esa-sceva/satcom-chunk-collection huggingface dataset:
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 4 Discussion
### Geochemical Signatures of Elemental Abundances
#### 4.1.1 Major Oxides  
According to the illustration method of spider diagrams, the 11 major oxides were first sequenced descending on their abundances of moon samples. Then, moon samples were normalized based on the MORB of the Earth. Finally, the spider diagrams of major oxides of moon samples were derived and illustrated in Figure 3.  
With respect to the MORB, the moon sample (labeled as Moon in Figure 3) is clearly rich in Cr\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) and poor in K\({}_{2}\)O in terms of their concentrations. The moon sample with low-Ca (labeled as Low-Ca in Figure 3) is also clearly rich in Cr\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) and poor in K\({}_{2}\)O, like the moon sample. While the moon sample with high-Ca (labeled as High-Ca in Figure 3) is clearly rich in Cr\({}_{2}\)O\({}_{3}\) and Al\({}_{2}\)O\({}_{3}\) and poor in Na\({}_{2}\)O, TiO\({}_{2}\), MnO, and FeO in terms of their concentrations. In the three abundances of moon samples, the moon sample with low-Ca is closer to the MORB, except for the clear signature of higher concentrations of Cr\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) and lower concentrations of K\({}_{2}\)O.  
With respect to the MORB, the moon samples (including Moon, low-Ca, and high-Ca in Figure 3) are clearly enriched in Cr and depleted in Na in the diagrams. Here, the terms of rich and poor are used for concentrations (comparison between/among samples), and the terms of enriched and depleted are used for diagrams (comparison among elements in the same sample).
## 1 Introduction
An objective function, \(F_{ob}\), describes it, that is, the disparity between the projected model and empirical data. (ii) The objective function, a mathematical function that measures the performance of a solution in optimization or decision-making problems, is optimized by formulating it as a multivariate Gaussian distribution, considering the experimental variances weighted by the measurement uncertainty. During this step, the goal is to minimize or maximize the probability of obtaining experimental measurements within the defined criteria. (iii) Evaluate the parameters [13] by maximum likelihood estimation (MLE), a statistical method used to find the best values for the parameters of a model. See Figure 1.  
DR has been utilized in various industrial applications, specifically in energy. These include proposals for potential modifications of data validation and reconciliation methods to enhance the accuracy of the emission characteristics of fuel co-combustion processes [14], the presentation of a data processing framework for industrial process decision-making based on operating regimes [15], and the control of unaccounted-for gas in natural gas pipelines [16]. Additionally, studies are being conducted on the simulation of mixed alcohol synthesis [17], the techno-economic analysis of a CO\({}_{2}\) capture process [18], and the forecasting of wind power for the next day [19]. These research efforts aim to improve the understanding and optimization of various industrial processes. Furthermore, investigations are being conducted into analyzing aero engine measurement data [20] and optimizing production processes [21]. These studies focus on enhancing efficiency and performance in specific sectors. Other areas of research include the control of thermal processes [22], the detection of anomalies in multiphase allocation systems [23], and the monitoring of industrial petrochemical plants [24]. These endeavors contribute to developing advanced monitoring and control techniques in industrial settings. Moreover, there have been reviews that specifically examine problems related to chemical reactors [25] and explore the applications of DR (Data Reconciliation) in Analytical Chemistry [26]. These reviews provide insights into specific domains and offer potential solutions to existing challenges. Lastly, research has been conducted on the connection of DR with the guard band concept in evaluating the conformity of fuel products [27; 28], as well as the identification and treatment of outliers [29]. These studies address important aspects of data analysis and quality control. The mentioned research efforts cover many topics, from process optimization and control to data analysis and anomaly detection. They enhance the efficiency, accuracy, and decision-making capabilities in various industrial sectors. Although DR is frequently used in engineering processes, it is rarely applied to Analytical Chemistry applications. The objective of this study was to explore this topic as a pragmatic and applicable parameter for assessing disputes concerning the marketing of petroleum products.
