esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 2 Materials and Methods
### Gasification
#### 2.2.1 Experimental Setup
\begin{table}
\begin{tabular}{l c c} Oxidation: C(s) + O\({}_{2}\)(g) \(\leftrightarrow\) CO\({}_{2}\)(g) & \(\Delta\)H \(<\) 0 \\ Partial oxidation: C(s) + 1/2O\({}_{2}\)(g) \(\leftrightarrow\) CO(g) & \(\Delta\)H \(<\) 0 \\ Boudouard: C(s) + CO\({}_{2}\)(g) \(\leftrightarrow\) 2 CO(g) & \(\Delta\)H \(>\) 0 \\ Water—gas primary: C(s) + H\({}_{2}\)O(g) \(\leftrightarrow\) CO(g) + H\({}_{2}\)(g) & \(\Delta\)H \(>\) 0 \\ Water—gas secondary: C(s) + H\({}_{2}\)O(g) \(\leftrightarrow\) CO\({}_{2}\)(g) + 2H\({}_{2}\)(g) & \(\Delta\)H \(>\) 0 \\ Water—gas shift (WGS): CO(g) + H\({}_{2}\)O(g) \(\leftrightarrow\) CO\({}_{2}\)(g) + H\({}_{2}\)(g) & \(\Delta\)H \(<\) 0 \\ Methanation: C(s) + 2H\({}_{2}\)(g) \(\leftrightarrow\) CH\({}_{4}\)(g) & \(\Delta\)H \(<\) 0 \\ Steam reforming: C\({}_{n}\)H\({}_{x}\)(g) + \(n\)H\({}_{2}\)O(g) \(\leftrightarrow\) (\(x/2\)+\(n\))H\({}_{2}\)(g) + nCO(g) & \(\Delta\)H \(>\) 0 \\ Dry reforming: C\({}_{n}\)H\({}_{x}\)(g) + \(n\)CO\({}_{2}\)(g) \(\leftrightarrow\) 2\(n\)CO(g) + (\(x/2\))H\({}_{2}\)(g) & \(\Delta\)H \(>\) 0 \\ Cracking: C\({}_{n}\)H\({}_{x}\)(g) \(\leftrightarrow\) nC(s) + (\(x/2\))H\({}_{2}\)(g) & \(\Delta\)H \(>\) 0 \\ \end{tabular}
\end{table} 
Table 2: Operational conditions in the gasification tests. The mass yields of gas, liquid, and solid products (\(\eta_{m,i}\)) were calculated with respect to the mass of SRF fed (Equation (1))  
\[\eta_{m,i}(wt\%)=\frac{m_{product}}{m_{SRF}}\times 100 \tag{1}\]  
where: \(m_{product}\) is the mass of the product (solid, gas, and tar) obtained in the reaction (kg) and \(m_{SRF}\) is the mass of SRF fed during the experiment (dry basis) (kg). The LHV of the syngas obtained (\(LHV_{gas}\), \(\mathrm{MJ/m^{3}}_{\mathrm{STF}}\)) and the cold gas efficiency, which represents the ratio between the energy content in the produced gasification gas (cooled at room temperature) and the energy content in the solid fuel, were calculated according to Equations (2) and (3), respectively:  
\[LHV_{gas}=\sum y_{i}\times LHV_{i} \tag{2}\]  
where: \(LHV_{i}\) (\(\mathrm{MJ/m^{3}}_{\mathrm{STF}}\)) is the LHV of each gaseous component and \(y_{i}\) is its volumetric fraction (determined with the gas microchromatograph). \[Cold\;gas\;efficiency\;(\%)=\frac{LHV_{gas}\times V_{gas}}{LHV_{SRF}\times m_{ SRF}}\times 100 \tag{3}\]  
where: \(V_{gas}\) is the total volume of gasification gas (\(\mathrm{m^{3}}_{\mathrm{STF}}\)), \(LHV_{SRF}\) (\(\mathrm{MJ/kg}\)) is the LHV of the SRF fed, and \(m_{SRF}\) is the total mass of SRF fed during the experiment (kg). Finally, the carbon yield to the gas phase (\(\%\)) was calculated as follows:  
\[Carbon\;yield\;to\;gas\;phase\;(\%)=\frac{m_{C\;in\;gas}}{m_{C\;in\;SRF}}\times 100 \tag{4}\]  
where: \(m_{C\;in\;gas}\) is the mass (g) of carbon contained in the gas product and \(m_{C\;in\;SRF}\) is the mass (g) of carbon contained in the SRF.
