esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Manganese Ore as Oxygen Carrier  
While synthetic particles sometimes are considered for CLC, it seems reasonable to believe that mineral based materials would be highly favored for OCAC of biomass. This is because ash components are expected to interact with the bed material. For example, in a previous OCAC campaign in Chalmers Research Boiler with ilmenite as oxygen carrier it was established that alkali metals diffuse into the particle core, which led to the formation of KTi\({}_{8}\)O\({}_{16}\)[7]. Therefore, in order to avoid enrichment of ash components in the boiler bed, material likely will need to be replaced in regular intervals. In conventional biomass combustion, typically one third of the sand bed is replaced (\"regenerated\") each day. For waste incineration, the whole bed can very well need to be replaced on daily basis. While it is not obvious that this will be the same for oxygen carrying materials, the use of synthetic particles nonetheless seems inconvenient. High-performing synthetic oxygen carriers are likely to be at least one order of magnitude more expensive compared to minerals.  
Iron rich minerals such as hematite, magnetite and ilmenite are viable oxygen carriers [1,2]. However, iron based minerals have low reactivity with fuel gases compared to synthetic particles and are incapable of releasing significant amounts of gas phase O\({}_{2}\) via oxygen uncoupling [3].  
Manganese ores represent an interesting alternative. The theoretical performance of oxide materials can relatively easy be determined by means of thermodynamic equilibrium calculations [9]. Manganese oxide has higher oxygen transfer capacity by weight (7-10 wt % depending on temperature of operation) compared to iron oxide (3.3 wt %). This is because complete conversion of fuel is possible both for reduction of Mn\({}_{2}\)O\({}_{3}\)\(\rightarrow\)Mn\({}_{3}\)O\({}_{4}\) and for Mn\({}_{3}\)O\({}_{4}\)\(\rightarrow\)MnO, while iron is limited to the reaction Fe\({}_{2}\)O\({}_{3}\)\(\rightarrow\)Fe\({}_{3}\)O\({}_{4}\).  
Manganese (III) oxide also has the ability to release oxygen directly in gaseous form in inert atmosphere at temperatures around 800 \({}^{\circ}\)C [3]. For manganese ores, which are not pure but also contain elements such as iron and silicon, other more favourable mechanisms for oxygen release that work at higher temperatures could also be available [10,11].  
From a practical and economic perspective manganese ores are rich ores which often contain more than 45 wt % manganese, in addition to other potential oxygen carrying components such as iron oxides. This means that the active metal content is similar to iron based ores, while oxygen capacity and reactivity should be better. The price for unprocessed manganese ore generally is below 200 \(\upxi\)/ton (2015). This can be compared with the price of unprocessed ilmenite (\(\approx\)150 \(\upxi\)/ton in 2015), unprocessed iron ore (\(\approx\)50 \(\upxi\)/ton in 2015) and high grade silica sand used in CFB boilers (\(\approx\)50 \(\upxi\)/ton).
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
###### Abstract
We have seen in [16; 17; 18] that the GMP model often requires many coefficients adding extra complexity and overheads. Contrarily, in NN-based DPD, this issue does not exist, as it should be able to learn all of the effects causing the error function; therefore, explicit designing of all possible causes of distortion in a model is not required. For instance, as explained in [33], if we want to correct for IQ imbalance, a simple Volterra-based method is unable to correct it. This requires an additional methodology to be implemented in the predistorter that further increases the complexity of the system manifolds. By the nature of the fully connected NN, the NN can learn such impairments and correct them. The significant contributions of this paper are listed as follows:  
1. A novel NN-based DPD algorithm is proposed for the linearization of RoF links. 2. This NN-DPD method has been implemented with a new training method without the utilization of indirect learning architecture where a separate RoF-NN is used to first model the RoF link. Once modelled, by back propagating the error through RoF-NN, the DPD-NN is trained. 3. The complexity of the proposed algorithms is estimated. 4. For the first time, an experimental comparative study has been conducted where DPD-NN, DPD-MP and DPD-GMP are compared in terms of Adjacent Channel Power Ration (ACPR) and Error Vector Magnitude (EVM). The remaining part of the paper is organized as follows. Firstly, we discuss the architecture of the NN to perform DPD covering model overview, model characteristics, design and NN training. Then, a Volterra method-based methodology is discussed, and its architecture is explained. This is followed by complexity considerations of NN and GMP. Then, the section discusses the experimental setup utilized followed by a discussion of future directions. Finally, conclusions are presented.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
## 4 Numerical Results  
Two collections of 34,501 simulations, corresponding to two transmitter heights (namely 35 and 30 m) were taken from a specific software application implementing the ray-tracing algorithm [24].  
