esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision System Based on Markov Chains for Sizing the Rebalancing Fleet of Bike Sharing Stations
## 3 Methodology
### Rebalancing Strategy for Model Validation
The scaling factor \(\alpha\) is selected so that the optimal criteria from Equation (16) are met:  
\[\hat{\mathsf{Tr}}\;\text{is OPTIMAL}\;\Longleftrightarrow\;\text{FK}(\hat{ \mathsf{Tr}})\approx\text{CK}(\hat{\mathsf{Tr}}), \tag{16}\]  
where \(\hat{\mathsf{Tr}}\) is the optimal number of trucks allocated to the subsystem; \(\text{FK}:\mathbb{N}\to\mathbb{R}^{+}\) is the scaled fleet cost; \(\text{CK}:\mathbb{N}\to\mathbb{R}^{+}\) is the rebalancing cost. Approximation (\(\approx\)) instead of equality (\(=\)) is used because it is impossible to allocate fractions of trucks, so the closest natural number is used instead (for example, in Figure 10, \(\hat{\mathsf{Tr}}=7\) or \(8\)). Equation (7) shows that the overall criticality \(\overline{x}\) changes over time and, based on Equation (14), the subsystem demand \(D\), therefore, also changes over time. The optimal number \(\hat{\mathsf{Tr}}\) of trucks is also dependent on \(D\). Because the subsystem to be modeled belongs to a real-world BSS, another constraint is that \(\hat{\mathsf{Tr}}\) is ideally selected once in the beginning phase of the subsystem. In order to determine a time invariant demand \(\hat{D}\) of the subsystem, the steady state probabilities for the subsystem are used. The steady state probabilities vector \(P^{*}\) is defined in the equation below:  
\[P^{*}=[P^{*}_{1}\quad P^{*}_{2}\quad\cdots\quad P^{*}_{N}], \tag{17}\]  
where \(N\) is the number of individual states of the subsystem; \(P^{*}_{i}\) is the probability for the subsystem to be in the state \(S_{i}\), and it can be determined by solving the following system of equations:  
\[ \begin{cases}P^{*}Q=0\\ \sum_{i=1}^{N}P^{*}_{i}=1\end{cases}  \tag{18}\]  
Figure 10: Fleet acquisition cost versus rebalancing cost. Considering the quantization Equation (8), which states that \(S_{i}\) corresponds to an overall criticality as an average of the interval boundaries (\(\frac{1}{2}(v_{i-1}+v_{i})\)), the steady demand \(\hat{D}\) of the subsystem will be calculated as follows:  
\[\hat{D}=P^{*}\mathrm{Y}m, \tag{19}\]  
where \(\mathrm{Y}\) is the associated average overall criticality vector to the subsystem states (\(\mathrm{Y}\mapsto\{S_{1},S_{2},\ldots,S_{N}\}\)), defined as follows:  
\[\mathrm{Y}= \begin{bmatrix}\overline{v_{0,1}}\\ \overline{v_{1,2}}\\ \overline{v_{2,3}}\\ \vdots\\ \overline{v_{N-2,N-1}}\\ \overline{v_{N-1,N}}\end{bmatrix} =\frac{1}{2} \begin{bmatrix}v_{1}\\ v_{1}+v_{2}\\ v_{2}+v_{3}\\ \vdots\\ v_{N-2}+v_{N-1}\\ v_{N-1}+1\end{bmatrix}  \tag{20}\]
# Photogrammetry-Based 3D Textured Point Cloud Models Building and Rock Structure Estimation
## 5 The Rock Structure Analysis for the Rock Slope
Despite the promising results of this methodology in the study of rock mass structure, there are several limitations that need to be addressed. A main limitation is the computational resources required to generate 3D point cloud models, which can be a bottleneck when dealing with large outcrops. Additionally, the accuracy of the method heavily depends on the number photos used to generate 3D texture models, which can be a challenging task in some field scenarios. Furthermore, the generated joint models can only provide information about the geometry of the joints, and not their mechanical properties, which are crucial for slope stability analysis. To overcome these limitations, there are several approaches that can be taken. Firstly, we suggest improving the hardware configuration of the computer. Additionally, if feasible, a large rock outcrop can be divided into several smaller models. Secondly, we recommend using unmanned aerial vehicles to capture more photos to solve the issue of insufficient photo data. Moreover, unmanned aerial vehicles can overcome some limitations of certain field conditions, such as inaccessibility or safety concerns. Finally, we recommend conducting indoor mechanical experiments to obtain the mechanical parameters of joints. If conducting mechanical experiments is difficult, the mechanical parameters of joints can be obtained from geological reports.