## 3 Results  
### Airborne Hyperspectral Imagery  
Image classification allowed separating the two different vegetation layers and the non-vegetated surfaces. Table 3 shows the percentage per category within the 1 km\({}^{2}\) square centered on each EC tower and date: \"grass\" represents between 68.42% and 79.49% of the surface as observed by the sensor, whereas \"trees\" represent between 13.83% and 21.55%. \"Soils and roads\" coverage is lower than 2%. In addition, \"water and shadows\" range between 3.98% and 11.55%.  
Figure 2 shows the mean spectra of the vegetation classes separated (Figure 2a,b), mixed (Figure 2c) and of all the classes (Figure 2d). As can be observed, \"grass\" (Figure 2a) shows a large variability, whereas \"trees\" (Figure 2b) optical properties are more stable. This is in agreement with the phenological behavior of each class. Since \"grass\" is more abundant, it governs \"vegetation\" _HDRF_ variability, whereas the remaining classes have a low effect. The figure also shows that the differences between grass spectral features of the three towers increase in 2015 after fertilization.
## 4 Proposed Framework
### EdgeGuard Framework
#### 4.1.4 Block Generation and Propagation  
EdgeGuard employs a proof of work (PoW) mechanism for secure block generation and propagation. This process unfolds in three key steps: hash generation, block generation rate determination, and block propagation with ledger update. In the hash generation step, a miner \(m\) in the network computes a hash value \(H\) by iteratively modifying a nonce value \(N\). The goal is to find a hash that satisfies the condition expressed in Equation (31):  
\[H(N)<T \tag{31}\]  
Here, \(H\) represents the hash function, \(N\) is the nonce, and \(T\) denotes the target value that defines the PoW difficulty. The block generation rate, denoted as \(\lambda\), is inversely proportional to the PoW difficulty, which is reflected in the target hash value \(T\). This relationship is captured in Equation (32):  
\[\lambda\propto\frac{1}{T} \tag{32}\]  
This inverse relationship implies that a more stringent target value \(T\) results in a lower \(\lambda\), thereby reducing the frequency of block generation. As stated in Equation (33), upon generation of a valid hash by a miner, the new block \(B\) is verified, approved, and disseminated to all miners. At this point, the miners cease their proof of work calculations and append \(B\) to their local ledgers.  
\[\text{If }H(T(\vec{u}_{i}^{t+1})\parallel n)<\text{Target, then }B\text{ is added to all local ledgers} \tag{33}\]  
The proof-of-work method, thus, ensures integrity and security within the EdgeGuard architecture and provides a good base for decentralized storage and validation of the model changes within the IoMT network.
# Building Information Modelling Facility Management (BIM-FM)
## 6 Barriers Affecting the Implementation of BIM-FM
### Lack of Data Retention  
[PERSON] et al. [22] suggest that \"when no BIM exists yet, it is crucial to do the prior analysis of stakeholders' information requirements to optimize geometric modelling and information handling effort\". In a study focused on COBie and BIM-FM integration from a Mechanical and Electrical (M&E) contractor perspective, [PERSON] and [PERSON] [55] stated that \"interviewees maintained that early engagement from the client is considered the most important factor to produce accurate COBie information\", asserting that ill-defined information requirements lead to consistent changes and errors in the scope.  
However, it is worth noting that even if all the information requirements for the existing assets are known, much of this data can be erroneous or missing, a likelihood which increases with the age of the asset [60]. Furthermore, since FM covers the whole operational life of an asset, it is not unreasonable to assume that new data will need to be added to the system over time to reflect any changes, whilst retaining the historical data. However, this functionality is not yet inherent in all proposed BIM-FM systems since it is not a utility necessarily required in construction, which the majority of BIM authoring tools were created for. For instance, [PERSON] et al. [79] found that when a model object was majorly updated or deleted, previous information associated with that object was removed from the model and only accessible by querying their FM database. As such, to avoid this data retention problem from recurring, a BIM-FM system should allow new data to be added over time, without compromising existing data, and whilst creating a historic record of any changes.
