esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Age Assessment of a New Course of Irish Fly Ash as a Cement Replacement
## 4 Results
### X-ray Diffraction  
Figure 11 shows the XRD patterns for hydrating CEM I with no fly ash additions. The CEM I clinker peaks are shown at 12.1\({}^{\circ}\) 2\(\theta\) (C\({}_{4}\)AF), 31\({}^{\circ}\) 2\(\theta\) (C\({}_{2}\)S), 32.5\({}^{\circ}\) 2\(\theta\) (mainly C\({}_{3}\)S and C\({}_{2}\)S) and 35\({}^{\circ}\) 2\(\theta\) (C\({}_{3}\)S). C\({}_{3}\)S also overlaps with the calcite peak at 29.5\({}^{\circ}\) 2\(\theta\). C\({}_{2}\)S can be difficult to identify in XRD patterns, and it appears to overlap with the C\({}_{3}\)S peak at 32.5\({}^{\circ}\) 2\(\theta\), whereas C\({}_{3}\)S is more abundant. C\({}_{3}\)A, along with overlapping C\({}_{2}\)S and C\({}_{3}\)S, is observed at 32-33\({}^{\circ}\) 2\(\theta\).  
The main hydrates, including portlandite and ettringite, along with calcite, are observed as expected. Due to the presence of approximately 4.8 g/100 g of calcite in the clinker (see Table 3), permitted for CEM I cements under EN 197 [3], monocarbonate is the most abundant AFm phase [20; 21], as shown by the peak at 10.8\({}^{\circ}\) 2\(\theta\), which remains  
Figure 11: XRD patters from 5 to 35\({}^{\circ}\) 2\(\theta\) for CEM I.  
Figure 10: Concrete compressive strength results.  
as hydration continues over time. Ettringite is the dominant AFt phase and is stable over time, with dominant peaks at 9, 15.8, 19 and 23\({}^{\circ}\) 2\(\theta\).  
[PERSON] et al. [22] suggested that the main differences between plain and blended cements containing SCMs are best observed at low angles (8-13\({}^{\circ}\) 2\(\theta\)), where the main peaks of the AFm and AFt phases are found. Figure 12 shows the XRD patterns at these low angles for the three cement replacements here after 1, 7, 28, 60 and 100 days of hydration. Monosulphate, which has been shown to diffract at 9.8\({}^{\circ}\) in the literature [23], is not shown in the patterns, whereas monocarbonate is precipitated at 11.5\({}^{\circ}\), with a stable ettringite phase at 9.7\({}^{\circ}\) over time. Some hemiconate is shown at 11\({}^{\circ}\) at 7 days but decreases over time as monocarbonate is formed. These observations are consistent with previous studies [22; 23; 24]. Unfortunately, XRD patterns at 60 days for the 35% replacement samples are not available.  
Another significant effect of blended fly ash cements is the reduction in portlandite over time due to pozzolanic activity, where it is converted to additional C-A-S-H with a lowering of the Ca:Si ratio. The increase in alumina may also lead to a reduction in ettringite and increase in AFm compared to 100% CEM I cement. This leads to a reduction in overall volume that could result in lower strengths, hence fly ash additions in cement standards being limited to 35%. This will be studied in the thermodynamic analysis.  
