and scalability. The 5G system model is defined to enable technologies such as SDN and NFV and to provide data connectivity.
## 3 Energy Consumption Mathematical Model  
We considered the first order radio model [42] in our experiments for the energy consumption calculation used for data communication. For path losses calculation, two channel models are used in this energy consumption model: One is a free space model and the other is multipath fading [14; 37]. The selection of any model for calculating the energy consumption is made on the basis of distance between transmitter and receiver. The free space model is used when the distance between transmitter and receiver is less than or equal to the threshold \(S_{0}\), otherwise multipath fading model is selected for computing energy consumption. The energy consumption for transmission of each packet is calculated by:  
\[E_{Tx}(l_{bits},S)=\left\{\begin{array}{ll}l_{bits}*E_{fs}*S^{2}+l_{bits}*E_{ elec}&S\leq S_{0}\\ l_{bits}*E_{mp}*S^{4}+l_{bits}*E_{elec}&S>S_{0}\end{array}\right. \tag{1}\]  
where \(E_{Tx}\) is the energy consumption for transmission, \(l_{bits}\) is a length of packet (i.e., number of bits in each packet), and the distance between transmitter and receiver is \(S\). \(E_{elec}\) is the energy consumption due to the transmitter and receiver circuit to process the data before sending or receiving while \(E_{fs}\) and \(E_{mp}\) are dependent on transmitter amplifier model. \(S_{0}\) is the transmission threshold which is defined as:  
\[S_{0}=\sqrt{E_{fs}/E_{mp}}. \tag{2}\]  
Energy consumption during reception of data packet is calculated as:  
\[E_{Rx}=l_{bits}*E_{elec}. \tag{3}\]
# Characteristics of Removal of Lead, Cadmium and Chromium from Soil Using Biosorbent and Biochar
## 2 Materials and Methods  
### Materials  
The natural sorbent used was aspen wood sawdust (AW), which is a by-product of mechanical wood processing. This lignocellulosic biomass mainly contains cellulose, hemicellulose, and lignin. Due to its compact and rigid structure, the sawdust is resistant to chemical and biochemical degradation [24]. Sawdust is utilized as an adsorbent because the cellulose and lignin it contains have hydroxyl, carboxyl, and phenolic groups, which facilitate the binding of metal cations [25]. The chemical composition of the analyzed, dry sawdust (with a diameter of 2.5 mm) includes cellulose (48%), lignin (18%), hemicellulose (20%), extractives (11%), and minerals (ash 3%) [24].  
High-quality flower soil, a natural organic mixture designed for growing flowers with moderate nutritional requirements, was used in the studies. The main component of this soil is high-quality peat, and it is additionally enriched with micro- and macroelements. The pH of the substrate is maintained in the range of 5.5-6.5.  
As a comparative material, granular activated carbon (AC) 4-8 mm pure p.a. was also used, with a minimum proper fraction of 80%, a methylene number of at least 7 cm\({}^{3}\), drying losses of up to 12%, ash content of up to 8%, and a water extract pH of at least 8 (Avantor, Gliwice, Poland). Reagents used in the studies, such as K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\), Pb(NO\({}_{3}\))\({}_{2}\), and Cd(NO\({}_{3}\))\({}_{2}\)\(\cdot\)4H\({}_{2}\)O, were of pure analysis grade (Sigma-Aldrich, Steinheim, Germany). All solutions used in the experiment were prepared with deionized water, which was also used to maintain constant soil moisture.
# A Wide-Ranging NMR Probe for Multiscale Structures
## 3 Hyperpolarization of \({}^{129}\)Xe  
Thermally polarized \({}^{129}\)Xe has been employed as the conventional probe since the first works in the 1980's. Samples with high surface area and/or high xenon adsorption capacities were analyzed with good signal-to-noise ratios without the need of excessively long acquisition times. Variable pressure studies were also conducted to extract the chemical shift of xenon interacting with the surface, \(\delta_{\text{S}}\), often reaching low xenon pressures. In these conditions, the \({}^{129}\)Xe NMR analysis of materials with low surface area or weak xenon adsorption was strongly limited by the low sensitivity of the technique.  
