esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the Digital Data Performance Transmission on a Fiber-Radio System
###### Abstract  
This work shows a set of numerical simulations to evaluate the digital data performance transmission for a fiber-radio system. For this goal, VPI Photonics software is used to simulate the transmission of a digital signal at a bit rate of 2.4 GHz through an optical link of 25 km of single-mode standard fiber (SM-SF). Whereas MATLAB software is utilized to emulate a wireless channel considering four main phenomena inherent to this channel, such as multipath and slow fading, co-channel interference, and additive white Gaussian noise (AWGN). The modeling carried out in MATLAB considers a user in movement within its coverage area cell. The constellation diagram, bit error rate (BER), and signal-to-interference-plus-noise ratio (SINR) statistical tests are used to evaluate the performance of this approach. This proposal finds potential applications in the field of cellular telephony, the Internet of Things (IoT), or any other communication system to evaluate the quality of the delivered signal to a user in motion within a particular coverage cell.  
fiber-radio system; wireless transmission; numerical simulations; digital signal Article
## 3 Research Methodology
### Convolutional Neural Network  
The convolutional neural network (CNN) is a highly significant and effective tool in the field of deep learning. It is classified as a type of \"feed forward neural network\" that is primarily employed in the domains of image processing and pattern recognition [34]. The architecture of a convolutional neural network (CNN) comprises several distinct layers, namely the \"input layer\", \"convolution layer\", \"pooling layer\", and the \"output layer\". The convolutional layer receives input data and conducts the convolution process through applying a filter to extract a feature map. The pooling layer is responsible for receiving feature maps from the convolution layer and performing down-sampling on these feature maps. During the pooling procedure, a group of n adjacent data points are transformed into a unified format. This is achieved through incorporating a bias term (\(\text{bx}+1\)) and a scalar weight (\(\text{Wx}+1\)) and applying an activation function. The outcome of this process is the generation of a condensed feature map. One significant benefit of convolutional neural networks (CNNs) lies in their capacity for parallel learning, which contributes to a reduction in network complexity [35]. Enhanced resilience and scalability can be attained through the implementation of the sub-sampling procedure. Equation (1) [35] is a general framework of CNNs and can be used to describe the processing of output at the layers of a CNN:  
\[C_{x,y}^{(l,k)}=\tanh\left(\sum_{t=0}^{f-1}\sum_{r=0}^{K_{h}}\sum_{c=0}^{K_{w} }W_{(r,c)}^{(k,t)}C_{(x+r,\ x+c)}^{(l-1,t)}+Bias^{(i,k)}\right) \tag{1}\]  
\(C_{x,y}^{(l,k)}\) is the generated outcome of a neuron at convolution layer \(l\), feature pattern \(k\), row \(x\), and column \(y\). \(f\) represents the number of convolution cores in a given feature data pattern. At the subsampling stage, the output of neuron at the \(lth\) subsampling layer, \(kth\) feature pattern, row \(x\), and column \(y\) is expressed in Equation (2):  
\[C_{x,y}^{(l,k)}=\tanh\left(W^{(k)}\sum_{r=0}^{S_{h}}\sum_{c=0}^{S_{m}}C_{(x \times S_{h}+r,\ y\times S_{w}+c)}^{(l-1,t)}+Bias^{(i,k)}\right) \tag{2}\]  
At the \(lth\) hidden layer H, the output of neuron \(j\) is given in Equation (3):  
\[C_{(l,j)}=\tanh\left(\sum_{k=0}^{s-1}\sum_{x=0}^{S_{h}}\sum_{y=0}^{S_{m}}W_{( x,y)}^{(j,k)}C_{(x,y)}^{(l-1,t)}+Bias^{(i,j)}\right) \tag{3}\]  
where s denotes the number of feature patterns in the subsampling layer.  
At the output layer, the output of neuron \(i\) at the \(lth\) output layer is expressed via Equation (4):  
\[C_{(l,i)}=\tanh\left(\sum_{j=0}^{H}C_{(l-1,j)}W_{(i,j)}^{l}+Bias^{(i,j)}\right) \tag{4}\]
# Stability Study of Erythrhitol as Phase Change Material for Medium Temperature Thermal Applications
## 3 Results and Discussion
### Kinetic Analysis of Latent Heat Degradation  
Kinetics of thermal degradation is usually based on Equation (2). After rearrangement and integration, Equation (2) becomes Equation (4)  
\[\int_{0}^{\alpha}\frac{d\alpha}{f(\alpha)}=k\int_{0}^{t}dt \tag{4}\]  
where the left side of the equation is the integrated form of the reaction model \(g(\alpha)\), according to Equation (5)  
\[\int_{0}^{\alpha}\frac{d\alpha}{f(\alpha)}=g(\alpha) \tag{5}\]  
Substitution of Equation (5) in Equation (4) leads to Equation (6)  
\[g(\alpha)=k\cdot t \tag{6}\]  
where \(g(\alpha)\) is the integrated reaction model. Selecting the best-fit reaction model in Equation (6) gives a straight line by plotting \(g(\alpha)\) versus time \(t\). [PERSON] et al. [36] showed that a first order reaction, expressed as \(-\ln(1-\alpha)\), is suitable to describe the degradation of the latent heat of sugar alcohols.  
