esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Based Membranes for CO2/CH4 Separation: Key Challenges and Perspectives
## 3 Few- to Multi-Layer Graphene-Based Stacked Laminates
### Perspectives
This is because, despite the higher CO\({}_{2}\)/CH\({}_{4}\) separation performances offered by the stacked laminates, they come at an extra cost. Hence, it is necessary to further ascertain the true impact of using graphene-based stacked laminates both from the process design and return on investment perspective. For example, [PERSON] proposed that if the membrane performance was raised to a CO\({}_{2}\) permeance of 100 GPU and CO\({}_{2}\)/CH\({}_{4}\) selectivity of 40 as compared to the \(\sim\)50 GPU and \(\sim\)15 selectivity of current standard cellulose acetate membrane, it is hypothetically feasible to achieve a reduction in the membrane area, compressor loading, and CH\({}_{4}\) loss through the permeate of up to 75%, which can potentially be translated to savings in the process design [50]. Further, [PERSON] analyzed that it is possible to create a higher annual profit and lower upgrading cost by using a high-performance membrane for a biogas upgrading plant of capacity 150 N m\({}^{3}\) h\({}^{-1}\). Specifically, an annual profit increment and upgrading cost savings of 8% and 86%, respectively, can be expected when membrane selectivity increased from 20 to 60, assuming the same CO\({}_{2}\) permeance of 60 GPU [51]. To date, such techno-economic studies on graphene-based stacked laminates are rather limited. It is, therefore, very difficult to judge the true potential of graphene-based stacked laminates for CO\({}_{2}\)/CH\({}_{4}\) separation just by considering the high separation performances.
## 2 Principles  
In phased array radar systems, multi-band signals have specific phase-frequency-time mapping relationships. When signals of different bands share the same antenna aperture and have the same aperture fill time, the phase difference between array elements is proportional to the frequency, as shown in Figure 1a. The proposed method jointly manipulates frequency and phase through optical pulses. Optical pulses serve as multiple local oscillators (LOs), with VODL introducing delays to the LOs to enable simultaneous frequency conversion and phase shifting. When combined with an electronic phase shifter (PS), this approach achieves precise compensation for phase differences across multi-band array elements, thereby meeting the requirements for multi-band signal transmission and reception in phased array systems, as illustrated in Figure 1b.  
The joint phase-frequency distribution manipulation method proposed in this paper involves several key parameters, whose physical definitions are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Transmitting** & **Receiving** & **Definition** \\ \hline \(u_{1}(t)\) & \(u_{2}(t)\) & Optical pulses signal \\ \(f_{s1}\) & \(f_{s2}\) & Repetition frequency of optical pulses \\ \(\Delta t_{1}\) & \(\Delta t_{2}\) & Introduced delay to optical pulses \\ \(\varphi_{1}\) & \(\varphi_{2}\) & Phase shift introduced by PS \\ \(S_{0}(t)\) & - & Baseband LFM signal \\ - & \(S_{\rm r}(t)\) & Received multi-band signal \\ \(S_{1}(t)\) & \(S_{2}(t)\) & Output signal \\ \(k_{0}\) & \(k_{0}\) & Band number of the multi-band signal \\ \(t_{d1}\) & \(t_{d2}\) & Aperture fill time \\ - & \(f_{k}\) & Center frequency of the \(k\)-th band \\ - & \(n_{k}\) & Frequency component index match to the \(k\)-th band \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of key parameters for joint phase–frequency distribution manipulation method.  
Figure 1: **(a)** Phase–frequency–time mapping under the same aperture fill time. **(b)** Joint phase–frequency distribution manipulation method and **(c)** its process. IM, intensity modulation; BPF, band pass filter; LPF, low pass filter; PS, phase shifter; ADC, analog to digital converter.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 4 Model Situation
### Results of Models of Individual Scenarios
#### 4.1.3 Scenario 2. Pool Fire with Thermal Radiation Hazards  
The model for this scenario was meticulously constructed to simulate a pool fire involving ammonia, placing particular emphasis on the resulting thermal radiation and its profound effects on surrounding areas. This simulation incorporated atmospheric conditions, specifically a temperature of \(t\) = 20 \({}^{\circ}\)C and a wind speed of \(v\) = 1 m.s\({}^{-1}\), alongside the geographic location (GPS: 49.135727, 20.433323). Critical chemical properties of ammonia, including its molecular weight of 17.03 g.mol\({}^{-1}\) and boiling point of \(-\)33 \({}^{\circ}\)C, were integral to the calculations. By utilising ALOHA 5.4.7 software, danger zones were established based on thermal radiation thresholds, as defined by the Emergency Response Planning Guidelines (ERPG).  