The area under consideration was urban, with randomly distributed dimensions of buildings and roads. The building heights ranged between 5 and 29 m, regardless of the base station's height. We could therefore characterize the case where the transmitter height is equal to 35 m as a scenario in which the base station is placed well above the rooftops (because even the tallest building would be 6 m below the transmitter). This assumption does not fully hold true when placing the transmitter at 30 m because buildings would exist that have only a 1 m difference in height with the placement of the transmitter, therefore leading to a situation where the base station is placed at a height close to that of the building rooftops.  
Both sets of simulations were split into a training set consisting of 80%, and the corresponding testing set comprised 20% of the simulations.
## 2 Materials and Methods
### HS-SPME-GC-FID/MS Determination of Volatile Compounds  
Volatile compounds were analyzed using SPME-GC-FID/MS. Each sample was analyzed in duplicate (\(n\) = 2). For SPME extraction, 2 g of homogenized currant sample was placed in a 10 mL vial, combined with 20 \(\upmu\)L of the internal standard propan-2-ol (220 \(\upmu\)g/L in water), and analyzed immediately. SPME was performed in headspace (HS) mode using a 50/30 \(\upmu\)m Divinylbenzene/Carboxen/Polydimethysiloxane (DVB/CAR/PDMS) fiber (Supelco, Bellefonte, PA, USA) under the following conditions: equilibrium time 10 min and extraction at 40 \({}^{\circ}\)C for 20 min.  
GC-FID analyses were performed using a TRACETM GC (ThermoQuest, Milan, Italy) with a DB-WAX capillary column (30 m \(\times\) 0.32 mm \(\times\) 0.5 \(\upmu\)m, J&W Scientific, Santa Clara, CA, USA) under the following conditions: injector temperature, 240 \({}^{\circ}\)C; splitless desorption, 10 min; and carrier gas, N\({}_{2}\) at a flow rate of 0.9 mL/min. The column temperature was programmed as follows: from 40 \({}^{\circ}\)C (held for 1 min) to 200 \({}^{\circ}\)C at a 5 \({}^{\circ}\)C/min gradient (held for 7 min). The FID temperature was 220 \({}^{\circ}\)C, with H\({}_{2}\) at 35 mL/min, air at 350 mL/min, and N\({}_{2}\) makeup gas at 30 mL/min.  
GC-MS analyses were performed on a TraceTM 1310 GC system with a split/splitless injector coupled with an ISQTM LT Single Quadrupole and a TG-WaxMS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.5 \(\upmu\)m, Thermo Fisher Scientific Inc., Waltham, MA, USA), using He as the carrier gas at a flow rate of 1 mL/min. The other GC conditions were as described above. The ion source temperature was 200 \({}^{\circ}\)C, and the MS was set in electron impact mode at 70 eV, scanning m/z 30-370 amu at a rate of 0.2 s.  
Volatile compounds were identified by comparing mass spectra with the NIST/EPA/NIH library, Version 2.0 (Gaithersburg, MD, USA), and confirmed by comparing retention times with reference standards and experimental retention indices (RI\({}^{\text{exp}}\)) with literature values from the NIST WebBook open-access database (RI\({}^{\text{lit}}\)). RI\({}^{\text{exp}}\) values were determined using a mixture of n-alkanes (Merck, Darmstadt, Germany) ranging from C\({}_{8}\) to C\({}_{20}\). Identified volatiles were semi-quantified using propan-2-ol as an internal standard, using Equation (1).  
\[c=A_{c}/A_{is}\times c_{is} \tag{1}\]  
where \(c\) represents relative concentration of analyte (\(\upmu\)g/kg), \(c_{is}\) concentration of the internal standard (220 \(\upmu\)g/L), \(A_{c}\) peak area of analyte, and \(A_{is}\) peak area of internal standard.  