As could be seen in Section 3.2, melting enthalpy values obtained from tests under an argon atmosphere remained more stable over time. The 100 h test period was rather short to observe significant latent heat changes. Therefore, the reaction kinetics for tests under an argon atmosphere were not assessed. Figures 5a and 6a depict the data obtained for tests of pure erythroid under air and with antioxidant at the three given temperatures, respectively.  
The rate constant (\(k\)) was determined from the slope of the straight line. Furthermore, the rate constants (\(k\)) were calculated at the three given temperatures and the Arrhenius parameters were determined according to Equation (3). Figures 5b and 6b show the Arrhenius plot for tests of pure erythroid under air and erythroid with antioxidant, respectively.  
Reaction rates (\(k\)) obtained for erythroid under air increased when the temperature increased. These changes were more evident in samples of pure erythroid. Temperature is a factor that affects reaction rates, i.e., an increase in temperature normally increases the rate of reaction. The temperature dependency of reactions is in turn determined by the activation energy. Activation energy (Ea) values of 41.65 kJ/mol and 5.55 kJ/mol were calculated according to Equation (3) for tests of pure erythroid under air and erythroid with antioxidant, respectively.  
Even though the reaction rates (\(k\)) of erythroid with antioxidant tend to increase with increasing temperature as shown in Figure 6a, the effect is not as considerable as it is in pure erythiritol samples. Antioxidant was found to diminish the thermal degradation during heating under air and its corresponding samples were less temperature sensitive than those without any added additive. Temperature-sensitive reactions have high activation energies, that is, the higher the activation energy, the faster the reaction is by increasing temperature.
## 4 Time-Frequency-Space Resource Allocation Method in Sharing Network
### General Framework  
In the context of a multi-user, multi-carrier, and multi-beam network, achieving network-level optimal performance and maximizing system-level capacity to reach the system limit are prerequisites for achieving the best overall user experience. As shown in Figure 6, when a user is in Frequency Band 1 of Operator A (service cell) and detects Frequency Band 2 of Operator B (adjacent cell), a decision is made on whether to perform inter-frequency handover to enhance the overall user experience. Therefore, we propose a resource allocation approach with the goal of maximizing network-level user experience. To achieve this optimization objective, time, frequency, and spatial resources need to be collaboratively taken into account.  
The framework of the time-frequency-space resource allocation method is shown in Figure 7. The input information of the framework uses features from service cells and  
Figure 5: Multi-user network-level experience maximization.  
Figure 6: Schematic diagram of air interface information prediction between different frequency bands.  
co-frequency neighbor cells. The output information is the optimal resource allocation scheme. By combining network-level optimization algorithm modules with smart grid technology modules, this framework aims to maximize the network-level user experience in a multi-user, multi-carrier, and multi-beam context. Specifically, we set up an optimization model aiming at the optimal network-level user experience. To determine the time, frequency, and spatial parameters that maximize network performance, we introduce an air interface information prediction method based on smart grid technology, which constructs fingerprint maps using intelligent techniques with features from service cells and co-frequency neighbor cells as training data. The smart grid comprises coverage grids, spectrum efficiency grids, and beam grids. The coverage grids yield information about the inter-frequency RSRP, the spectrum efficiency grids offer insights into inter-frequency spectral efficiency, and the beam grids provide information about inter-frequency SRS beam ID. These grids collectively provide input data for the network-level optimization algorithm. Additionally, cell load information from neighboring cells operating at different frequencies is taken into account to achieve optimal results in resource allocation. By solving the optimization algorithm, an optimal time-frequency-space resource allocation scheme is obtained. This scheme facilitates adjustments to the distribution of users and services at the beam level, ultimately enhancing the downlink experience for users across the entire sharing network. The details of the method will be given in Sections 4.2 and 4.3.
