esa-sceva/satcom-chunk-collection huggingface dataset:
# Connected Vehicles: Technology Review, State of the Art, Challenges and Opportunities
## 4 Challenges
### Privacy and Security
Further to these efforts, a consortium named the _Mobility Open Blockchain Initiative_ (MOBI [[https://dlt.mobi/](https://dlt.mobi/)]([https://dlt.mobi/](https://dlt.mobi/)) (accessed on 11 November 2021)) was formed by GM, BMW, Renault, Ford, IBM, Hyperledger, IOTA, and Consensus(Ethereum) to focus on promoting standards and accelerating the adoption of blockchain, distributed ledger, and related technologies in the vehicle domain. The prominent need for a private and secure CV system will drive the research community to enhance, develop, and innovate low overhead and lightweight security and privacy algorithms against different cyber-physical attacks that are considered life threatening for vehicular environments. The aforementioned studies have addressed different security and privacy aspects to encourage automotive companies to incorporate a _\"security by design\"_ paradigm at the early stages of vehicle manufacturing to mitigate different security and privacy attacks. Table 3 summarizes the challenges, potential research, and state-of-the-art technologies to handle these challenges.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 1 Introduction
Third, a model-based framework is introduced to specify the candidate data sources for a given geographical area. Fourth, with the help of the framework, the effect of various alternatives of data fusion can be estimated. Last, to synthesize the optimal set of data sources within specified constraints, algorithms are defined. To the best of our knowledge, such a framework has not been proposed by the research community before. This article is organized as follows. The following Section 2 introduces the background and related work. Section 3 presents the problem statement, the research questions, and the method adopted. Section 4 describes the model base for data sources, presents the domain model, and gives examples of a selected set of data sources. The architecture of the framework and the associated object-oriented models are explained in Section 5. Section 6 defines the objectives of data fusion and presents formulas to calculate a selected set of quality values of the user-defined data fusion models. Two synthesis algorithms are presented in Section 7. Section 8 describes how the proposed framework can be extended to support UAS-based data fusion systems. Section 9 discusses the threats of validity of the assumptions made in this article. Section 10 presents our research plans. Finally, Section 11 gives the results and conclusions. The Appendices A and B present the estimated characteristics of the data sources which are referred to in this article.
## 2 Materials and Methods
### VPM Model and Simulation
#### 2.5.2 VPM Simulations with the Climate Data from the EC Flux Tower Sites  
We used the climate data from the EC flux tower sites to run the VPM simulations (GPP\({}_{\text{VPM-Site}}\)). The half-hourly air temperature and PAR data were aggregated to daily and 8-day datasets for the model simulation. The resultant GPP\({}_{\text{VPM-Site}}\) data were compared with the GPP\({}_{\text{EC}}\) data.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### Disaster Response and Recovery
#### 4.3.1 Simulation of Natural Disasters
The paper does not provide a detailed discussion on the challenges associated with data sharing, integration, and updating mechanisms between the platform and provincial-level seismic departments. The scalability and adaptability of the platform to other cities or regions with different earthquake risk profiles and data availability are not thoroughly explored.
For instance, [PERSON] et al. introduced an enhanced CBAM-1 DCNN model for rolling bearing fault diagnosis, which achieved optimal fitting with fewer training iterations and exhibited superior fault identification accuracy and generalizability [21]. [PERSON] et al. designed a model that combines a one-dimensional Convolutional Neural Network with a self-attention mechanism for detecting natural gas pipeline leakage faults [22], significantly outperforming traditional methods by emphasizing critical fault information. Moreover, [PERSON] et al. presented a deep learning model that leverages a temporal convolutional network and an attention mechanism for real-time motor fault diagnosis, which outperformed both 1D-CNN and Temporal Convolutional Networks (TCN) in multi-motion dataset experiments [23]. While deep learning has advanced fault diagnosis significantly, key challenges persist. Firstly, datasets such as the CWRU [24] provide data for only two types of bearings, restricting the breadth of research. There is a notable gap in studies on mixed faults in multiple bearing types, and it is still being determined if models can retain their accuracy in such complex scenarios. Secondly, traditional CNNs and RNNs often need help with long-sequence data dependencies. Thirdly, most models need more holistic optimization despite improvements in accuracy, speed, and noise immunity. In response to these challenges, this paper introduces several advancements:  
(1) Utilization of [PERSON] et al. 's practical bearing datasets from HUST [25], demonstrating our model's high accuracy in diagnosing hybrid faults across five distinct bearing types. (2) A pioneering fault diagnosis model that synergizes ACN, BiGRU, and a multi-head self-attention mechanism to manage long-distance dependencies in sequence data effectively. (3) Our model exhibits rapid convergence, superior accuracy, and noise immunity performance compared to recent state-of-the-art models. These contributions signify a substantial leap forward in deep learning applications for fault diagnosis, showcasing the potential for more reliable and efficient predictive maintenance solutions. The remainder of this paper is structured as follows. Section 2 provides a detailed introduction to Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRU), and the self-attention mechanism. Section 3 presents our experimental methodology. Section 4 is devoted to an introduction of the two datasets used in the study and a detailed explanation of the experimental protocol and setup, as well as a presentation of our model's experimental results. Section 5 is devoted to our discussion of the experiments. Section 6 concludes the paper with a summary of the experiments.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 4 Conclusions  
Based on the conventional vibration frequency method for cable force determination, a lightweight measurement method using non-fixed smartphone was developed in this paper and verified through real-life bridge experiments. This study contributes to the body of literature in the following three aspects:  
1. Based on the smartphone terminal, a standardized procedure for on-site cable vibration video collection is proposed, including target design and layout, parameter settings of data acquisition and video recording method. The standardization of the procedure can facilitate multi-project and multi-person data collection in practice.