Several factors contributed to the potential ignition of ammonia in this scenario, including improper storage conditions, equipment malfunctions, and external heat sources. Such conditions can lead to a pool fire when ammonia vapours come into contact with flames or elevated temperatures, thus creating a significant hazard.  
The ALOHA model identified three primary risk zones (ERPG-1, 2, and 3) detailed in Scenario 1.2, each highlighting life-threatening thermal radiation levels.  
This scenario highlights the importance of understanding both the thermal radiation risks associated with pool fires and the specific response strategies necessary to manage these unique hazards effectively. The relationship between thermal radiation intensity (\(q\)) and distance (\(r\)) can be quantitatively described using the inverse square law for radiative heat transfer, as follows:  
\[q(r)\propto\frac{1}{r^{2}} \tag{5}\]  
This expression indicates that the intensity of thermal radiation decreases with the square of the distance from the fire, underscoring the necessity for precise calculations to identify safe zones during a fire incident. The creation of this scenario also aimed to prepare for various types of emergency responses. While gas leaks present hazards primarily associated with inhaling toxic substances, fire incidents involve additional risks from thermal radiation and the potential for secondary fires. By simulating Scenario 2, tailored responses to the unique risks posed by each type of emergency can be effectively developed.
## 4 Results Interpretation and Drilling Verification
### Results Interpretation
The detection results are shown in Figures 3 and 4. In the surface wave dispersion curve after inversion, information such as the inflection point of the curve, tangent mutation point of the curve, and wave velocity change are used to stratify the results. Based on the information on the wave velocity difference in Table 2, sk09 can be divided into five layers and sk10 can be divided into eight layers according to comprehensive geological understanding. sk09 inferred that the first layer is silty clay and silty sand with a wave velocity of 159-446 m\(\cdot\)s\({}^{-1}\) above 18 m below the surface. The second layer is a soil-intercalated gravel layer between 40 m and 18 m below the surface, and the wave velocity is between 547 and 581 m\(\cdot\)s\({}^{-1}\). The third layer is a pebble layer with a wave velocity of 575-740 m\(\cdot\)s\({}^{-1}\) between 75 and 40 m below the surface. The fourth layer is a shale layer between 145 m and 75 m below the surface, and the wave velocity is between 825 m and 930 m\(\cdot\)s\({}^{-1}\). The fifth layer is a limestone layer with a wave velocity of 992-1400 m\(\cdot\)s\({}^{-1}\), at 145 m below the surface. sk10 inferred that the first layer is a silty clay layer at more than 10 m below the surface, with a wave velocity of 306 m\(\cdot\)s\({}^{-1}\). The second layer is a soil-intercalated gravel layer at 70 m\(\sim\)10 m below the surface, and the wave velocity is 504 m\(\cdot\)s\({}^{-1}\). The third layer is silty mudstone at 110 and 70 m below the surface with a wave velocity of 1093 m\(\cdot\)s\({}^{-1}\). The fourth layer is silststone with a wave velocity of 1400 m\(\cdot\)s\({}^{-1}\) at 180-110 m below the surface. The fifth layer is argillaceous siltstone with a wave velocity of 1303 m\(\cdot\)s\({}^{-1}\) at 210-180 m below the surface. The sixth layer is siltstone with a wave velocity of 1473 m\(\cdot\)s\({}^{-1}\) at 270-210 m below the surface. The seventh layer is mudstone at 310-270 m below the surface with a wave velocity of 1510 m\(\cdot\)s\({}^{-1}\). The eighth layer is limestone at 320-310 m below the surface with a wave velocity of 1653 m\(\cdot\)s\({}^{-1}\). Figure 3: Lithographic layering results of sk09 natural source surface wave microtremor detection of dispersive wave velocity.
## 4 Simulation Experiments and Result Analysis
### Analysis of Simulation Experiments
#### 4.2.1 Static Scenario  
Figure 7 depicts the real-time throughput and RTT of ICCDRL and its comparison algorithm in static scenarios.  