## 2 Materials and Methods
### WSA Units Hardware
It supports four RF bands from 3.5 GHz to 6.5 GHz with a channel bandwidth of 500 MHz or 1 GHz and data rates from 110 kbit/s up to 6.8 Mbit/s. The output power is adjustable and should be limited to \(-\)41.3 dBm/MHz for most regions. The UWB communication is based on the transmission and reception of radio frames structured according to the IEEE 802.15.4-2011 standard. Figure 3 below shows the single structure of a UWB frame. The frame starts with the preamble, a repeating sequence of pulses used for frame detection, and the start frame delimiter (SFD), which together form the synchronization header (SHR). After the SHR, the Physical Layer Header (PHR) defines the length and the data rate of the Medium Access Control (MAC) portion of the frame. The MAC begins with the 9-byte MAC header, which contains the default frame control field, sequence number, network, source address, and destination address. The frame control field contains the frame-type-definition information, addressing fields, and other control flags. The MAC header is followed by the 22-byte MAC payload containing user-defined packet-type data, inertial sensor data, the packet counter and battery-status indicator. The MAC ends with a 2-byte footer MAC (MFR) with a packet checksum. According to IEEE 802.15.4-2011 the preamble can consist of 64, 1024 or 4096 symbols, the SFD can contain 8 or 64 symbols, the MAC can be up to 127 bytes long and the PHY is 19 bits long. The WSA packet frame was configured as the default data-type frame. The custom packet-type field determines whether the type of a packet is an instruction to the WSA entity or a response. The WSA was configured to operate on a radio channel with a central frequency of 4.5 GHz and a channel bandwidth of 500 MHz The data frames are  
Figure 3: Synchronization header (SHR), the Physical Layer Header (PHR) and the Medium Access Control (MAC) portions of the frame, each with subfields identified. transmitted at 6.8 Mbit/s with a 64-symbol preamble and non-standard SFD consist of 8 symbols. The UWB implemented according to the 802.15.4 standard is called the impulse-radio UWB (IR-UWB). The name comes from the high-speed pulses of RF. During the PHR and the data portion of the frame, the information bits are signalled by the position of the burst in a modulation scheme called burst-position modulation (BPM). Each data bit passes through a convolutional encoder to produce a polarity bit that is used to set the phase of the burst as either positive or negative. This component of the modulation is referred to as binary phase shift keying (BPSK). A symbol represents successive bursts that are decoded into two bits of information. Unlike the BPM/BPSK modulation used for PHR and MAC, SHR consists of individual pulses. The detection of SFD is a key event in the reception of a frame, as it marks the beginning of the PHY header and thus the change from preamble demodulation to BPM/BPSK demodulation of the PHR (and later the MAC).
###### Abstract  
During tunnel development in metal mines, there are situations where a zone of contact between the ore and the surrounding rock is reached. Nevertheless, there is a notable disparity in the mechanical characteristics between the ore and the surrounding rock, leading to a specific response of grouting in the contact area between the ore and rock. This response differs from the typical diffusion and curtain formation effects observed when using grouting slurry. This study investigates the effects of grouting curtain creation when implementing highly advanced curtain grouting in a water-rich highway, utilizing the engineering conditions of Zhongju Iron Mine as a reference. At first, Darcy's law and the Navier-Stokes equation are used to control the flow of fluid in the area where the ore-rock meets the rock around it. COMSOL, a multi-physical field coupled analysis software, is employed for the numerical simulation of slurry plane diffusion, single-hole, and group-hole curtain grouting. Two optimization strategies for group-hole grouting parameters are subsequently suggested and proven using numerical simulation. Finally, the project implements the research to assess the influence of curtain grouting by employing the water influx of the exploratory apertures as the standard of comparison before and after grouting; the results demonstrate that the slurry forms a highly efficient grouting curtain, effectively impeding water infiltration. The findings indicate that slurry diffusion in the contact zone between the ore and rock follows a spherical motion pattern, resulting in a considerable decrease in the flow rate compared to the previous stage. The force of gravity visibly affects the spreading of the slurry in the area where the ore and rock come into contact, causing the slurry to mostly spread downwards. This inclination intensifies as the rate of grouting is elevated. To successfully address the inadequate distribution of the slurry, one can either increase the rate at which grouting is performed or decrease the distance between the grouting holes.  
ore-rock contact zone; water-rich roadway; advance curtain grouting; numerical simulation 22116, 2021  
1
## 2 Ti6 Al4V
### Crystal Structure  
Titanium is known to exist in two different crystal structures: a close packed hexagonal \(\alpha\) phase and a body-centered-cubic \(\beta\) phase [16].  