The method is based on the method described and validated in our previous study [27] with minor modifications.
## 8 Experimental Setup and Results
### Experimental Results  
To demonstrate the practicality of the proposed approach, we have performed the simulation under the same real experiment parameters and presented them side-by-side in Figures 17-19. The experimental and simulation results have no significant differences. Therefore we can say that the proposed method successfully balances between the three objectives, which indicates that all tasks were assigned on average to a robot that satisfied their required quality level as shown in Figure 17. Moreover, tasks are distributed among the available robots nearly equally, as shown in Figure 18. Consequently, robots travel the same distance on average, as shown in Figure 19.  
Figure 16: Experimental environment with three Turtelbot2 robots (\(R_{0},R_{1},\text{and }R_{2}\)) and 15 locations where a task can possibly appear.  
Figure 17: Average assigned task quality per Turtbot2 robot. The quality levels low (2), medium (5), and high (8) are the quality levels of robots \(R0,R1,\text{and }R2\), respectively.
# Insider Threat Detection Using Machine Learning Approach
## 3 Materials and Methods
### Feature Extraction  
One of the main problems with insider threat detection is the extraction of features throughout the feature engineering process. There is no rule regarding the number of features derived from each log source, and it has been found to be variable in different studies. The CERT insider threat dataset is seen as a relational dataset from which features may be manually derived from the relationships between the entities (files). All researchers try to manually derive as many features as possible to glean high-quality descriptive results. One powerful automated feature engineering tool that has gained the attention of many scholars working in relational datasets over the last couple of years is Deep Feature Synthesis (DFS). This algorithm captures features usually supported by human intuition and performs feature engineering for multi-table and transactional datasets commonly found in databases or logs. After the DFS process, the data are gathered so that every event relating to a user is represented in a separate feature vector. We build 1000 user-specific profiles based on user action sequences.  
Given that many of the DFS characteristics are categorical and hence unreadable by the ML methods we are using, the aggregated data must be transformed into the right format. We perform one-hot encoding for categorical values. Further, all columns containing null values are removed. DFS results in 69,737 features. DFS produces a large number of features that lead to the dimensionality curse issue (where the dimensions are complex and difficult to visualise). To determine the most important characteristics in the CERT dataset that have the greatest influence on the target variable, we employed PCA as a dimensionality-reduction technique. It produces a reduced set of the most important linear combinations of the initial set of features, which are uncorrelated features. After PCA, we obtain a final 553 features. We used SMOTE [52] balancing techniques because CERT is an imbalanced dataset, as the anomalous samples are significantly less than the standard samples. DFS, PCA, and SMOTE are described in the following subsections.
# Performance Analysis of Latency-Aware Data Management in Industrial IoT Networks +
## 7 Conclusions and Future Work  
In this paper, we efficiently regulate the data distribution in industrial IoT networks using proxies. Given a set of data, the set of consumer nodes and the maximum access latency that consumers can tolerate, we consider a method for identifying a limited set of proxies in the network where data are cached. We implement the method and evaluate its performance using the IoT-LAB testbed. Additionally, we validate a simulation model and use it for performance evaluation in larger scales and more general topologies. We demonstrate that the proposed method guarantees average access latency below the given threshold and outperforms traditional centralized and even distributed approaches. The next step is to take into account limited bandwidth in the network, which can lead to congestive collapse, when incoming traffic exceeds outgoing bandwidth.  
Conceptualization, T.P.R., A.P. and M.C. Methodology, T.P.R., A.P. and M.C. Software, T.P.R. Validation, T.P.R., A.P. and M.C. Formal analysis, T.P.R. Investigation, T.P.R. Writing, original draft preparation, T.P.R. Writing, review and editing, T.P.R., A.P. and M.C. Visualization, T.P.R. Supervision, M.C. Project administration, A.P. and T.P.R. Funding acquisition, A.P. and M.C.  
This work has been partly funded by the European Commission through the FoF-RIA Project AUTOWARE: Wireless Autonomous, Reliable and Resilient Production Operation Architecture for Cognitive Manufacturing (No. 723909).  
The authors declare no conflict of interest.