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 5 Conclusions  
The possibility of manufacturing smart objects is very appealing, reducing time, cost, and assembly tasks. Additive manufacturing (AM) technologies seem to be the key enabler to achieve this goal, and several fields, like the biomedical one, can take full advantage from smart devices. Fused filament fabrication (FFF) is the most inexpensive AM technology. It has recently been employed to manufacture smart objects with embedded sensors in a single-step fabrication cycle. This research presents the characterization of a low-cost 3D-printed strain sensor fabricated using FFF technology by using a commercial conductive material composed of a polylactic acid (PLA)-based filament doped with carbon black and carbon nanotubes (CNT). A high repeatability industrial robot was used to perform automatic tests, following suitable force/displacement profiles. The sensor exhibited a sensitivity of 0.077 \(\Omega\) g\({}^{-1}\) and 12.54 \(\Omega\) mm\({}^{-1}\).  
Since sensor resistance also depends strongly on temperature, a compensation method was developed based on the resistance change of an unsolicited dummy sensor. The method proved effective; however, experimentation revealed the presence of nonnegligible sensor hysteresis and material viscoelasticity that will be the object of further investigation.  
Overall, for two-step profiles (on-off), the reconstruction of the applied force/displacement led to acceptable performances. This suggests the suitability of these sensors to detect impulse forces or the presence of a load. For instance, they could be employed for collaborative robotic applications to interact with humans or delicate objects since the FFF process allows directly integrating these sensors into more complex objects in one single fabrication cycle and adjusting to the shape of the fabricated smart structure.  
Conceptualization, M.A.R. and G.S.; Methodology, A.M.L.L., F.A., G.P. and A.D.N.; Supervision, A.M.L.L. and F.A.; Validation, M.A.R. and G.S.; Writing--original draft, M.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflict of interest.
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 3 Problem Statement and Mathematical Model
### Problem Statement  
The EVRPD was defined as follows:  
* Recipients (customers) were defined (the location of each recipient was known, which translated into the determination of the distance-cost grid (network) between recipients and the travel time from recipient to recipient);
* Shipments (parcels) must be delivered or picked up from the customer;
* Several shipments (parcels) could be addressed to a customer (parcels should be delivered at once; if there are too many of them, e.g., the capacity of the drone was reached, we replaced one customer with two or more (we introduced the so-called virtual customers who had the same location)), which created significant novelty in relation to the VRPD;
* Various types of shipment were considered;
* Mobile points (mobile hubs--MHs) were used for drone take-offs;
* Drones and vehicles (i.e., trucks) were used to transport shipments (trucks to deliver drones to MHs and drones to deliver shipments to customers);
* The number of MHs was limited;
* Locations for MHs were known;* The selection of MHs was optimized for a given set of shipments which created novelty in relation to VRPD;
* The number of drones and trucks was limited;
* Each drone was characterized by: \(\bigcirc\) Load capacity (which determined the number of shipments (parcels) it could carry); \(\bigcirc\) The maximum range (which determined the time in which the parcels must be delivered);
* In general, a drone could visit several clients, which was especially useful for a network of clients, e.g., a network of pharmacies, a network of car repair shops, etc.;
* The delivery route for all drones was minimized;
* The allocation of shipments to drones and drones to vehicles was determined, which introduced novelty in relation to the VRPD;
* If it was impossible to deliver a set of shipments, the shipments preventing delivery were found;
* Situations where a specific drone \(dx\) was damaged or unavailable and could not take part in deliveries were also considered.  
The load capacity was balanced at individual road stages, i.e., after the shipment was delivered or picked up from the customer, its value was updated.
## 3 Results
### An Accuracy Assessment of CHM-Based Height
#### 3.3.2 Effect of Tree Height on Measurement Accuracy  
Height measurement error generally showed a decreasing trend as tree height increased (Figure 13). The variance of error also decreased as tree height increased. Standard deviation of height error was 10.0 m when tree height was in the 6-9 m range and 3.6 m when tree height was in the 39-42 m range. In particular, the height error calculated from the 2018 LiDAR data showed more negatively skewed distribution compared to other LiDAR acquisition years.  