In Figure 7, the DRL-based DCC achieves a relatively high average throughput of about 910 KB/s; 90.6% of the total bandwidth and an average RTT of 130 ms, reflecting a better performance. Although the average throughput of NewReno and DRL-based DCC are close, NewReno has a larger RTT of about 142 ms. NewReno is relatively conservative in the congestion avoidance phase, achieving 88.7% network utilization. Westwood also achieves a higher throughput, but with an RTT of 170 ms, resulting in about 61 packet losses. This is because Westwood drops its _cwnd_ only when packets are lost. Cubic's RTT is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Scene size & 0.4 km\({}^{2}\) & Modulation Technology & OFDM \\ Scene Type & Two-way single lane & Packet Size(packet) & 50–100 MB \\ Number of vehicles & 0–150 & Number of data flows & 5–20 \\ Movement speed of nodes & 40–60 km/h & One-way time delay & 60 ms \\ Channel Type & Wireless Channels & Wireless Random Error & 0.0001 \\ Frequency & 5.9 GHz & Simulation time & 800 s \\ Bottleneck Bandwidth & 200 Mbps & Data transfer rate & 60 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters.  
Figure 6: Dumbbell Network Topology Diagram.  
only 65 ms, but the throughput is very low, with only 35.4% link utilization. In other words, Cubic sacrifices throughput for low latency. ICCDRL outperforms these algorithms not only in terms of throughput, but also in terms of RTT. It achieves the maximum throughput, reaching 91.9% of the bottleneck bandwidth, with a very low RTT of 76 ms.
# Map Matching Based on Seq2 Seq with Topology Information
## 2 Related Work
### Basic Concepts and Problem Description
#### 2.1.1 Road Network Dictionary  
A road network comprises a sequence of GPS trajectory points, which are interconnected based on the topological structure of the road network, thereby forming a directed graph denoted as G(V,E). Here, V represents the set of GPS points located on the roads within the graph, with each point being represented as P = [Ing, lat]. E denotes the set of road edges formed by these GPS points and is represented as l = [P1, P2, P3,... PN]. Building upon this concept, this article establishes a series of associated dictionaries to effectively express concepts pertaining to map matching problems.
## Appendix A  
We assumed that the subcarrier intensity modulation (SIM) is used in the relay R, the re-transmitted optical signal at this relay is given as [46; 47; 48]:  
\[r_{1}=\alpha_{1}s+n_{1},\]  
where \(\alpha_{1}\) the is the fading amplitude of the Rayleigh fading channel for the S-R link, and the \(n_{1}\) is an additive white Gaussian noise (AWGN) with the power spectral density of \(N_{01}\). The \(s\) indicates the RF signal transmitted from the source S.  
Thereafter, the optical signal is forwarded from R to D via the FSO channel. At the destination, the received optical signal is as follows:  
\[r_{2}=A_{0}I\{G[1+\eta(\alpha_{1}s+n_{1})]\}+n_{2},\]  
where \(A_{0}\) is a constant for propagation. \(G\) is the fixed relay gain at the relay R, and \(\eta\) is the electrical-to-optical conversion coefficient. \(I\) is a stationary random variable following aGamma-Gamma distribution for the FSO link, and the \(n_{2}\) is an AWGN with the power spectral density of \(N_{02}\).  
The overall signal-to-noise ratio (SNR) at the destination is as follows [9]:  
\[\gamma=\frac{I^{2}G^{2}\eta^{2}\alpha_{1}{}^{2}P_{1}}{I^{2}G^{2}\eta^{2}\alpha_{ 1}{}^{2}N_{01}+N_{02}}=\frac{\frac{\alpha_{1}{}^{2}P_{1}}{N_{01}}\frac{\eta^{2} \lambda^{2}}{N_{02}}}{\frac{\eta^{2}\lambda^{2}}{N_{02}}+\frac{1}{G^{2}N_{01}}},\]  
where the \(P_{1}\) is the power transmitted at the source. Here, we consider the relay as a fixed gain, regardless of the fading information on the first link, we can let \(C=\left(G^{2}N_{01}\right)^{-1}\). Therefore, the SNR can be rewritten as:  
\[\gamma=\frac{\gamma_{1}\gamma_{2}}{\gamma_{2}+C}.\]
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 2 Materials and Methods
### Experimental Data  
An extensive 3-year SCADA database of five Fuhrlander FL2500 2.5 MW wind turbine is presented. Data is generated by the wind turbine's SCADA, collected via an Open Platform Communications (OPC), following the IEC 61400-25 format. Therefore, the data is structured as follows: (i) wind turbines are represented with logical devices; (ii) physical systems or subsystems are represented with nodes. Every 5 min, events and statistics indicators are recorded. The reported values for each sensor are: minimum, maximum, mean and standard deviation. The database contains 312 analogous variables from 78 different sensors. All events in the database are originally labelled with one of the following three numbers: '0' indicates normal operation, '1' indicates a warning state (in this case the turbine is working but should be checked as soon as possible) and '2' indicates an alarm state (in this case the turbine is stopped). Alarms are very scarce in all wind turbine databases, because most of the time the turbine is working properly. A warning that is not properly checked and addressed could result in an alarm. Therefore, in our database we merge the two labels ('1' and '2') in the same group, reducing the problem to a 2-classes scenario (operation and failure). The goal will be to detect in advance when it will be that a wind turbine will start working with potential problems, which would lead to a warning or an alarm state. In the experiments, only some of the variables will be used, those related to the system of the analysed subsystem. The database was provided by Smartive ([[http://smartive.eu](http://smartive.eu)]([http://smartive.eu](http://smartive.eu))) and has been used in other publications [14; 23; 24].