## 3 Performance Analysis Methods
### Expressions for Path Loss and Average Path Loss
#### 3.1.3 The Average Path Loss of the NLOS Link Formed by \(R\)S  
Similarly, based on the symmetry of Tx1 and Tx2, from Tx1, the average path loss of the NLOS link formed by RS can be mathematically calculated. Referring to Appendix C, the overall average path loss of the closed-form expression for the NLOS link formed by \(RS\) can be obtained as follows:  
\[\begin{array}{ll}h_{NLOS\_RS}^{PL\_avg}&=\frac{8A_{RS}\ ^{2}D_{R}\ ^{2}I_{V_{1-PD}} \ ^{4/\epsilon}e^{\frac{-c_{PD}D_{R}}{5}}}{\xi^{4P3/2+2/\epsilon}(L_{w}-w_{w}) Gamma\left[2+\frac{4}{4}\right]}\left\{2\sqrt{\pi}Gamma\right.\\ &-Gamma\left[2+\frac{2}{\epsilon}\right]\left\{Beta\left[\frac{4P}{4P+L_{w}},\frac{3}{2}+\frac{2}{\epsilon},\frac{1}{2}\right]+Beta\left[\frac{4P}{4P+(L _{w}-2w_{w})^{2}},\frac{3}{2}+\frac{2}{\epsilon},\frac{1}{2}\right]\right\} \right\},\\ &\text{where }P=I_{V_{1}-PD}\ ^{2}+4L_{PD}\ ^{2}.\end{array} \tag{14}\]
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 4 Results  
After the preprocessing step, the published set consisted of 3903 1-min samples, of which 2308 were normal samples and 1595 were sleep apnea samples. Three sets of features extracted from each sample were fused and fed into a classifier for sleep apnea detection after feature selection. During the experiment, the dataset was divided into a training set (80%) and a test set (20%) by a stratified sampling method. On the training set, five-fold cross validation was used to select optimal features, optimize classifier parameters, andmodel training. Accuracy, sensitivity, specificity, and AUC were used on the test set to evaluate the model performance.  
The experimental environment is based on the Windows 10 operating system, the software used to develop the algorithm is python 3.6, and the hardware configuration is Xeon E5-2640v4 CPU, Nvidia GeForce RTX2080 Ti GPU, and 32 GB RAM.
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 1 Introduction
### Digital Twin  
The general concept of a digital twin refers to a new simulation means based on CPS and IoT providing a real-time synchronization with the production systems [15]. The DT-coupled big data analysis and Artificial Intelligence (AI) implements real-time monitoring, decision-making, and failure prediction, enhancing the cooperation between an operator and the smart machine [16]. \"A conceptual idea\" of the digital twin was first proposed by [PERSON] in 2002 for product lifecycle management (PLM), and it was called \"Conceptual Ideal for Product Lifecycle Management\" [17]. The concept contained three components: the virtual and real systems, as well as constant information exchange between both systems. Through real-time data communication between the physical world and its digital counterpart, DT was expected to play a role throughout a product's lifecycle. In 2010, the term \"digital twin\" was adopted by the National Aeronautical Space Administration (NASA), which defined it as follows: \"A digital twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that usesthe best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin\" [18].  
Recently, DT has grown into an increasingly wide variety of domains with different applications and objectives [15]. In cases, DT can be used for simulation and modeling of physical assets, systems, or processes [16; 19]. Furthermore, DT can help to monitor the production line performance and balancing by the analyses of production line performance parameters [20]. Moreover, DT's processes can be fully integrated into the various stages of build construction, from physical construction site entity modeling to virtual and real interaction modeling, to improve the efficiency of construction efficiency [21]. Additionally, DT can be adopted for predictive maintenance or design improvement through monitoring and evaluation, e.g., the inefficiency or stress load of machinery [22; 23]. In others, DT can be coupled with XR technologies to support modeling, simulation, and evaluation of manufacturing systems [24; 25]. In addition, to contain and handle meta-level DTs, an open-source, Git-based Digital Twin Web server Twinbase was provided [26].  
A feature-based DT framework proposed in [27] suggested nine technical features and their integration through a central data link. Computation, coupling, identifiers, security, data storage, user interfaces, simulation models, analysis, and artificial intelligence, marked by the functional requirements in implementing DTs in the context of Industry 4.0. Among these elements, user interfaces allow operators to interact with DTs, thus creating HMI as a central building block of DT implementations.