Figure 12: XRD patterns of blended fly ash cements with 0, 10, 20 and 35% replacement levels.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 2 Materials and Methods  
### Study Area  
The study area (35\({}^{\circ}\)07\({}^{\prime}\)59\({}^{\prime\prime}\) N\(\sim\)35\({}^{\circ}\)08\({}^{\prime}\)0.822\({}^{\prime\prime}\) N; 113\({}^{\circ}\)15\({}^{\prime}\)26.91\({}^{\prime\prime}\) E\(\sim\)113\({}^{\circ}\)15\({}^{\prime}\)28\({}^{\prime\prime}\) E) located at Wuzhi County, Jiaozuo City, Henan Province, as shown in Figure 1. It belongs to the Yellow River and Qinhe River alluvial plain in the Huang-Huai-hai Plain. The average altitude is 92 m, the climate type is warm temperate continental monsoon climate, the annual average precipitation is 575.1 mm, and the annual average temperature is 14.4 degrees Celsius. There are 211 frost-free days during the year. The main soil type is tidal soil, and the soil is fertile and conducive to crop cultivation. The main crop types in this area are winter wheat and summer corn. The study area was divided into 90 rectangular blocks with 18 rows and 5 columns, each rectangular block with an area of 4 m \(\times\) 3 m. Six different cultivars of winter wheat (Aikang58, Bainong207, Jimai22, Jiamai8, Nongda5181, Pumai168) were selected in this study. They all belong to mid-late maturity varieties, 228 days or so after sowing can be harvested. These six types of winter wheat were sowed on 30 October 2022 from north to south and coded from type 1 to type 6. Every three rows had the same type of winter wheat cultivars. Nitrogen fertilizer was urea (containing 46% pure nitrogen), applied at a rate of 90 kg/ha before sowing and 90 kg/ha during the tillering stage. The phosphorus fertilizer was calcium superphosphate (containing 12% P\({}_{2}\)O\({}_{5}\)), and the potassium fertilizer was potassium chloride (containing 60% K\({}_{2}\)O), both applied at a rate of 135 kg/ha simultaneously with the nitrogen fertilizer before sowing. The UHD185 hyperspectral data were collected at the booting stage of winter wheat to carry out the following fine classification experiments.  
Figure 1: Location and orthophoto of the study area: (**a**) Map of China; (**b**) Map of Jiaozuo; (**c**) Orthophoto of the study area.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 5 Food Safety and Traceability in the Wine Industry
### Detecting and Managing Microbiological Risks
#### 5.2.2 Identifying and Monitoring Mycotoxins  
Mycotoxins are toxic compounds produced by certain molds that can contaminate grapes and affect wine safety. The presence of mycotoxins such as ochratoxin A (OTA) is a major concern for winemakers, as these compounds are harmful to human health and impact the sensory qualities of wine. AI systems utilize spectroscopic techniques, such as near-infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy, to detect mycotoxins in grapes and wine. Spectroscopy provides a rapid, non-destructive method for analyzing samples. When combined with machine learning algorithms, it can identify specific mycotoxins based on their unique spectral signatures.  
Research has shown that surface-enhanced Raman spectroscopy (SERS) is effective for detecting OTA in wine, providing a rapid and cost-effective alternative to traditional methods [127]. Additionally, non-invasive spectroscopic methods like two-photon-induced fluorescence (TPIF) have been employed to detect mycotoxins, offering advantages in terms of sensitivity and the ability to reduce background interference [128].  
Machine learning models, such as support vector machines (SVM) and neural networks, are trained on spectral data to recognize patterns associated with mycotoxin contamination. These models can detect even low concentrations of mycotoxins, enabling early intervention to prevent contaminated grapes from entering the production process. For example, a novel enzyme-free biosensor using a toehold-mediated catalytic hairpin assembly demonstrated high sensitivity in detecting multiple mycotoxins, including OTA, aflatoxin B1, and zeanelenone in wine samples [129].  
Real-time monitoring of mycotoxin levels is also crucial for maintaining wine safety. AI-driven systems equipped with biosensors can continuously monitor mycotoxin levels during the winemaking process. These biosensors, often based on immunoassay or molecular recognition elements, provide specific and sensitive detection of mycotoxins. For instance, the development of a multiplex mycotoxin SERS immunoassay using functional gold nanotags has shown significant potential in providing rapid and accurate mycotoxin detection in wine [130].  
The integration of biosensors with AI allows for real-time data analysis and decision-making. When mycotoxin levels exceed safe thresholds, the AI system can alert winemakers and suggest corrective actions, such as removing contaminated batches or adjusting processing parameters to mitigate risks. This proactive approach enhances the overall safety and quality of the wine. Techniques such as liquid chromatography-tandem mass spectrometry (LC-MS/MS) have also been optimized to efficiently determine multiple mycotoxins in wine, further supporting these AI-integrated systems [131].
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 1 Introduction
Amongst numerous MM estimate approaches, the interacting multiple model (IMM) filter, which is one of the highly effective state estimation algorithms, can be applied to multi-sensor data fusion [18; 19; 20; 21]. Meanwhile, this approach is capable of estimating the state variables of a dynamic system with numerous behavior models as a probability switching approach. However, there are few real tests to verify its validity. In this paper, we present an enhanced adaptive IMM (AIMM) filter method for real sea trials. This paper is organized as follows. Firstly, a full mathematical model of the SINS/GPS navigation system is analyzed. Then the AIMM method is proposed to increase performance of GPS/INS integrated navigation system, and the sea trial is applied to compare and analyze the performance of different kinds of integration methods. Lastly, main conclusions are offered and discussed.