2. The procedure of video data processing and analysis is developed, including target recognition, target tracking and displacement correction. The vibration displacement of stay cables can be automatically extracted based on video data. The algorithm is verified to be stable and efficient.
3. By comparing with the sensor-based cable force measurement, the maximum error of the proposed method based on the smartphone terminal is 1.99%, which is smaller than conventional methods. The proposed method is proven to generate reliable and accurate cable force estimations.
4. The proposed method and the developed software allow simultaneous tests on multiple projects and multiple cable surfaces by multiple personnel. By scanning the QR code for cable location and information retrieval, the users can upload videos of cable vibration and perform cable force measurement with efficiency and accuracy. For future work, the algorithms can be improved and application on smartphones can be developed to allow real-time cable force analysis.  
The proposed method based on smartphone imagery is advantageous in practice since there is no need to carry special equipment or to fix the camera. The measurement method in the field will be simple, with reliable accuracy and low cost, thus boosting great promotion value in engineering applications.  
Conceptualization, Y.W., K.L. and Y.C.; methodology, Y.W., K.L., Y.C. and S.X.; software, K.L. and Y.C.; validation, Y.W., K.L. and Y.C.; formal analysis, Y.W.; investigation, Y.W., K.L. and Y.C.; resources, Y.W. and W.S.; data curation, Y.W., K.L. and Y.C.; writing--original draft preparation, Y.W. and S.X.; writing--review and editing, S.X.; visualization, S.X. and W.S.; supervision, Y.W. and W.S.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (grant number 2017 YFC0805304).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Nyquist Analysis for Ideal FPR Controller on Voltage Applications
### Stability Analysis Based on a Conceptual Nyquist Trajectory
#### 2.2.4 Crosspoints with Real Axis  
The possible crossing points with the real axis are required to obtain the stability from the Nyquist criterion (detect number of encirclements of (\(-1\),0I)). For integer \(\alpha\) values, it is possible to obtain exact crossing points.  
* For \(\alpha=0\), the crossing point with the real axis takes place at (0,0I) when the \(\omega\) is \(\pm\frac{\sqrt{k_{p}\omega_{0}(k_{p}\omega_{0}+k_{j})}}{k_{p}}\).
* For \(\alpha=1\), the crossing point takes place at \((\frac{r_{C}^{2}C^{2}k_{p}\omega_{0}^{2}+r_{C}Ck_{i}\omega_{0}+k_{p}}{r_{C}C^ {2}\omega_{0}^{2}},0I)\) when the \(\omega\) is \(\pm\frac{\sqrt{(r_{C}Ck_{i}\omega_{0}+k_{p})k_{p}\omega_{0}}}{r_{C}Ck_{i} \omega_{0}+k_{p}}\).
* For \(\alpha=2\), the crossing point takes place at (0,0I) when the \(\omega\) is \(\pm\frac{\sqrt{(\omega_{0}k_{p}+k_{p})k_{p}\omega_{0}}}{\omega_{0}k_{p}+k_{p}}\).  
The other crossing points for \(\alpha\in(0,2)\setminus\{1\}\) values are not analytically possible due to the roots from (8) as a function of the \(\omega\) requires the resolution of a transcendental equation. Thus, the exact values of the crossing points for all the other intermediate values of \(\alpha\) require the setting of the parameters \((k_{p},k_{i},r_{C},C\) and \(\omega_{0})\). It has been proposed to analyse numerically possible crossing points for \(\omega_{0}\) equal to \(100\pi\) rad/s (50 Hz), \(k_{p}\in[0,100]\), \(k_{i}\in[0,10,000]\), \(r_{C}\in[0,100]\) m\(\Omega\), \(C\in[0,1]\) mF and \(\alpha\in(0,2)\setminus\{1\}\). The frequency of 50 Hz is selected as it corresponds to the typical AC rated frequency for Europe, Asia, Africa, and Australia. The same qualitative results arise if 60 Hz is used instead. All previous intervals are split into 100 points except for the \(\alpha\), which is split into 10 values. Then, \(10^{10}\) different cases were numerically evaluated, concluding that for intermediate \(\alpha\), only one crossing point is present for \(\omega\in(0,\infty)\).