Figure 11: Height measurement error with respect to LiDAR point density. Boundaries of upper and lower whisker are the ninety-seventh and third percentiles. The number above each box plot is median, and the number below box plot is number of data points. CHM: canopy height model; Ht: height.
## 2 Materials and Methods
### Data Editing  
The first important information from the ISOBUS was the wheel-based machine speed (WBMS), which is the value of the speed of the machine as calculated from the measured wheel or tail shaft speed [19]. It is specified in meters per second and, similar to the corresponding timestamps, it was derived from the WBSD messages using the suspect parameter number (SPN). Expressed in hexadecimal values, the WBMS has the SPN \"1862\". In the next step, the RHS messages from the ISOBUS were split into their time stamps and the rear hitch position (RHP), which has the SPN \"1873\". The RHP is the measured position of the rear three-point-hitch, expressed as a percentage of full travel, whereby 0% indicates the full down position, and 100% the full up position [19]. Finally, the D-GNSS latitude and longitude according to the world geodetic system 84 (WGS 84), as well as their timestamps were deduced from Msg2 (see Table 1). The acquired data for the signals RHP and WBMS are presented in Figure 5a,b, respectively.  
To enable a concurrent analysis at a specific point and to increase the resolution of the D-GNSS information, a linear interpolation of the WBMS, as well as the D-GNSS latitude and longitude, was performed using the MATLAB function \"interp1\". The RHP time stamps were the query points  
Figure 4: Flowchart of the main data analysis steps. The utilized MATLAB functions are also indicated.  
Figure 5: Acquired data for: (**a**) rear hitch position (RHP) signals; and (**b**) wheel-based machine speed (WBMS) signals.  
for the interpolation, whereas the sample points were the time stamps of the WBMS and D-GNSS. As the positioning error of the used D-GNSS receiver, as well as the excitation of the tractor cabin, caused a jittering of the recorded track, a Savitzky-Golay filtering was applied using the MATLAB function \"sgolayfilt\". The polynomial order of the filter was set to one because it best fit the chronologic sequence of the recorded track. A frame length with a value of 25 was considered to give a smoothing that represents the real track of the rover in a suitable way.
# Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
## 3 Methodology and Solutions
### FFX Algorithm  
The Feistel structure serves as the foundation for all FFX algorithms, including FF1 and FF3-1, which have been established as the US National Institute of Standards and Technology (NIST) standard.  
A Feistel structure is created when reversible transformation is repeated numerous times, known as \"rounds\". The transformation is divided into three phases. First, data is divided into two segments, and a keyed function named the round function is applied to one segment of the data. For the next round, the roles of the two parts are switched to alter another segment of the data. The function F generates a repeatable hash-like value by using a user-specified symmetric block cipher. Figure 1 below shows the framework for both encryption and decryption for ten rounds, although FF1 and FF3-1 have ten and eight rounds, respectively.  
Figure 1: Encryption (**left**) and decryption (**right**) structure of FF1 and FF3-1 [23].  
Two strings of characters serve as the input (and output) data for each round; for FF1 and FF3-1, these strings will be numerals. The two strings are equal in length, and n represents the overall number of characters. The round function, denoted by FK in Round i, is applied to one of the input strings, marked by Bi, with the extra inputs of length n, tweak T, and round number i. (In Figure 1, these three extra inputs (n, T, and i are shown as the super-posed arrow with the proper values for i.) By applying modular addition--indicated by the plus sign \(\oplus\)--on the numbers the strings stand for, the result is utilized to change the other string, denoted by Ai. A temporary variable called Ci is used to name the string that represents the outcome number. For the following round, the names of the two components are switched, resulting in the modified Ai, or Ci, becoming Bi + 1 and Ai becoming Bi + 1.  