In 1991, [PERSON] et al. reported the successful use of optical pumping of gaseous Rb atoms to strongly enhance the polarization of \({}^{129}\)Xe nuclei [42]. This is one of the first and most important examples of hyperpolarized \({}^{129}\)Xe NMR. Nowadays, hyperpolarization allows the sensitivity of \({}^{129}\)Xe NMR to be enhanced up to \(10^{4}\) times compared to the use of thermally polarized xenon. In addition to that, the longitudinal relaxation times can be greatly reduced. These two combined features allow to study a wide variety of materials with low acquisition times and high sensitivity.  
Hyperpolarized \({}^{129}\)Xe can also transfer its spin polarization to other nuclei, such as \({}^{1}\)H and \({}^{13}\)C [43; 44]. This phenomenon can be exploited to characterize functionalized surfaces and in magnetic resonance imaging (MRI).  
There are several methods to obtain hyperpolarized (HP) \({}^{129}\)Xe. A brief description of the different possibilities is given below. Further details can be found in the literature [30; 31; 45].
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 1 Introduction
### Continuy Scanning Strategy for Damage Detection  
The CSLDV system contains a single head scanning vibrometer and an extra National Instruments (NI) and PCI extensions for Instrumentation (PXI) system for providing control signals to manipulate the two orthogonal scanning mirrors to cover the scanning rectangle area. Generally, there are two continuously scanning strategies: The Lissajous and the Zigzag, as shown in Figure 1. For Lissajous scanning, the sinusoidal signals with two different frequencies are used to drive the orthogonal scan mirrors. The main purpose of demodulation is to obtain the sideband information and fit the mode shape of the plate using polynomial functions based on the sideband spectral data. However, the changes of mode shape caused by the damage or crack are usually local and could be easily treated as noise and eliminated through the mode shape fitting process. On the contrary, for the Zigzag scanning, the control signals to the scanning mirrors are triangular waves or saw tooth signals in order to make the laser spot scan along a line path and span a rectangle scan area. The Zigzag scanning is essentially a uniform straight-line scan method. The measured mode shape can be directly obtained through the demodulation of vibration signals with spatial position transformed to the time axis. Then, the mode shape can be realized by plotting this demodulated signal as a three-dimensional surface against the measurement position, which is derived from the simultaneously recorded mirror drive signals.  
Here, the uniform rate continuously scanning method is introduced to perform a rectangle area measurement. The boundaries of the scan area are \(x\in[x_{\text{min}},x_{\text{max}}],y\in[y_{\text{min}},y_{\text{max}}]\). The spatial resolution of measured mode shape along the scanning direction is determined by the sample frequency and the scan frequency. In the x direction, the sample frequency \(f_{\text{s}}\) and the scan frequency \(f_{x}\) can be expressed as  
\[SR_{x}\ =\ 2(x_{\text{max}}-x_{\text{min}})f_{x}/f_{s} \tag{7}\]  
In the y direction, it can be treated as stepped scan and the real spatial resolution can be expressed as  
\[SR_{y}\ =\ (y_{\text{max}}-y_{\text{min}})/(f_{x}/(2f_{y})) \tag{8}\]  
In order to cover the measured plate as much as possible, the scan parameters should be reasonably adjusted according to geometrical characteristics of the test structure and requirement of the measurement. In general, lower scan frequency and higher sample frequency will lead to high resolution of the mode shape data. But it will take a longer scanning time and will generate a large amount of vibration signal data, which makes data demodulation more difficult.