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 4 Evaluation
### Simulation Results
By analyzing the results, we conclude that CAPONE saved 25% and 14% of energy compared to SDN-FANET and SDN-FANET + PREDICTION, respectively, at the end of 300 simulation seconds. This is because SDN-FANET considers periodic control message dissemination, which consumes more energy for nodes that are sending and receiving the message. On the other hand, SDN-FANET + PREDICTION considers only prediction algorithms to reduce the number of control message transmissions. Finally, CAPONE considers a clustering approach and prediction algorithms to reduces the required number of control messages for SDN-FANET operations. Figure 4: PDR for different control plane message management mechanisms. Figure 5: Position error for different control plane message management mechanisms.
# Real-Time 3D PET Image with Pseudoinverse Reconstruction
## Appendix A  
The expression of a regularization for the PINV can be derived mathematically from the algorithm of the iterative Landweber method [55; 56; 63]:  
\[X_{i+1}=X_{i}+A^{T}(Y-A\cdot X), \tag{11}\]  
Assuming an initial image of zeros:  
\[X_{0}=(0), \tag{12}\]  
\[X_{1}=A^{T}Y, \tag{13}\]  
\[X_{2}=2A^{T}Y-A^{T}AA^{T}Y=\left(2\mathbb{I}-A^{T}A\right)A^{T}Y, \tag{14}\]  
\[X_{3}=3A^{T}Y-3A^{T}AA^{T}Y+A^{T}AA^{T}AA^{T}Y=\left(3\mathbb{I}-3A^{T}A+A^{T}AA ^{T}A\right)A^{T}Y, \tag{15}\]  
The term in brackets can be rewritten as:  
\[3\mathbb{I}-3A^{T}A+\left(A^{T}A\right)^{2}=\left(\mathbb{I}-\left(\mathbb{I} -A^{T}A\right)^{3}\right)\cdot\left(A^{T}A\right)^{-1}, \tag{16}\]  
Thus, a recurrence rule can be found for this iterative algorithm resulting in:  
\[X_{n}=\left(\mathbb{I}-\left(\mathbb{I}-A^{T}A\right)^{n}\right)\cdot\left(A^ {T}A\right)^{-1}\cdot A^{T}Y, \tag{17}\]  
Considering the SVD of matrix \(A\):  
\[A=U\cdot S\cdot V^{T};A^{T}=V\cdot S^{T}\cdot U^{T}=V\cdot S\cdot U^{T}, \tag{18}\]  
\[U^{T}=U^{-1};V^{T}=V^{-1}, \tag{19}\]  
\[A^{T}A=V\cdot S^{2}\cdot V^{T}, \tag{20}\]  
\[\left(A^{T}A\right)^{n}=V\cdot\left(S^{2}\right)^{n}\cdot V^{T}, \tag{21}\]  
\[s^{-1}=\frac{1}{s}(s\
eq 0)\ ;\ s^{-1}=0\ (s=0), \tag{22}\]  
\[\mathbb{I}=V\cdot V^{T}, \tag{23}\]  
Using Equations (18)-(23), Expression (17) can be rewritten as:  
\[X_{n}=V\cdot(\mathbb{I}-\left(\mathbb{I}-S^{2}\right)^{n})\cdot V^{T}\cdot V \left(S^{2}\right)^{-1}\cdot V^{T}\cdot V\cdot S\cdot U^{T}\cdot Y, \tag{24}\]  
This can be simplified to obtain:  
\[X_{n}=V\cdot(\mathbb{I}-\left(\mathbb{I}-S^{2}\right)^{n})\cdot(S)^{-1}\cdot U ^{T}\cdot Y, \tag{25}\]  
Then, the Landweber algorithm, for niteration can be expressed in a pseudoinverse form as:  
\[X_{n}=A_{n}^{\dagger}\cdot Y=V\cdot S_{n}^{\dagger}\cdot U^{T}\cdot Y, \tag{26}\]  
where:  
\[S_{n}^{\dagger}=\left(\mathbb{I}-\left(\mathbb{I}-S^{2}\right)^{n}\right) \cdot(S)^{-1}, \tag{27}\]  
That matrix is diagonal, and its elements can be expressed as:  
\[s_{n}^{\dagger}=\frac{1-\left(1-s^{2}\right)^{n}}{s}, \tag{28}\]
# Spatial-Temporal Self-Focusing of Partially Coherent Pulsed Beams in Dispersive Medium
## 3 Spatial-Temporal Self-Focusing of PCP Beams in Dispersive Medium
### Air Case  
In the following calculation, we present the evolution behavior of PCP beams in air medium. Because the refractive index \(n_{\text{g}}=1.00028\) of air is much closer to the 1, and the second-order dispersion coefficient \(\beta_{2}=0.021233\) ps\({}^{2}\) km\({}^{-1}\) is very small. Hence, we choose more short pulse duration and temporal coherence length, namely, \(T_{0}=200\) fs and \(T_{\rm c}=100\) fs in order to present the self-focusing more clearly. The other parameters are the same as the case of water.  