The Feistel structure for encryption and decryption is nearly interchangeable. There are three variations:  
1. The order of the round indices is changed;
2. The roles of the two components of the data in the round function are switched, so that, in addition to n, T, and i, Ai + 1 (instead of Bi) is used as the input to FK, and Bi + 1 (instead of Ai) is combined with the output to produce Ai (instead of Bi + 1)
3. Modular addition is changed to modular subtraction (the output of FK from Bi +1).  
The solution outlined in this article employs the NIST standard format-preserving encryption techniques FF1 and FF3-1.
## 3 Simulation Results and Empirical Results Verifications
### Experimental Equipment Establishment  
This study constructed a measurement system with a non-contact capacitive sensor that could measure and record signals that are based on the voltage values between the sensor and miniature transducer diaphragm using a signal acquisition card. The signals were transmitted to a computer and capacitive sensor sensitivity (air dielectric coefficient sectional area/distance = \(\varepsilon A/d\)) was used to measure the changes in the displacement of the miniature transducer diaphragm, i.e., displacement \(\mathbf{x}(t)\) in Equation (1). The high-precision capacitance displacement nano-sensor manufactured by the US company LION Precision [23] was used in the system; the sampling rate was according to National Instruments NI-DAQ 6251 (set to 48,000 Hz) [24]. The random sinus signals (Equation (24)) were used to actuate the transducer; the displacement signals were captured and transmitted to the computer for analyses and calculations. Figure 9 shows the interface and measurement procedure of the capacitive sensor system that is proposed in this study. With regard to the verification of the method proposed in this study, a capacitive displacement system and laser vibrometry displacement measurement system were used to measure the displacement of the miniature transducer diaphragm. In the measurement process, a power amplifier was used to generate 1.4 V\({}_{rms}\) random sinus signals. Large signals were used because the nonlinear characteristics that were produced in the metal diaphragm of the transducer need to be captured. The excitation signals were recorded using a voltage sensor. The voice coil current i\({}_{max}(t)\) was measured using a current sensor and the displacement \(\mathbf{x}_{max}(t)\) was measured using a capacitance displacement sensor. Laser beams of the laser displacement measurement system were concentrated at the center of the transducer diaphragm for measurements.  
Figure 8: Comparison of impedance curves at different measurement errors (\(\sigma\)= \(0.01,0.05,0.15\)), (**a**) \(\sigma\)= \(0.01\), (**b**) \(\sigma\)= \(0.05\), and (**c**) \(\sigma\)= \(0.15\).
## 3 Data Fusion
### State-of-the-Art Research on Data Fusion  
The process of data fusion mainly includes preprocessing, feature extraction, data fusion computation, and data compression. There are always some kinds of noise and interference in human physiological signals, which cause that collected signals cannot be used directly. In this circumstance, preprocessing is helpful, as it can filter noise from data and get useful information. The main preprocessing methods are piecewise linear representation, Fourier Transform (FT), Wavelet Transform (WT), high pass and low pass filters, mathematical morphology filters, Laplacian Transform, and Kalman filter, _etc._, which can be applied to remove noise. Feature extraction is carried out after preprocessing, which assembles representative data into feature vectors to distinguish among different actions or phenomena [61]. In this stage, researchers analyze information by means of Fast Fourier Transform, wavelet analysis, _etc._, to thus extract the features of collected signals. After that, the extracted features are used as input of data fusion computation methods which will generate fusion results. Data fusion computation on information of different types is difficult. Researchers found that activity classification algorithms and intelligent computing methods [24,25], including hierarchical methods, decision trees, Bayesian Network (BN), Artificial Neural Network (ANN), and Hidden Markov Model (HMM), are suitable for solving this kind of problem. Furthermore, the methods can effectively relieve the pressure of low recognition accuracy. Finally, data that gets preliminary treatment will be compressed before transmission, which will significantly reduce the total amount of data transmission in BSNs. In this way, power consumption is reduced [62]. In the aspect of data compression, several encoding methods are common techniques. In recent years, research on data fusion has achieved many fruitful results, and a number of research hotspots in the data fusion process area have come out, as well as combination of data fusion with other techniques and applications.