Alloying elements in titanium can be categorized into \(\alpha\)-stabilizers, \(\beta\)-stabilizers or neutral additions, depending on which phase the elements tend to stabilize, or whether they effectively increase or decrease the solid-state \(\alpha\)-\(\beta\) transus temperature of titanium [14]. \(\alpha\)-stabilizers, including the substitutional element (aluminum) and the interstitial elements (oxygen, nitrogen and carbon), strongly increase the threshold temperature at which the \(\alpha\) phase is stable, even if the solute content increases. \(\beta\)-stabilizers, including isomorphous elements (vanadium, molybdenum, niobium and tantalum) and eutectoid elements (ferrous, manganese, chromium and nickel), stabilize the \(\beta\) phase at lower temperatures. In addition, some neutral elements (zirconium, hafnium and tin) have no significant influence on the \(\beta\) transus temperature [14; 17].  
\(\alpha\)+\(\beta\) alloys, such as Ti6 Al4V, have a high-strength and formability and contain 4-6 wt.% \(\beta\)-stabilizers, which allow substantial amounts of \(\beta\) phases to be retained from the \(\alpha\)+\(\beta\) phase fields after quenching. Al reduces the density of the alloy and stabilizes the \(\alpha\) phase, while vanadium provides a greater amount of the more ductile \(\beta\) phase for hot-working [18].  
As an \(\alpha\)+\(\beta\) alloy, Ti6 Al4V contains both an \(\alpha\)-stabilizer (aluminum) and a \(\beta\)-stabilizer (vanadium), and thus has a moderate strength and weldability. Ti6 Al4V by far is the best adopted titanium alloy for additive manufacturing. The lowest temperature at which a 100% \(\beta\) phase can exist is called the beta transus. The addition of alloying elements to Ti leads to an increase of the \(\beta\) transus temperature from 882 \({}^{\circ}\)C of pure titanium to 980 \({}^{\circ}\)C, and approximately 9 \(\pm\) 2 wt.% of the \(\beta\) phase is retained at room temperature. The majority of the diffusion transformation occurs within the temperature range between 850 and 950 \({}^{\circ}\)C at a transformation cooling rate between 5 and 50 \({}^{\circ}\)C/s [14]
# Coupled Least Squares Identification Algorithms for Multivariate Output-Error Systems
## 2 System Description and Identification Model  
Let us introduce some notation. The symbol \(\mathbf{I}_{m}\) is an \(m\times m\) identity matrix; \(\mathbf{1}_{n}\) is an \(n\)-dimensional column vector whose elements are \(1\); the superscript T denotes the matrix transpose; the norm of the matrix \(\mathbf{X}\) is defined as \(\|\mathbf{X}\|^{2}:=\text{tr}[\mathbf{X}\mathbf{X}^{\dagger}]\); the symbol \(\otimes\) denotes the Kronecker product or the direct product: if \(\mathbf{A}=[a_{ij}]\in\mathbb{R}^{m\times n}\), \(\mathbf{B}=[b_{ij}  
For the identification model in (5), \(\mathbf{\Phi}_{x}(t)\) is the information matrix that consists of the unknown inner variables \(x(t-j)\)'s, so we construct an auxiliary model \(\mathbf{x}_{\text{a}}(t)\), and define the estimate of \(\mathbf{\Phi}_{x}(t)\) as  
\[\mathbf{\hat{\Phi}}_{x}(t):=[-\mathbf{x}_{\text{a}}(t-1),-\mathbf{x}_{\text{a}}(t-2),\cdots, -\mathbf{x}_{\text{a}}(t-n_{a})]\in\mathbb{R}^{m\times n_{a}}.\]  
Then, we use \(\mathbf{\hat{\Phi}}_{x}(t)\) and \(\mathbf{\Phi}_{s}(t)\) to construct the estimate of \(\mathbf{\Phi}(t)\) as  
\[\mathbf{\hat{\Phi}}(t):=[\mathbf{\hat{\Phi}}_{x}(t),\mathbf{\Phi}_{s}(t)]\in\mathbb{R}^{m \times n_{0}}.\]  
Thus, according to (4), we can obtain the auxiliary model,  
\[\mathbf{x}_{\text{a}}(t) =\mathbf{\hat{\Phi}}_{x}(t)\mathbf{\hat{a}}(t)+\mathbf{\Phi}_{s}(t)\mathbf{\hat{ \theta}}(t)\] \[=\mathbf{\hat{\Phi}}(t)\mathbf{\hat{\vartheta}}(t).\]  
The objective of this paper is to use the auxiliary model identification idea and the coupling identification concept to derive new methods for estimating the system parameters \(\mathbf{\theta},a_{1},a_{2},\cdots,a_{n_{a}}\) from the observation data \(\{\mathbf{y}(t),\mathbf{\Phi}_{s}(t)\}\) and to confirm the theoretical results with simulation examples.