# Handover Management in 5G Vehicular Networks
## 3 Preliminaries
### HO Execution  
An improved scheme for the HO execution is proposed based on the FPMIPv6 protocol. Furthermore, if the selected and the current network belong to different MAGs, inter-MAG HO execution is performed. On the contrary, if the two networks belong to the same MAG, intra-MAG HO execution is performed.
Due to the foregoing, the strain gauge is proposed to be used as a device for measuring small forces, used in a way different from that for which it was designed; that is to say, the strain gauge itself is subjected to pure bending. Furthermore, lateral displacement employing a two-point bending test (cantilever) induced in a way different from that defined by ASTM D747 [17] is proposed. The proposed method is justified for the following reasons: Firstly, the size of the proposed device is in the order of millimeters. Therefore, it is smaller than the minimum dimensions required for standardized tests [17]. Secondly, in this type of test, the data analysis is based on the conventional beam theory [18], or the elastic theory [19], provided that there is a direct relationship between the applied load and the resultant lateral displacement within the elastic range of the specimen's material. However, the appropriateness of the conventional beam theory for determining the relationship between the load and lateral displacement of a sample is called into question when the dimensions of the specimen are in the order of millimeters; thus, uncertainties are generated in the bending tests. Thirdly, because the geometric dimensions of the proposed device correspond to the ones of a small plate, it is difficult to obtain an analytical relationship between load and displacement. The numerical solutions (finite elements or differences) and analytical solutions (superposition method or integral transform method) [20] are well known; however, they require complex and time-consuming procedures. Furthermore, thin flats behave differently from beams in terms of strain and internal stress distribution, which is under [PERSON]'s theory. For these reasons, the objective of this paper is to present the experimental results of two-point bending tests applied directly to strain gauges to measure forces of small magnitude directly. A direct relationship between the load and the instrumental signal is expected to be found because both of them depend on the lateral displacement of the device. Finally, these results are expected to be a practical contribution to the evidence about the mechanical behavior of strain gauges subjected to pure bending.
# Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes
###### Abstract  
The research presented in this article analysed the influence of incorporating precast concrete waste as an alternative to coarse aggregate in self-compacting concrete to generate new precast elements. The experimental study involved the characterization of recycled aggregate and the design of the mix of the new self-compacting concrete (SCC). The experimental study evaluates the physical processes that affect the durability of concrete with percentages of incorporation such as 20%, 50% and 100% of recycled aggregate. Two types of SCC were manufactured with minimum compressive strength of 30 MPa and 45 MPa. The properties analysed were density of hardened SCC, shrinkage cracking, freeze-thaw resistance, resistance to ageing by thermal shock and abrasion resistance. The results obtained were compared with those of the control concrete, observing great capacity of the SCC under physical aggressions that affect durability. The results of this research show that it is possible to use the recycled aggregate coming from precast pieces in order to the manufacture of self-compacting recycled concrete in the same precast industry. However, high loss of proprieties occurs for a 100% substitution, while for 20% and 50%, the variations with respect to control concrete are smaller. In addition, taking advantage of this waste to incorporate it back into the production chain contributes to more sustainable construction.  
recycled; precast; recycled concrete; recycled aggregate; durability; physical Article
## 4 Validation and Discussion
### \(D\) Tunnel Stability Analysis: \(L/d\geq 0.5\)
#### 4.2.3 Comparison of Predicted Stability Numbers with FELA Results  
In order to enlarge the database and further demonstrate the reliability of cavity contraction theory in the prediction of the tunnel heading stability, a new series of 3D numerical simulations are conducted in this paper, with the aid of the FELA software Optum G3. The influence of \(L/D\) and \(H/D\) on the stability number is focused in this series, which is an extension of Sloan [37] for 3D tunnel stability. In the numerical model, the vertical displacement of the bottom, and the horizontal displacement of the two sides are restricted. The minimum distances from the tunnel centre to the bottom and right/left sides are large enough to eliminate the influence of the possible boundary effect. In the lined zone, the linings are modelled by a smooth, rigid element, and in the unlined zone the supporting pressure \(\sigma_{T}\) is optimized in the LB and UB limit analysis. The soil is set as the weightless Tresca material, and default material parameters are adopted since these parameters are believed not to affect the undrained tunnel stability [9]. Adaptative meshes are applied in calculation and the initial and final mesh numbers are 1000 and 10,000, respectively. It has been pre-tested that the mesh densities can satisfy the calculation accuracy.  