# Non-Autoregressive Sparse Transformer Networks for Pedestrian Trajectory Prediction
###### Abstract  
Pedestrian trajectory prediction is an important task in practical applications such as automatic driving and surveillance systems. It is challenging to effectively model social interactions among pedestrians and capture temporal dependencies. Previous methods typically emphasized social interactions among pedestrians but ignored the temporal consistency of predictions and suffered from superfluous interactions by dense undirected graphs, resulting in a considerable deviance from reality. In addition, autoregressive approaches predicted future locations conditioning on previous predictions one by one, which would lead to error accumulation and time consuming. To address these issues, we present Non-autoregressive Sparse Transformer (NaST) networks for pedestrian trajectory prediction. Specifically, NaST models sparse spatial interactions and sparse temporal dependency via a sparse spatial transformer and a sparse temporal transformer separately. Different from previous predictions such as RNN-based approaches, the transformer decoder works in non-autoregressive pattern and predicts all the future locations at one time from a query sequence, which could avoid the error accumulation and be less computationally intensive. We evaluate our proposed method on the ETH and UCY datasets, and the experimental results show our method outperforms comparative state-of-the-art methods.  
p edestrian trajectory prediction; sparse transformer; non-autoregressive
# Viability of ABO Blood Typing with ATR-FTIR Spectroscopy
## 2 Materials and Methods  
Human blood samples were kindly provided by the Centro Comunitario de Sangre y Tejidos de Asturias from blood samples from anonymous donors. All samples were ABO-typed following the manufacturer instructions of an immune-based assay of blood typing from IBDCiencia (Ref.: ME91253).  
FTIR spectra were taken using a Varian 670-IR spectrometer equipped with a Golden Gate ATR device. Spectra were averaged from 16 scans and recorded from 600 cm\({}^{-1}\) to 4000 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\). Measurements were carried out by putting a droplet of blood onto the diamond crystal of ATR and evaporating it under a constant air current.  
Principal Component Analysis (PCA) was performed using MatLab scripts developed by the authors. Naive Bayesian Classifier (NBC) and Linear Discriminant Analysis (LDA) calculations were performed using MatLab functions.
## 5 Conclusions  
This paper shows how firms can design optimal RRPs when customers have hidden budget constraints, and offers a line of managerial implications regarding marketing.  
First, we show that a high-price-high-reward strategy is no longer optimal in the presence of budget constraints. Rewards are often considered an effective approach to filling the gap between high prices and low product valuations [14]. However, we determine that a high price with a high reward is not always profitable when accounting for the friend's budget constraints. When the friend cannot afford the high price, the firm's profit is at risk. Therefore, to assure the profitability of RRPs, the firms should consider both the friend's product valuation and any budget constraints. When the customer valuation is very low, setting a reward is always profitable. However, when the product valuation is at a medium level, the firm should set a reward only on the condition that the friend is likely to have a more flexible budget. Meanwhile, if the customer valuation is very high, then the reward is not necessary; in this case, firms should consider providing financing options for the friend.  
Second, RRPs might be optimal when the friend has either a low or a high valuation, but they work differently in each situation for the reason of budget. As the budget of a friend is private information, an estimation of the budget from the firm or the existing customer can bring risks for the reason that the friend may have a low budget and cannot afford a high price. When the product valuation of the friend is low, the firm adopts low-price RRPs to gain a risk-free profit. While when the product valuation of the friend is high, the firm adopts high-price RRPs to pursue a high profit with risks instead of a risk-free profit.  
Third, we show that there are two budget thresholds that limit a firm's ability to use rewards. One of them is a fixed threshold, beyond which the firm's RRPs are profitable. The other threshold is sensitive to the friend's initial valuation of the product.  
Fourth, our study suggests that, when the cost of a referral or firm financing is high, the firm does not need to implement both strategies because they are perfect substitutes for each other.  
This paper has some limitations. Our study assumes that the firm is risk-neutral. Further research can investigate RRPs in relation to other risk preferences. In addition, we assume a constant marginal cost associated with the existing customer's referral effort, which might be reexamined using a concave relationship.