Figures 7 and 8 give the contour graph of normalized spatial-temporal intensity distribution \(I(x,t,z)\) of PCP beams at \(z\) plane with different values of \(z\). Figure 7 is the case of rect weight function, while Figure 8 is the case of Gaussian weight function. From Figures 7 and 8, we can see that the similar spatial-temporal self-focusing phenomenon takes place, i.e., the pulses focus first on the temporal dimension, then focus on the spatial dimension with increasing propagation distance \(z\). At some critical distance, the spatial-temporal self-focusing phenomenon takes place (see Figure 7c z = 0.13 km and Figure 8c z = 0.04 km). Compared with rect weight function, for the case of Gaussian weight function, the spatial-temporal self-focusing behavior appears more early and noticeable. Physically, Gaussian weight function compared with rect weight function has better energy focusability.  
Figure 8: Contour graph of normalized spatial-temporal intensity distribution \(I(x,t,z)\) of PCP beams with Gaussian weight function at \(z\) plane with different values of \(z\): (**a**) \(z=0\), (**b**) \(z=0.02\) km, (**c**) \(z=0.04\) km and (**d**) \(z=0.2\) km in the \(x\)-\(t\) plane.  
Figure 7: Contour graph of normalized spatial-temporal intensity distribution \(I(x,t,z)\) of PCP beams with rect weight function at \(z\) plane with different values of \(z\): (**a**) \(z=0\), (**b**) \(z=0.07\) km, (**c**) \(z=0.13\) km and (**d**) \(z=0.3\) km in the \(x\)-\(t\) plane.  
Figure 9 gives the on-axis and off-axis normalized intensity profile of PCP beams in air for the rect weight function (a,b) and Gaussian weight function (c,d), respectively. In general, the similar self-focusing phenomenon can be found for the rect weight function and Gaussian weight function of PCP beams. What is more, the self-focusing effect of PCP beams with Gaussian weight function is more noticeable than the case of rect weight function. Mathematically, there are high degrees of resemblance between rect function and Gaussian function. Physically, Gaussian weight function has better energy focusability than rect weight function.
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 1 Introduction  
Internet of Vehicles (IoV) is a concept in the Internet of Things ecosystem representing intelligent transportation [1]. IoV is also a superset of Vehicular Ad-Hoc Networks (VANETs) and extends its scale and services [2]. VANET originates from Mobile Ad-Hoc Network (MANET) and is considered as a network in which different moving vehicles and other devices are connected with each other and exchange useful information to provide roadside services such as road safety, navigation and traffic control.  
These exchanging messages include sensitive information such as vehicle identification, personal data (e.g., driver's identity and current location) and navigation information which participants may be unwilling to share [1]. In addition, the number of autonomous vehicles (self-driving cars) is anticipated to increase rapidly in the near future, so VANET will have to manage more information and user data. Moreover, attackers and malicious users may attempt to gain vehicle users' personal information in order to cause serious problems to the road network and drivers' safety. Therefore, it is highly important to ensure the privacy of participants and the security of the exchanged information and to prevent malicious attacks [1].  
Furthermore, a key issue is to assure that the shared information will not be changed or leaked by third parties. Blockchain is a technology that can provide data protection due to the use of an immutable distributed ledger and the adoption of several cryptographic techniques, such as hash functions, Merkle trees and public-key cryptography, in a decentralised environment [1]. Thus, blockchain is used to deal with the problems mentioned above.  