Figure 5 shows the tunnel stability number calculated by Optum G3 and the cavity contraction method (Equation (18)). It is shown that the theoretical results derived by Equation (18) agree well with numerical results when \(H/D\leq 3\) but underestimate the tunnel stability when \(H/D=5\). It means the tunnel may not collapse when the plastic zone expands to the ground surface because of the arching effect, and some errors may also be induced from the simplified definition of \(k\), as shown in Equation (19).  
Figure 4: Comparison of predicted stability numbers with 2D centrifuge model tests.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 6 Conclusions
\begin{tabular}{l l} ADC & Analog-to-digital converter \\ AOCS & Attitude and orbit control system \\ CDP & Coulomb drag propulsion \\ COM & Communication system \\ CGP & Cold-gas propulsion \\ CMOS & Complementary metal-oxide-semiconductor \\ COTS & Commercial-off-the-shelf \\ CSP & CubeSat space protocol \\ CTS & Corrosion Testing in Space \\ EGSE & Electrical Ground Support Equipment \\ EOP & Earth observation payload \\ EPS & Electrical power system \\ FPGA & Field-programmable gate array \\ FRAM & Ferroelectric random-access memory \\ GPIO & General purpose input/output \\ HAL & Hardware abstraction layer \\ HSCOM & High-speed communications \\ ICP & Internal communication protocol \\ LEO & Low-Earth orbit \\ MAT & Multi-asteroid touring \\ MCU & Microcontroller \\ MEMS & Microelectromechanical systems \\ MPB & Main power bus \\ MPPT & Maximum power point tracker \\ OBC & Onboard computer \\ IPB & Ionospheric Plasma Brake \\ PCB & Printed circuit board \\ PCOM & Primary communication system \\ RTC & Real-time clock \\ RW & Reaction wheel \\ SCOM & Secondary communication system \\ SDRAM & Synchronous dynamic random-access memory \\ SP & Side panel \\ ST & Star tracker \\ TLE & Two-line element set \\ UKF & Unscented Kalman filter \\ \end{tabular} \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & \multicolumn{2}{c}{**Ground Station Transmitter**} & \multicolumn{1}{c}{**Unit**} \\  & \multicolumn{2}{c}{**(1.2k bit/s)**} & \\ \hline Transmitter power & 47 & dBm \\ \hline Transmitter losses & \(1\) & dB \\ \hline Transmitter antenna gain & 18 & dBi \\ \hline Antenna pointing error & 1 & dB \\ \hline Equivalent isotropically & & 63 & dBm \\ radiated power (EIRP) & & & dBm \\ \hline Elevation angle & 80 & 50 & 30 & 2 & \({}^{\circ}\) \\ \hline \multicolumn{4}{c}{Losses} \\ \hline Distance (zenith 550 km) & 558 & 699 & 993 & 2491 & km \\ \hline Free space loss & 130.60 & 132.56 & 135.61 & 143.60 & dB \\ \hline Atmospheric loss & & 1 & dB \\ \hline Polarisation loss & & 2 & dB \\ \hline Total loss & 133.60 & 135.56 & 138.61 & 146.60 & dB \\ \hline \multicolumn{4}{c}{**Satellite receiver**} \\ \hline Receiver antenna gain & & 3 & & dBi \\ \hline Noise temperature & & 1500 & K \\ \hline Boltzmann constant & & \(-\)198.60 & & dBm/K/Hz \\ \hline Received power & \(-\)67.60 & \(-\)69.56 & \(-\)72.61 & \(-\)80.60 & dBm \\ \hline Data rate & & 1200 & & bit/s \\ \hline SNR per bit (Eb/N0) & 68.44 & 66.48 & 63.44 & 55.45 & dB \\ \hline Required Eb/N0 for 2 FSK & & 18 & & dB \\ modulation (G3 RUH) & & 18 & & dB \\ \hline
**Link budget reserve** & 50.44 & 48.48 & 45.44 & 37.45 & dB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Link budget of ESTCube-2 communications link for secondary COM uplink. Frequency band 144–146 MHz.
## 4 Experiments  
A scheduling problem was approached in a company dedicated to providing fabric finishing services to test the effectiveness of the proposed genetic algorithm in realistic environments. The production system has six processes or stages, 19 machines, some of which can perform more than one process (recirculation condition), and different processing routes for the products to be processed. The setup times are dependent on the schedule sequence due to color changes in the fabrics, and two scenarios of 20 and 30 jobs make up the production schedule. Figure 10 shows the production system highlighting the six processes, the machines enabled by each process, and the routes established within the production plant.  