## 1 Introduction  
Cryptography is the science of encoding information to safeguard its confidentiality and integrity. It is pivotal in achieving a secure and protected environment [1]. Over the years, various cryptographic techniques have emerged. Each technique/algorithm has its own strengths and weaknesses [2, 3]. In this context, Elliptic Curve Cryptography (ECC) is regarded as a method that strikes good balance between security and efficiency [4]. Due to its ability to operate effectively with smaller key sizes and low computing requirements, it has become a popular option for devices and applications with limited resources. This includes Internet of Things (IoT) devices and secure communication protocols [5].  
Despite the numerous advantages of ECC, it does present challenges. One of the major drawbacks of ECC lies in its vulnerability to Side-Channel Power Analysis (SPA) attacks [6]. SPA attacks exploit variations in a device's power consumption to infer sensitive information such as cryptographic keys. To address vulnerability to SPAs, cryptographic algorithms often require advanced countermeasures [7]. A typical example of these countermeasures is the employment of NAF representation during the point multiplication [8, 9, 10] process. Point multiplication is one of the most computational processes in any type of elliptic curve. Therefore, by utilizing the NAF algorithm, the number of additions and subtractions needed for point multiplication is reduced, consequently minimizing the vulnerability to SPA attacks.  
This article introduces a novel hardware architecture design for the implementation of the NAF algorithm. Moreover, the introduced architecture in this article leverages unified mathematical formulations. A variety of elliptic curves provide unified mathematical formulations, including binary Huff curves (BHCs) [11], Hessian curves (HCs) [12], and binary Edwards curves (BECs) [13]. Nevertheless, the proposed architecture adopts BECs for unified mathematical formulations. BECs were chosen because of their effective arithmetic operations and acceptable security characteristics. They are the perfect match for our design since they ensure high-performance calculations while offering a solid foundation for secure ECC. In the following, state-of-the-art hardware architectures for BECs are presented.
# BeiDou Augmented Navigation from Low Earth Orbit Satellites
## 1 Introduction
Meanwhile, in late-February 2018, the China Aerospace Science and Technology Corporation (CASC) announced plans to build the 'Hongyan' LEO constellation of 324 small satellites for global communications and other services. The Hongyan constellation with 1100 km altitude is targeted to be operational by 2021 [17]. Currently, the Iridium constellation is already broadcasting structured independent navigation signals, and provides independent positioning, navigation and timing (PNT) service based on Satelles' satellite time and location (STL) signal technology to augment or serve as a back-up to existing GNSS PNT solutions [18]. STL is designed such that a receiver can reliably decode the bursts and perform precise Doppler and range measurements at attenuation of up to 39 dB relative to unobstructed reception. This is sufficient to penetrate buildings and other occlusions, providing coverage in most deep indoor and urban canyon environments. The testing results show that STL always experienced strong signals and can provide sub-microsecond timekeeping even in deep attenuation environments such as indoors. In addition, after 10 min of convergence, the STL solution had converged to an accuracy of better than 35 m with same accuracy in vertical and horizontal direction [19]. Used as the independent navigation satellite system, a PNT service comparable to GPS could be achieved although with the added benefits of LEO, including stronger signals and rapid changes in geometry. Besides, the LEO can also be used to augment the GNSS to provide combined solution with GNSS and LEO navigation signals. The aim of this study is to assess the possibility and performance of LEO constellation used to augment the BeiDou navigation system for precise positioning. In Section 2, the advantage of LEO navigation system in positioning accuracy and integer ambiguity resolution will be analyzed theoretically. In Section 3, the strategy used for simulating the observations from GNSS and LEO constellation as well as Precise Point Positioning (PPP) with GPS-only or combined GNSS and LEO will be presented. The results will be analyzed and discussed in Section 4 with emphasizingon the convergence of ambiguity as well as accuracy after convergence. Finally, the study will be concluded in Section 5.
# Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images
###### Abstract  
Natural gas pipelines represent a critical infrastructure for most countries and thus their safety is of paramount importance. To report potential risks along pipelines, several steps are taken such as manual inspection and helicopter flights; however, these solutions are expensive and the flights are environmentally unfriendly. Deep learning has demonstrated considerable potential in handling a number of tasks in recent years as models rely on huge datasets to learn a specific task. With the increasing number of satellites orbiting the Earth, remote sensing data have become widely available, thus paving the way for automated pipeline monitoring via deep learning. This can result in effective risk detection, thereby reducing monitoring costs while being more precise and accurate. A major hindrance here is the low resolution of images obtained from the satellites, which makes it difficult to detect smaller changes. To this end, we propose to use transformers trained with low-resolution images in a change detection setting to detect pipeline risks. We collect PlanetScope satellite imagery (3 m resolution) that captures certain risks associated with the pipelines and present how we collected the data. Furthermore, we compare various state-of-the-art models, among which ChangeFormer, a transformer architecture for change detection, achieves the best performance with a 70% F1 score. As part of our evaluation, we discuss the specific performance requirements in pipeline monitoring and show how the model's predictions can be shifted accordingly during training.  