## 4 Discussion
### Data Dissemination, Validation and Accounting for Uncertainties  
Data dissemination needs to respond to several key questions. Such key questions relate to (i) how have data been produced? (clear documentation); (ii) how have data been validated? and (iii) what are the underlying uncertainties in data? As such, it is essential to quantify uncertainties in the produced maps [70]. To be of true value and gain trust, any map or (socio-economic or demographic) variable prediction should be associated with an uncertainty level or confidence interval. Uncertainties should be reported as an overall summary for the entire dataset and spatial information associated with a specific geographic location (e.g., a grid cell in the case of the gridded system). Thus, the three elements of uncertainties need to be reported along with data (i) uncertainties in data (e.g., input data used as input for training the model) usually called aleatoric uncertainty; (ii) uncertainties in the prediction of the model, usually called epistemic uncertainty and (iii) spatial uncertainty (e.g., related to the general geography of an area). As EO experts, we need to understand when aiming to support evidence-based policymaking and to promote data-driven policy- and decision-making that the quality of data products needs to be carefully assessed as well as communicated in a way that non-EO experts can understand.  
In addition to uncertainty quantification, there is a growing interest in making machine learning and deep learning models more interpretable and understandable, aiming at neural networks that provide understandable justifications for their output, leading to insights about the inner workings [89; 90]. A clear response to the black box problem of AI algorithms and the reduction in the technical barrier. For example, in several AI-based outputs, systematic biases can be observed [91] but are difficult to explain, which endangers the easy interactions with users and trust in data.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 4 Numerical Simulations
### Orbit Injection with Temporary Propulsion Failure  
One of the main advantages of driving the spacecraft with a closed-loop control law is that the guidance strategy is also effective in nonnominal conditions because the feedback control law is based on the actual state of the vehicle. For the purpose of underlining and using this property, stochastic temporary failure is introduced to point out the capability of the feedback strategy to drive the spacecraft toward the operational orbit also in this situation. More precisely, it is assumed that a random failure occurs during the orbital transfer, with starting time uniformly distributed in \([0,70]\) days. The propulsion system is unavailable for a random interval of time, with uniform distribution in [5; 10] days.  
A Monte Carlo (MC) campaign, also termed MC 1, composed of 1000 simulations, is run to check if the feedback orbit control is capable of driving the vehicle toward the desired orbit, despite the temporary failure. This analysis leads to identifying the effect of the stochastic failure on the transfer duration and consumption. The results of the Monte Carlo analysis are described in Figures 11-15. For all the simulations, nonlinear orbit control succeeds in injecting the vehicle into the desired orbit in spite of the temporary failure. In fact, all the orbit elements converge to their desired value, ensuring the acquisition of the operational orbit in less than 95 days. Moreover, no impact with the Martian surface takes place, as shown in Figure 14 (the minimum altitude is 470 km). Table 3 reports the mean value and the standard deviation for the convergence time and the final mass ratio.  
Figure 11: Monte Carlo analysis 1: semimajor axis (**left**) and eccentricity (**right**).  
Figure 10: Maintenance phase: time histories of the thrust acceleration magnitude (**top**) and the true anomaly (**bottom**).  
Figure 14: Monte Carlo analysis 1: radius (**a**) and related zoom (**b**); the dotted line represents the Martian radius.  
Figure 12: Monte Carlo analysis 1: inclination (**left**) and RAAN (**right**).  
Figure 13: Monte Carlo analysis 1: argument of periapsis (**left**) and mass ratio (**right**).  
Figure 15 is useful to underline the effects of the failure duration on the propellant consumption and on convergence time. A linear correlation appears between the time of convergence and failure duration, i.e., if the failure persists for more time, then the orbit transfer has a longer duration. On the other hand, the duration of the failure exhibits no apparent correlation with the final value of the mass ratio.
# Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
## 5 Morphing Attack Detection  
In this section, we present a summary of the most recently proposed techniques for the automatic detection of morphing attacks. It must be stressed that it is a problem that has sparked the interest of many research teams with great efforts being directed towards its solution. This has turned the relevant bibliography into a very active landscape, resulting in numerous published articles covering a great variety of feature extraction and classification techniques.  