Although blockchain can provide a tamper-proof environment, it cannot ensure any aspects of privacy, so it needs to be combined with other technologies to achieve privacy preservation.  
This paper examines proposed solutions for different sectors of IoV, which use blockchain technology, while also attempting to protect the privacy of involved parties. The protection concerns users' identity, vehicles' location and exchange data between vehicles and infrastructures.  
The rest of this paper is organised as follows: Section 2 contains a brief introduction to the main topics discussed in this paper, i.e., IoV, blockchain and privacy in IoV. In Section 3, an overview of related works on blockchain-enabled privacy preserving solutions is provided. Section 4 describes the research methodology that has been followed in conducting this review and provides the research questions that this work addresses. Section 5 presents an analysis of this study's results and summarises key findings while, in Section 6, existing privacy-preserving blockchain-based solutions for IoV are presented in detail, grouped under seven major categories based on the services they provide. Finally, in Section 7, we provide a discussion on future research directions and open issues, while Section 8 concludes this review paper.
# Reallocating Charging Loads of Electric Vehicles in Distribution Networks
## 4 Configurations of the System under Study  
### The Network under Study  
Figure 2 illustrates the proposed architecture of the centralized controller. An adapted UK generic distribution network (UKGDN) [30] was used to test the performance of the centralized control algorithm for the smart charging of EVs.  
Six radial feeders are emanating from the medium voltage (MV) side of the two parallel on-line tap changing transformers. The capacity of each transformer is 15 MVA 33/11 kV. The MV feeders serve 18,432 customers with 3,072 customers for each feeder. Each MV feeder is divided into 8 segments, serving 8 ground-mounted distribution transformer. Each one (_i.e._, 500 kVA 11 kV/0.4 kV  
Figure 2: The proposed architecture of the centralized controller.  
ground-mounted distribution transformer) serves 384 customers distributed across 4 LV feeders. Ninety-six customers were distributed along each LV feeder (See Figure 2). More details about the original UKGDN can be found in the literature [30].  
Metering points were allocated at the emanating point of each LV feeder. These meters are used to upload readings of measured phase currents using two-way communications (see Figure 2). When these readings are received by the centralized control algorithm, unbalanced power flow calculations are performed to reallocate EV charging loads. The unbalanced power flow updates voltage magnitudes, voltage unbalances and electric loads of network components. If EV users upload their unscheduled EV charging loads via these two-way communications, EV users can accordingly reschedule their smart charging based on the charging duration received from the control algorithm. EV charging loads will be accordingly reallocated using the centralized control algorithm as illustrated in Section 2.2.  
To solve the power flow for the UKGDN (Figure 2) using the developed solver, the following steps are followed:  
* Calculation of the total power at node 1 using Equations (13) and (14).
* Calculation of the total current at node 1 using Equation (11).
* Calculation of the phase voltages at node 2 using Equation (12).
* Sequentially repeat the above calculations to determine the phase voltages at nodes 2, 4, 6, 8, 10, 12, 14, and 16.
## 3 Monte Carlo Simulation
### Structure of the Simulation  
The Monte Carlo simulation comprises 1000 iterations, after the results are saved. As shown in Monte Carlo simulations of [3; 6; 10], 1000 iterations are enough to maintain stable results. The simulation starts with sampling random input variables. Number of PVs in the network is sampled from the lognormal distribution, that is presented below. The location of PVs is sampled with uniform distribution. Every PV plant has a bus associated with it and only one PV plant can occupy a bus. One-week measured voltage data is sampled from the 8 weeks of measured data. Every measured week has the same probability to be chosen for current iteration. Finally, deviations of magnitude and angle of each phase voltage are randomly sampled to provide higher variety of background VU, as described in Section 2.2. In the next step, VU is calculated for all the strategies. Input parameters, that were sampled in previous step, are kept the same for all the strategies. Voltage unbalance is calculated for each 10-minute time step of one week. Then, a 95 th percentile value of each bus is calculated. The highest VU value is then saved for result analysis. During the third step of the Monte Carlo simulation, the optimization routine is initiated. Since the PV injection value fluctuates along a week, the most conservative minimum value is chosen at first to represent PV injection. However, to take into account the tolerance condition allowed by the standard, 5 th percentile value of PV injection is found at each bus and then saved for further analysis. The flowchart of the simulation can be seen in Figure 4.