## 2 Material and Methods
### Time-Domain Methods for V-I Characteristics of EAF Arcs
#### 2.3.3 Exponential-Hyperbolic Model (Model 3)  
The exponential-hyperbolic model is characterized by a synthesis of exponential and hyperbolic models. This model represents the V-I characteristic structure of the electric arc as a hyperbolic function during the current increase, and as an exponential function during the current drop. This approach is mathematically expressed by Equation (3) [9; 15]:  
\[V(i)=\begin{cases}\Big{[}V_{at}+\frac{C_{i,d}}{D_{i,d}+\mid i\mid}\Big{]}\cdot sign (i),&\frac{di}{dt}\geq 0\text{ and }i>0\\ V_{at}\cdot\bigg{(}1-e^{\mid\frac{i}{0}\mid}\bigg{)}\cdot sign(i),&\frac{di}{dt}< 0\text{ and }i<0\end{cases} \tag{3}\]  
In Equation (3), \(V\) is the arc voltage, \(V_{at}\) is the threshold voltage dependent on the arc length, \(i\) is the arc current, \(i_{0}\) is a steepness factor that controls the rate of voltage decay, and \(C_{i,d}\) and \(D_{i,d}\) are constants that define the shape of the voltage-current curve. The mathematical representation of the \(V_{at}\), contingent upon the arc length, is delineated by Equation (4) [48]:  
\[V_{at}=A+B\cdot l \tag{4}\]  
In Equation (4), \(A\) is a constant value determined by the anode and cathode voltage dips, approximately 40 V, \(B\) is the voltage value contingent upon the arc length, empirically established as \(B=10\) V for each centimeter of arc length, and \(l\) denotes the arc length.
## 2 Antenna Design and Performance  
### Single Ka-Band Patch Antenna Design and Performance  
Figure 1a shows the geometry of a single patch antenna for the Ka-band. Two substrates are stacked for aperture-coupled feeding to enhance the bandwidth of the patch antenna [7]. All substrates are Rogers RT/duroid 5880 (\(\varepsilon_{\mathrm{r}}\) = 2.2, tan \(\delta\) = 0.0009). As shown in Figure 1c, a patch antenna is located on the top side of the stacked structure, and two orthogonal microstrip lines and slots are located at the bottom and middle of the structure, respectively. The detailed view of the antenna design is shown in Figure 1b. As shown in Figure 1d, dual polarization is achieved by exciting Ports 1 and 2. Figure 1e shows the simulated S-parameters of the proposed single patch antenna. The \(-\)10 dB reflection coefficient (S\({}_{\mathrm{NN}}\)) bandwidths are 2.87 GHz (33.67-36.54 GHz) and 3.28 GHz (33-36.28 GHz) when Ports 1 and 2 are excited, respectively. The operating frequency in the Ka-band ranges from 34 to 36 GHz, and the simulated result shows that the designed antenna fully covers the target band with dual-polarization characteristics. Figure 1f,g show the simulated radiation patterns for each excitation. When Ports 1 and 2 are excited, the gains in the \(+\)z direction are 5.74 and 5.84 dBi, respectively. The front-to-back ratios (FBRs) are 28.04 and 19.29 dB with Port 1 and 2 excitations, respectively.
# Multiple Instance Learning with Trainable Soft Decision Tree Ensembles
## 5 Soft Tree Ensemble for MIL
### Soft Tree Ensemble  
A key idea behind STE-MIL can be represented in the form of the following schematic algorithm:  
1. Let us assign incorrect labels to instances of a bag; for example assigning the same label as that of the corresponding bag. The instance labels may be incorrect because we do not know true labels and their determination is our task. However, these labels are needed to build an initial RF. This is a kind of initialization procedure for the whole model, which is trained in the end-to-end manner.
2. The next step is to convert the initial RF to a neural network having a specific architecture. To implement this step, non-leaf nodes of each tree in the RF are parametrized by trainable parameters \(\mathbf{b}\), \(\tau\), \(\omega\), and non-trainable parameters \(\mathbf{W}\).
3. Parameters of the tree nodes \(\mathbf{b}\), \(\tau\), \(\omega\) are updated by using the stochastic gradient descend algorithm to minimize the bag loss defined in (5). To implement the updating algorithm, we propose approximating the tree path indicators \(q_{l}(\mathbf{x},\mathbf{W},\mathbf{b},\tau,\omega)\), by using the specific softmax representation (11). This is a key step of the algorithm which allows us to update trees by updating neural networks and incorporating trees or the RF in the whole scheme of modules, including the attention mechanism and a classifier.  