In general, MAD is treated as a binary classification problem in the bibliography. Given an input image, the goal is to infer the class it belongs to, namely, decide whether it represents an original image, or it is the product of morphing. As it is the case with the vast majority of classification problems, the decision is carried out in a feature space, i.e., it is performed by utilizing the values of selected features of the input. Based on the information available at the time of detection, the proposed techniques can be categorized as on- or off-line. In the first case, the system tries to decide whether an image is the product of morphing by utilizing a live capture as reference, while in the second case, the decision is based in the image itself. A further possible categorization is based on the type of the classification framework used, namely, whether the features used for classification are automatically learned, or engineered.
## 3 Objectives
### Program and Mission Objectives  
#### 3.1.1 University of North Dakota Small Satellite Objectives  
Several small spacecraft design and development projects are ongoing at the University of North Dakota. These projects share several common goals and objectives which are presented below.  
Primary:  
To advance the art, science, management and engineering practices associated with the development of small spacecraft while producing inspirational opportunities for student research participation.  
Secondary:  
1. To advance the capability of the University of North Dakota and other North Dakota-based institutions with regards to small spacecraft development to continuously prepare for ever greater future opportunities.
2. To assure that a level of risk commensurate with the importance of success is undertaken throughout program missions.
3. To demonstrate management, scientific and ethical standards and practices that will pave the way for a successful long-term program.
## 4 DRCE-IS Combined with D-Vine Copula and SNMM to Assess the Power System Reliability
### Pre-Simulation Stage of DRCE-IS
#### 4.2.3 IS PDF of the Wind Speed and Load Based on D-Vine Copulas and SNMM  
Load and wind speed are represented by continuous RVs, so we need to acquire the IS joint PDF, rather than the PMF. According to the basic idea of CE-IS, there is no restriction to \(f(\cdot;\mathbf{\omega})\) in (19). Actually whatever the form of \(f(\cdot;\mathbf{\omega})\) is, we can obtain a practical IS PDF by optimizing \(\mathbf{\omega}\).  
For simplicity, we select the multivariate Gaussian PDF as the IS joint PDF, in which all variables are supposed to be independent. The expectation \(\mathbf{\mu^{LW}}\) and standard deviation \(\sigma^{LW}\) are the parameters to be optimized, just like \(\mathbf{\omega}\) in (19). Therefore, when we update \(\mathbf{\mu^{LW}}\) and \(\sigma^{LW}\), the objective function can be represented by:  
\[\max_{\mu^{LW},\sigma^{LW}}\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})\ln \varphi(x_{s}^{LW};\mu^{LW},diag(\sigma^{LW}))+C, \tag{38}\]  
where \(diag(\cdot)\) is a transformation by which a vector is converted into a diagonal matrix.  
Similarly, with the processing of the PMFs, (38) is solved, whose solutions are the updating formulae for \(\mathbf{\mu^{LW}}\) and \(\mathbf{\sigma^{LW}}\), shown as:  
\[\mu_{t}^{LW}=\sum\limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})x_{s}^{LW}/\sum \limits_{s=1}^{n_{s}}I(x_{ss,s})Q(x_{ss,s})\,, \tag{39}\]  
\[\left(\sigma_{t}^{LW}\right)^{\circ 2}=\frac{\sum\limits_{s=1}^{n_{s}}I(x_{ ss,s})Q(x_{ss,s})\left(x_{s}^{LW}-\mu_{t}^{LW}\right)^{\circ 2}}{\sum\limits_{s=1}^{n_{s}}I(x_{ ss,s})Q(x_{ss,s})}\,, \tag{40}\]  
where \((\cdot)^{\circ 2}\) stands for the Hadamard square.  
The LRF of wind speed and load is defined based on the D-vine copulas and SNMM, because we need to combine them to obtain the original joint PDF. The LRF is expressed as:  
\[Q(x^{LW})=D(x^{LW})\prod\limits_{lw=1}^{n_{lw}}f_{lw}(x_{lw})/\varphi(x^{LW} ;\mu^{LW},diag(\sigma^{LW}))\,, \tag{41}\]  
where \(n_{lw}\) is the number of RVs of wind speed and load. \(f_{lw}(\cdot)\) is the marginal PDF modeled by SNMM.