## 4 Experimental Results and Discussion
### Comparative Study
The proposed scheme can be used for copyright security, ownership verification, image authentication, telemedicine, military applications, transmission of scientific or courtroom documents, fingerprinting, image forensics, etc. Improving the embedding rate and robustness can be seen as future work. Hybrid optimization approaches for high exploitation and exploration for higher imperceptibility and robustness is also prospective research. Conceptualization and methodology, P.S., K.J.D., H.K.T. and J.S. ; software, P.S. and K.J.D. ; validation and formal analysis, J.S. and H.K.T. ; investigation and resources, P.S. and K.J.D. ; writing original draft preparation, P.S. and K.J.D. ; writing review and editing, J.S. and H.K.T. ; supervision, H.K.T. and J.S. ; project administration, P.S. and K.J.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.
## 1 Introduction
The Dead Reckoning approach uses inertial sensors to infer the current location from the moving direction, velocity, and sampling interval, given an initial position. The main advantage of such a methodology is that it needs no extra infrastructure and has no coverage limitation. This makes it useful in areas where Wi-Fi is not available. However, the main drawback is the accumulated error, leading to the degradation of accuracy over time. The error can be corrected periodically by using other localization methods or spatial information such as environmental maps and landmarks. In the specific case of the pedestrian dead-reckoning, the approach requires three phases: step detection, step length estimation and heading estimation. Many different strategies and methods have been proposed to cope with each of these three steps [4; 8; 9; 10]. Finally, hybrid localization techniques aim to overcome the limits of the other methodologies in terms of accuracy, coverage, the requirement for infrastructure and the cost of deployment by fusing multiple localization signals [4]. A brief review of technologies for the development of indoor localization system is given in Section 2, while in Section 3, the RESIMA system is presented along with considerations related to the implementation of the User-Environment Interaction (UEI) functionality.
# Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest
## 3 Results
### Determination of Optimal Canopy Shape and Comparison of Shadow Fraction  
Based on the results in Section 3.1, Sentinel-2 images from the two seasons with SZA close to 20\({}^{\circ}\) and 40\({}^{\circ}\), shown in Table 5, were employed in the simulations. Due to the availability of Sentinel-2 BOA products beginning at the end of 2019 in the target area and cloud contamination, images in 2019 and 2020 were used. The relative reflectance was calculated using the average of the reflectance for each of Season 1 and Season 2. Note that because non-forest pixels, such as those for buildings, exist in the target forest, as shown in Figure 1, 40 forest pixels were sampled visually.  
Table 6 shows the RMSE of the Sentinel-2 Band 4 reflectance simulated and the simulated shadow fraction. The shadow fraction calculated from the voxel model based on the point cloud acquired by UAV-SfM was used for the comparison of shadow fractions. The canopy shape that had the lowest RMSE in both reflectance and shadow simulations was the half ellipsoid. The use of a canopy shape with a large error in the reflectance simulation also resulted in a larger error in the shadow fraction. The shadow fraction simulation shows that the RMSE differs by about 10% between canopy shapes with rounded (ellipsoid and half ellipsoid) and flat (cylinder and inverted half ellipsoid) tops.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Season** & **Date** & **SAA (\({}^{\circ}\))** & **SZA (\({}^{\circ}\))** \\ \hline \multirow{4}{*}{1} & 13 April 2019 & 108.7 & 21.3 \\  & 28 April 2019 & 129.9 & 22.4 \\  & 12 April 2020 & 108.9 & 21.4 \\  & 22 April 2020 & 100.0 & 19.7 \\  & 7 May 2020 & 86.3 & 18.5 \\ \hline \multirow{4}{*}{2} & 9 December 2019 & 155.1 & 43.3 \\  & 14 December 2019 & 154.7 & 43.9 \\ \cline{1-1}  & 24 December 2019 & 153.4 & 44.6 \\ \cline{1-1}  & 13 December 2020 & 154.7 & 43.9 \\ \cline{1-1}  & 28 December 2020 & 152.6 & 44.7 \\ \hline \end{tabular}
\end{table}
Table 5: Date of Sentinel-2 image acquisition and Sun geometry conditions (Sun azimuth angle (SAA) and Sun zenith angle (SZA)).  
Figure 7: Reflectance simulation results for the same SZA with different canopy shapes. \(X\)-axis is SZA and \(y\)-axis is reflectance.