Suppose that the RF consisting of \(T\) decision trees has been trained on the repeated instance labels (12). We convert its trees to a set of \(T\) neural networks which implement functions \(f^{(1)}(\mathbf{x}),\ldots,f^{(T)}(\mathbf{x})\), such that the \(i\)-th tree corresponds to the \(i\)-th network implementing the function \(f^{(i)}(\mathbf{x})\). After converting trees to neural networks, we can update their parameters to minimize bag-level loss (5). The ensemble prediction for a new instance \(\mathbf{x}\) is defined as follows:  
\[f(\mathbf{x})=\frac{1}{T}\sum_{i=1}^{T}f^{(i)}(\mathbf{x}). \tag{13}\]  
The bag prediction can be obtained by applying any aggregation function \(G\):  
\[f(\{\mathbf{x}_{k}^{(i)}\}_{i=1}^{N})=G(f(\mathbf{x}_{1}^{(i)}),\ldots,f( \mathbf{x}_{n_{i}}^{(i)})). \tag{14}\]  
The next question is how to convert the decision trees into neural networks.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Applications of LoRaWAN
#### 3.1.4 Health  
Health is another area in which LoRAWAN technology is frequently applied. In [31], the use of LoRaWAN is proposed to monitor the health of workers and maintain efficiency during their work activity, analyzing working environment factors such as temperature, light, CO\({}_{2}\) concentration, pressure, etc. On the other hand, in [6], a theoretical model is proposed to create a wireless monitoring device, which sends electrocardiogram and other data by applying machine-to-machine communication. Further, in [32], the creation of a device that monitors physical activity in elderly people is proposed, integrating an IMU (inertial measurement unit) to the sensors. Also, in [17], the implementation of an alert system for mine workers is proposed, taking into account the data obtained from smoke sensors and using respiratory and cardiac data. The study presented in [33] deals with a solution based on IoT devices and the deployment of several LoRaWAN gateways to geolocate vulnerable people, in which the location can be obtained at a range of 40 to 60 m.
# Simple Degree-of-Freedom Modeling of the Random Fluctuation Arising in Human-Bicycle Balance
## 3 Fluctuation Model of the Human-Bicycle Balance
### A Human-Bicycle Fluctuation Model  
In view of our setup in Figure 2, we model the human-bicycle mechanics by a simple inverted pendulum about the contact point of the bicycle wheel. Based on the time series statistics in Table 1,  
Figure 4: The measured joint probability density functions (PDFs) from all participants (\(s=1,\cdots,8\)).  
we also assume that the roll angle \(\theta\) about the equilibrium is sufficiently small; the maximal three standard deviation indicates \(3\times\max_{s}SD^{(s)}[\theta]\approx 4.88\times 10^{-2}\) rad (or \(2.80^{\circ}\) deg). Therefore, we model the bicycle's rolling motion by a linearized inverted pendulum of the form:  
\[mr^{2}\ddot{\theta}-mgr(\theta-\hat{\theta})=T, \tag{6}\]  
where \(m\) [kg] and \(r\) [m] are the mass and length of the pendulum, respectively, \(g:=9.81\) m/s\({}^{2}\) is the gravitational acceleration, \(T\) [Nm] is a torque input, and \(\hat{\theta}\) [rad] is the equilibrium angle. The equation of motion (6) is nondimensionalized and represented in the following state-space form:  
\[\dot{x}_{1}=x_{2},\quad\dot{x}_{2}=k(x_{1}-\hat{\theta})+u, \tag{7}\]  
where \(\mathbf{x}=(x_{1},x_{2})^{T}:=(\theta,\hat{\theta})^{T}\), \(k:=g/r\), and \(u:=T/mr^{2}\).  
Next, we specify \(u\) to simulate the human fluctuation during human-bicycle motion. As successfully demonstrated in our previous study [11], some human fluctuations can be accurately simulated by the following state-feedback mechanism:  
\[u=F_{1}\{1+\sigma_{1}\xi_{1}(t)\}(x_{1}-\hat{\theta})+F_{2}x_{2}+\sigma_{2} \xi_{2}(t), \tag{8}\]  
where \(\xi_{1}(t)\) and \(\xi_{2}(t)\) are independent white Gaussian noises with zero mean and unit variance. The first coefficient \(F_{1}\{1+\sigma_{1}\xi_{1}(t)\}\) represents a random proportional gain with mean \(F_{1}\) and variance \((F_{1}\sigma_{1})^{2}\). The second coefficient \(F_{2}\) is a deterministic derivative gain. The third term \(\sigma_{2}\xi_{2}(t)\) represents an additive random perturbation.  