In order to execute and evaluate the proposed genetic algorithm, this study considers a \(2k\) design of experiments for parameter tuning, taking as experimental factors the population (30, 50), number of iterations (1000, 2000), mutation rate (0.03, 0.1), and crossover rate (0.8, 0.9) based on the values proposed by [PERSON] [38], [PERSON] and [PERSON] [39], and [PERSON] [40], and considering the conditions of the productive system addressed. The results of the parameter tuning are shown in Table 1. The number of jobs to be tested is equivalent to 20 and 30 production orders (jobs) because it represents an appropriate size of operations for the fabric finishing plant, in which processing times are high for each production order. The tardiness and earliness weighting parameters of the objective function for the production system are \(w_{T}=1\) and \(w_{E}=0.4\).  
The genetic algorithm was compared with four heuristics to evaluate the effectiveness and performance of solving the FJSSP. The selected heuristics were EDD (Earliest Due Date), CR (Critical Reason), SPT (Shortest Processing Time), and Monte Carlo simulation. These heuristics were used in the production scheduling problems addressed by [PERSON] and [PERSON] [41], [PERSON] and [PERSON] [35], and [PERSON] [42]. Likewise, three of these heuristics represent conventional priority rules (EDD, SPT, CR) that have been used by [PERSON], [PERSON], and [PERSON] [4]. The adaptation of each heuristic to the flexible job shop system with fuzzy processing times and due windows is explained below.
# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
### Research Contribution  
Studying the fusion of smartphone sensor measurements with RF measurements is an important research direction to improve the accuracy of indoor localization. For systems that calculate the point location of a phone (such as UWB), well-known optimal sensor fusion algorithms such as Kalman Filtering can be easily used [6; 7]. However, for fingerprinting-based localization, which is similar to pattern recognition, there is no apparent standard approach that can be employed.  
Hence, the question arises: Is there an approach to use the onboard phone sensors to improve the accuracy of the underlying RF fingerprinting pattern recognition problem? To the best of the authors' knowledge, no study has answered this question using the smartphone's device pose. In this study, a novel conceptual framework was implemented using an end-to-end BLE setup that first uses accelerometer sensor measurements to identify the device pose of the smartphone and then combines the identified device pose with the RSSI measurements to locate the smartphone in the zone.  
The original contributions of this study can be described as follows:  
1. A conceptual data-level framework is proposed that combines a smartphone's device pose obtained from accelerometer measurements with the smartphone's BLE RSSI for indoor fingerprinting localization.
2. We employed cascaded neural networks to implement and evaluate the proposed framework's model improvement (i.e., RSSI-only vs. RSSI-and-device-pose) via the neural network cross-entropy loss function improvement.
3. We applied the Kolmogorov-Smirnov statistical test to link the accuracy improvement seen in the neural network implementation to the data distribution.
4. We experimentally evaluated the localization accuracy improvement provided by the neural network implementation of the framework in an indoor environment.
5. We demonstrate the robustness of the conceptual framework by also implementing it using K-Nearest Neighbour and Naive Bayes algorithms showing the localization accuracy improvement.
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 2 Literature Review
### Path Planning  
Determining a collision-free geofence-based flight trajectory is central to the design of our geofencing volumization work. A variety of path planning algorithms were considered. Grid-based path planning methods overlay a fixed-resolution grid on top of the configuration space and find discretized line segment paths connecting start state to destination. This search is fast in low-dimensional space but quickly becomes computationally intractable with high-resolution maps and appreciable travel distance. The most notable grid-based path planning algorithms are \(\mathcal{A}^{*}\)[30] and \(\mathcal{D}^{*}\)[31]. A family of roadmap-based path planning algorithms have been developed to offer a more compact search space optimizing a specific cost metric. For example, a visibility graph [32] minimizes travel distance, while a Voronoi diagram maximizes obstacle clearance distance [33]. The application of cell decomposition [34] offers a compact map for discrete search path planning in an obstacle field. Other path planning methods include potential-field algorithms [35] that efficiently build plans with gradient descents but are subject to local minima issues. Sampling-based path planning algorithms [36] have also been developed and are particularly well suited to planning in uncertain environments. Our work utilizes a visibility graph approach to path planning. This approach allows us to directly translate geofence volumes generated with computational geometry into visibility graph roadmaps. As is discussed below, we scale keep-out geofences to assure safe separation is maintained. Note that a visibility graph does not require a rasterized map, enabling geofences to be represented without distortion or approximation.