Keywords:transformer; PlanetScope; pipeline monitoring; change detection : 16266
## 3 Results
### Automotive Multi-Layer System on Metal Substrate
#### 3.1.1 Single Layer Characterization  
Figure 3 shows the dispersion of the optical parameters of the E-coat, the BC layer and the CC layer. The dispersions were obtained by fitting the ellipsometric measurements of the single layers deposited on a metal substrate from 0.1 THz up to 6 THz.  
The E-coat measurement can be modeled with a constant refractive index and absorption, without any frequency dependence. The complex refractive index of the E-coat layer is n = 1.762 + i0.076 with a thickness of 18.5.  
The thickness of the white BC layer is 43.2. The dispersion is parametrized by two Lorentz oscillators, one clearly visible at about 2.2 THz. The second one lies outside the measured spectral range at 7.5 THz and is much broader. This Lorentz oscillator is responsible for the slope of the absorption.  
The ellipsometric measurement of the CC layer can be seen in Figure 4. Here, two measurements with different layer thicknesses are shown. Measurement one (black) and measurement two (blue) correspond to a layer thickness of 54 and 57, respectively. Although the thickness difference was only 3, both spectra are clearly distinguishable, which demonstrates the sensitivity of the ellipsometric measurement. The differences between the spectra increase with frequency, indicating that the sensitivity grows with the measurement bandwidth. The dispersion is parametrized by a single broad Lorentz oscillator.  
Figure 3: Dispersions of the refractive index and the absorption for the E-coat, the BC layer and the CC layer.
## 5 Open Issues and Challenges
### Impact of Wireless Communication on the Human Body  
Because the biosensors are placed in or on the human body, the radio frequency (RF) radiation could affect adversely to the human body. The RF radiation in interaction with biological systems has been investigated in terms of thermal and non-thermal bioeffect [50]. Average specific absorption rate (SAR) limitation in the whole human body is 0.4 W/kg and 0.08 W/kg in controlled and uncontrolled environments, respectively; local SAR is 1.6 W/kg in any gram tissue in the shape of a cube [51]. The thermal mechanisms result in temperature rise of the biological system; e.g., a continuous wave RF field of 30 V/m at 1 GHz produces SAR of about 1 W/kg [50]. One of the non-thermal mechanisms is membrane excitation; _i.e._, a very short pulse can cause membrane breakdown. It is conducted in [52] that the non-thermal mechanisms are not fully understood but they do not lead to the biological effects. Therefore, the human body may be concerned with the safety problem not only for a short term but also for a long term.  
More specifically, in [53], the impact of RF radiation on the human body has been identified as well as classified according to the radiation sources, impacts, and factors. The radiation impacts are grouped into the extremely low frequency and radio frequency groups. Furthermore, the factors that affect RF absorption are categorized into physical parameters, biological parameters, artifacts, and environmental parameters. Consequently, some open research areas for passive wireless technologies have been addressed for healthcare environments. Another research has been conducted in three-dimensional wireless sensor networks in [54], in which the aspect of radiation awareness is investigated in three-dimensional environments. The main focus is to find the minimum radiation path for a person moving from one point to another point.  
In WBASNs, the coordinator node is considered as a body-placed device which can transmit signals to the router. In [55], the new antenna structure which can reduce SAR is used in a body-worn communication device, and it can be also applied to the coordinator node. The newly designed antenna which is one type of an artificial magnetic conductor (AMC) has the slotted periodic structure. In addition, this type of antennas is used at a wideband code division multiple access as well. In [56], another type of AMC is used as an AMC-intergrated antenna which is designed for WBASNs to operate in the ISM bands. The simulation results in [55,56] show that the AMC antenna is suitable to reduce SAR on the human body.  
However, it is necessary to find a path with low SAR in WBASNs, which is conducted in [57] by using an optimization algorithm. One node will be chosen to be the relay node which transmits signals from other sensor nodes to a hub by using the low SAR path.