## Appendix A Input Data of InVEST Models
### Sediment Delivery Ratio Module  
In this module, we also calculated a number of additional key parameters that are independent of the InVEST model. Of these, the soil erosive factor (\(K\)) was calculated by applying an erosion/productivity impact calculator model that is based on soil texture [69] using Equation (10), as follows:  
\[\begin{array}{ll}K&=\left\{0.2+0.3\exp\left[\frac{-0.0256 SAN(1-SIL)}{100} \right]\right\}\times\left(\frac{SIL}{CLA+SIL}\right)^{0.3}\\ &\times\left(1.0-\frac{0.25C}{C+\exp(3.72-2.95C)}\right)\\ &\times\left(1.0-\frac{0.7 SNI}{SNI+\exp(-5.51+22.9 SNI)}\right)\times 0.1317 \end{array} \tag{12}\]  
In this expression, \(SAN\), \(SIL\) and \(CLA\) denote the sand (%), silt (%) and clay fractions (%), respectively, while \(C\) refers to soil organic carbon content (%), and \(SNI\) is equal to \(1-SAN/100\). We also used a factor of 0.1317 to convert values from U.S. customary units to SI units.  
Because the rainfall erosivity factor, \(R\), equals the kinetic energy of rainfall, E, multiplied by the maximum rain intensity in a 30-min period I30 (cm h\({}^{-1}\)), use of this expression would require an unreasonably high frequency of rainfall events (i.e., one event every 30 min). We therefore customized the \(R\) factor calculation used in this study to a local context using half-month precipitation erosivity [70], a module that is already embedded in the Chinese soil loss equation [71], as follows:\[\begin{array}{l}\overline{R}_{ij}=\frac{1}{N}\sum\limits_{i=1}^{N}\left(\alpha \sum\limits_{d=1}^{m}{P_{di}}^{\beta}\right)\\ \alpha=21.586g^{-7.1891}\\ \beta=0.8363+\frac{18.144}{P_{d12}}+\frac{24.455}{P_{d12}}\\ P_{d12}=\frac{1}{N}\sum\limits_{i=1}^{N}\left(\frac{1}{m}\sum\limits_{d=1}^{m}{P _{di}}\right)\\ P_{y12}=\frac{1}{N}\sum\limits_{i=1}^{N}\left(\sum\limits_{d=1}^{m}{P_{di}} \right)\end{array} \tag{10}\]  
In this expression, \(R_{ij}\) denotes half-month rainfall erosivity (MJ mm h m\({}^{-2}\) h\({}^{-1}\) year\({}^{-1}\)) while \(P_{di}\) denotes effective rainfall for a given day, \(i\), in one half-month period. Thus, \(P_{di}\) is equal to actual rainfall if this value is larger than a threshold value of 12 mm, the standard for erosive rainfall in China. In all other cases, \(P_{di}\) is equal to zero, while the term m refers to the number of days in the half-month period. The terms \(\alpha\) and \(\beta\) are undetermined parameters, while \(P_{d12}\) denotes the average daily rainfall more than 12 mm, and \(P_{y10}\) refers to the annual average rainfall for days when this level is exceeded.  
We obtained values for the conservation practices (\(P\)) and cover type and management (\(C\)) factors from the USLE (handbook [46] and related studies [72] carried out in the TRHR (Table 1).