Finally, we substitute (8) into (7), rewrite it to reduce the dependency between parameters, and propose a human-bicycle fluctuation model of the form  
\[\dot{x}_{1}=x_{2},\quad\dot{x}_{2}=\{p_{1}+p_{2}\xi_{1}(t)\}(x_{1}-\hat{\theta })+p_{3}x_{2}+p_{4}\xi_{2}(t), \tag{9}\]  
with the generalized parameter vector  
\[\mathbf{p}=(p_{1},p_{2},p_{3},p_{4}):=(g/r+F_{1},F_{1}\sigma_{1},F_{2},\sigma_{2} )\in\mathbb{R}^{4}, \tag{10}\]  
which parameterizes the equivalent properties of human-bicycle motion. Here, \(p_{1}\) and \(p_{2}\) are a mean and a standard deviation of the randomly fluctuating stiffness of the human-bicycle motion, respectively, \(p_{3}\) is a deterministic viscous damping, and \(p_{4}\) is an additive random fluctuation strength.
# Transform a Simple Sketch to a Chinese Painting by a Multiscale Deep Neural Network
## 5 Other Applications
### Image Colorization  
The goal of colorization is not to recover the actual ground truth color, but rather, to produce a plausible colorization that the user finds useful even if the colorization differs from the ground truth color. Colorization can seem like a daunting task because so much information is lost (two out of three color dimensions) in converting a color image to its underlying grayscale representation. Because grass is usually green and the sky is usually blue, the semantics of an image scene provide many clues for a plausible colorization.  
To verify the effectiveness of the proposed method, we artificially colorize grayscale satellite imagery. The dataset used is the UC Merced Land Use dataset [40]; this dataset consists of images of 21 land use classes (\(100\)\(256\times 256\)-pixel RGB images for each class) selected from aerial optical images acquired by the U.S. Geological Survey. Given the lightness channel \(L\), our system predicts the corresponding \(a\) and \(b\) color channels of the image in the CIE Lab color space. Lab color space is an alternative system for representing pixel colors versus the standard RGB values. It is useful because the \(L\) channel is statistically independent of the pure color \(a,b\) channels. We divide the training set and test set according to the ratio of 8:2. After training, we display the resulting colorizations of our method in Figure 13, the grayscale image on the left and the artificial colorization of a grayscale image on the right. The result demonstrates that our method can produce a realistic colorization.
## 3 Related Work
### Advantages of SVC over AVC  
A few studies have focused on the comparison of AVC and SVC based streaming. [PERSON] et al. [6] show the bit rate for spatial and SNR scalability increases by 10% for SVC based streaming. However, the findings of [PERSON] et al. [17] show that replacing AVC encoded videos with SVC encoded videos saves up to 50% of the storage space. Another study by [PERSON] et al. [18] concluded that SVC results in improved caching efficiency for HTTP streaming while storing AVC representations leads to spoilage of the cache capacity. In addition, SVC-DASH needs less buffering compared to AVC-DASH and improves the quality of Experience (QoE) for client [15]. [PERSON] et al. [19] use heuristics to evaluate performance of SVC and AVC based streaming using NS3. They concluded that SVC shows decreased performance in restricted bandwidths but performs well when using a single connection in a variable bandwidth environment. [PERSON] et al. [17] give a detailed objective assessment of SVC with DASH in mobile environments with various layer setups. They suggest creating a separate SVC stream for each resolution. This provides a nice balance between the advantages of SVC and its overall coding overhead. Their important contribution was to validate the bit rate recommendations derived from industry solutions. [PERSON] et al. [20] analytically investigated the SVC layer segment selection policy. It is concluded that policy with diagonal selection is optimal that balances quality variation and quality score for variable rate while the vertical policy is optimal under fixed bandwidth.