## 2 Theoretical Background
### Dissipative Nonlinearity  
The latest experiments have shown that nonlinear energy dissipation can be caused in the structure at the mesoscopic interfaces (such as cracks or contact surfaces) in the case of VAM [16; 18; 23; 24]. The most significant phenomenon of such dissipation is the HF wave dampened by a stronger LF wave.  
Moreover, dissipative nonlinearity (DN) was indicated to occur either in a low-strain level crack surface perturbation (e.g., thermal [25]) or a relatively highly intense interaction (e.g., friction [26] or hysteresis [27]). However, differentiation between the two dominant dissipation mechanisms is usually difficult to obtain in practice [20]. Assuming that the structural nonlinear responses are dominated by thermo-elastic dissipation, the HF losses at the inner contact of the cracks can be described as [28]  
\[W_{HF}^{dis}=\bigg{(}\frac{2\pi}{\omega_{L}}\bigg{)}\kappa\eta\bigg{(}\frac{ \kappa E}{H\rho}\bigg{)}^{2}\mathbb{T}\bigg{(}\frac{L}{I}\bigg{)}^{2}\varepsilon ^{2}, \tag{12}\]  
where \(\eta\) is the temperature, \(\alpha\) is the temperature expansion coefficient of the solid, \(E\) is the bulk elastic modulus, \(\rho\) is the density, \(H\) is the specific heat, \(\varepsilon\) is the average strain, \(\kappa\) is the thermal conductivity, and \(\omega_{L}\) is the relaxation frequency for the defect scale \(L\). Recently, [PERSON] et al. [16] revealed that the dissipation based on the thermo-elastic coupling may be more efficient in the bolted joint; meanwhile, frictional and adhesional hysteretic dissipation may also exist. The contribution of the dissipation mechanism may cause continuous effects on the spectral amplitudes in VAM testing, which cannot be neglected.
# Dual-Band Frequency Selective Surface-Backed Reflectarray for High-Speed Ka-Band Satellites
## 3 Dual-Band Reflectarray System Design
### Reflective Surface Design at 20/30 GHz  
To direct the beam reflected from the reflectarray surface towards predefined direction, the phase angle of each unit cell on the reflectarray should be calculated. Equation (2) is used to calculate the distance \(r_{f,mn}\) of each unit cell from the phase center of the feed antenna, while Equation (3) is used to calculate the corresponding phases (\(\psi_{mn}\)) required to direct the beam.  
\[r_{f,mn}=\sqrt{\left(x_{mn}-r_{f_{x}}\right)^{2}+\left(y_{mn}-r_{f_{y}}\right) ^{2}+\left(z_{mn}-r_{f_{z}}\right)^{2}} \tag{2}\]  
\[\psi_{mn}=k_{0}\Big{(}r_{f,mn}-\overrightarrow{r}_{mn}.\hat{r}_{0}\Big{)}+ \psi_{o} \tag{3}\]  
Figure 4: Reflectarray antenna configuration.  
Figure 5: Aperture efficiency at (**a**) 20 GHz and (**b**) 30 GHz.  
\(\overset{\rightarrow}{r}_{mn}\) is the position vector of the mnth element, \(k_{0}\) is the wave number, \(\psi_{0}\) is the phase constant, and \(\hat{r}_{0}\) is the main beam direction [5]. \(r_{f_{x}}\), \(r_{f_{y}}\), and \(r_{f_{z}}\) in Equation (2) are the \((x,y,z)\) components of the feed position. The phase distribution on the reflectarray surface is matched with the unit cell size given in Figure 2. For each of the unit cell on the reflective surface, the element dimension should give the phase value obtained using Equation (3). The phase patterns of the reflectarray consisting of \(33\times 33\) unit cells are obtained at 20 and 30 GHz for \(D=198\) mm, \(\theta_{o}=20^{\circ}\), and \(\theta_{f}=-20^{\circ}\) in the \(\phi=0^{\circ}\) plane, as given in Figure 6. The vertical distance between the reflective surface and the phase center of the feed antenna is set to \(H=250\) mm for 30 GHz. Since the phase center position of an antenna varies as the function of frequency, the phase center of the feed horn antenna at 20 and 30 GHz would be different from each other. By considering the phase center position at 20 GHz and reflective surface position compared to the reflective surface of 30 GHz, 5.18 mm is added to \(H\) to determine the corresponding phases of the unit cells for 20 GHz reflectarray.