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 2 Related Work & Research Motivation
### Research Motivation  
Random mobility models are beneficial for multi-UAV reconnaissance due to their randomness and robustness to failure. In the path-planning reconnaissance, it is possible for targets or enemies to predict UAVs' movement so that they can hide for the expected reconnaissance time. On the contrary, the random mobility model makes it difficult to predict the reconnaissance time. In addition, the random mobility model can endure some malfunctions of UAVs because a UAV can visit anywhere in the reconnaissance area.  
However, one weakness of multi-UAVs reconnaissance based on the random mobility model is overlapped reconnaissance which is defined by simultaneous reconnaissance of the same area by two or more UAVs. As the scan area or camera scanning area is larger than the UAV, a part of the scan area can be overlapped by multiple UAVs, as shown in Figure 1a. If two UAVs fly towards a similar destination with an overlapped area, the overlapped area becomes the loss in terms of coverage efficiency. Even in the case where two UAVs at the same instant fly in the opposite directions with overlapped areas, the area scanned by one UAV can be re-visited soon by the other UAV. This is a cause of imbalance of reconnaissance. The overlapped reconnaissance can also lead to collision of UAVs.  
Figure 1a shows an example of overlapped reconnaissance area for random mobility model-based reconnaissance. We simulated Random Waypoint model in the range of 30 km \(\times\) 30 km. The UAV model follows Table 1 and the camera scan range is given by 2 km \(\times\) 1 km. Figure 1b shows the overlapped  
Figure 1: Overlapped reconnaissance and area: (**a**) An example of overlapped reconnaissance; and (**b**) overlapped area simulated for 7200 s.  
area for two hours by ten UAVs. As shown in Figure 1b, UAVs reconnoiter overlapped area frequently (about 40%) for two-hour reconnaissance.  
While much recent work has proposed efficient random mobility models for multi-UAV reconnaissance [12; 13; 14; 15], none of work considers overlapped reconnaissance. Thus, in this paper, we focus on avoiding overlapped reconnaissance by redesigning the existing reconnaissance mobility models. We provide overlap avoidance schemes for well-known random mobility models of multi-UAV reconnaissance. Those models include Random Waypoint, Random Markov Process, Enhanced Gauss-Markov, and Distributed Pheromone Repel models.
## 1 Introduction  
Interpretation performance varies greatly among radiologists when assessing lung nodules on computed tomography (CT) scans. A good example of such variability is the Lung Image Database Consortium (LIDC) dataset [1] for which out of 914 distinct nodules identified, delineated, and semantically characterized by up to four different radiologists, there are only 180 nodules on average across seven semantic characteristics on which at least three radiologists agreed with respect to the semantic label (characteristic rating) applied to the nodule. Computer-aided diagnosis (CADx) systems can act as a second reader by assisting radiologists in interpreting nodule characteristics in order to improve their efficiency and accuracy.  
In our previous work [2] we developed a semi-automatic active-learning approach [3] for predicting seven lung nodule semantic characteristics: spiculation, lobulation, texture, sphericity, margin, subtlety, and malignancy. The approach was intended to handle the large variability among interpretations of the same nodule by different radiologists. Using nodules with a high level of agreement as initial training data, the algorithm automatically labeled and added to the training data those nodules which had inconsistency in their interpretations. The evaluation of the algorithm was performed on the LIDC dataset publicly available at the time of publication, specifically on 149 distinct nodules present in the CT scans of 60 patients.  
A new LIDC dataset consisting of 914 distinct nodules from 207 patients was made publicly available as of June 2009. This has opened the way to further investigate the robustness of our proposed approach. Given the highly non-normal nature of medical data in general and of the LIDC dataset in particular (for example, on the set of 236 nodules for which at least three radiologists agree with respect to the spiculation characteristic, 231 of these nodules are rated with a 1 (\"marked spiculation\") and only five nodules are rated with ratings from 2 to 5 (where 5 \"no spiculation\"), we include in our research design a new study to evaluate the effects of balanced and unbalanced datasets on the proposed ensemble's performance for each of the seven characteristics. Furthermore, we investigate the agreement between our proposed computer-aided diagnostic characterization (CADc) approach and the LIDC radiologists' semantic characterizations using the weighted kappa statistic [4] which takes into account the general magnitude of the radiologists' agreement and weighs the differences in their disagreements with respect to every available instance. Finally, we include a new research study to investigate the effects of the variation/disagreement present in the manual lung nodule delineation/segmentation on performance of the ensemble of classifiers.  
The rest of the paper is organized as follows: we present a literature review relevant to our work in Section 2, the National Cancer Institute (NCI) LIDC dataset and methodology in Section 3, the results in Section 4, and our conclusions and future work in Section 5.