## 4 Experimental Results and Analysis
### Impact of the RAAN Precession on the LEO-LEO Occultation Events
#### 4.2.2 Impacts of the Height on the Near-Polar Orbit Constellation Occultation Events
Figure 8: The variation in LEO-LEO occultation event numbers with the Rx and Tx orbital element of height for the Rxi98-Txi98 and Rxi82-Txi98 datasets on event accumulations over 3 months. Figure 10: The accumulated time dependence of the LEO-LEO occultation event number (orange lines) and the global coverage fraction (GCF) (blue lines) for the representative Rxi98-Txi82 constellations composed of a Rx satellite and a Tx satellite with particular orbital heights. Figure 9: The global coverage of LEO-LEO occultation events for representative Rxi98-Txi82 constellations composed of a Rx satellite and a Tx satellite with particular orbital heights, illustrating the occultation numbers per \(5^{\circ}\times 5^{\circ}\) latitude and longitude grid cell. The GCFs were calculated using grid cell areas according to Equation (12).
# Wind Power Forecasting with Deep Learning Networks: Time-Series Forecasting +
## 1 Introduction  
With the increasingly serious global warming crisis and the burning of fossil fuels inducing air pollution and climate change, concerned parties have begun to invest in the development and application of renewable energy. European countries such as Denmark, Germany, and Sweden have invested in renewable energy through smart power grids, in which power suppliers and regional suppliers provide two-way complementary power supply and demand. The key technology of a smart power grid is power forecasting in relation to renewable energy, which is a clean power supply. Many techniques have been applied to wind power forecasting to solve various problems, such as the fluctuations in power from wind farms for very short-term, short-term (from 30 min to day-ahead), medium-term (from day-ahead to month-ahead), and long-term (more than month-ahead) [1]. Wind power forecasting prediction models can be classified using the following three approaches: (1) the physical approach, in which weather changes are considered as deterministic events [1], (2) the statistical approach, in which weather changes are considered as a random process [2,3], and (3) the hybrid approach, which constitutes a weighted aggregation of the other two prediction models [4, 5, 6, 7, 8, 9]. Compared with these three methods for wind power prediction problems, deep learning network (DLN) approaches, such as Boltzmann machines (RBM), long short-term memory (LSTM), temporal convolutional networks (TCN), and convolutional neural networks (CNN) have exhibited superior results and are generally considered as an alternative solution for wind power prediction [10,11]. These wind power forecasting schemes are summarised as Table 1.  
These developed models can perform long-term day-ahead wind power forecasting; however, forecasting schemes with a mean absolute percentage error (MAPE) between 12% and 17% [12,13] do not meet the engineering requirements. Thus, the development of an accurate and robust approach for wind power forecasting under varying climate conditions is still a challenge. Considering the increasing role of wind power in the renewable energy system, the research gaps and opportunities for wind power predicting are summarised as:  
1. Practically, most existing approaches to forecasting do not model the uncertainty of wind well. Thus, a high-accuracy wind power model needs high resolution weather data inputs generated by an NWP model, which is not a trivial task. 2. Typically, deep learning-based neural networks for day ahead wind power forecasting outperform traditional neural networks such as ANN in renewable power
###### Abstract  
Combining Organic Rankine Cycles (ORC) with cooling cycles offers a promising approach to achieving greater outputs within a single system. In this study, a novel directly combined ORC-VCC system has been designed to not only meet the cooling demand using a geothermal heat source but also generate power. The proposed novel ORC-VCC system has been analyzed for its energetic performance using four selected fluids: R290, R600a, R601, and R1234 ze(E). Parametric analysis has been conducted to investigate the effects of parameters of heat source temperature, heat source mass flow rate, cooling capacities, condenser temperature, ORC evaporator temperature, pinch point temperature difference and isentropic efficiencies on net power production. Among the working fluids, R290 has provided the highest net power production under all conditions in which it was available to operate. Additionally, the results have been analyzed concerning a reference cycle for comparative evaluation. The proposed novel cycle has outperformed the reference cycle in all investigated cases in terms of net power production such as demonstrating an improvement of approximately from 8.7% to 57.8% in geothermal heat source temperature investigations. Similar improvements have been observed over the reference cycle at lower heat source mass flow rates, where net power increases by up to 50.8%.  
Organic Rankine Cycle (ORC); refrigeration; combined; power; geothermal Article
## 3 Finite Element Modeling  
The finite element method (FEM) is one of the powerful tools for modeling a structure with very complicated geometry and materials. There are many strategies, as shown in Figure 10, to model a masonry structure with FEM, which includes macro- and micro-modeling. The macro-model is based on the assumption of homogenous materials, and the mortar joints and units can be smeared into one isotropic or anisotropic material. This procedure may be preferred for the analysis of large masonry structures due to the reduced time and memory requirements as well as a user-friendly mesh generation. In addition, this type of modeling is most valuable when a compromise between accuracy and efficiency is needed [23; 24].  
More accurate response of a masonry assembly can be deducted by using micro-modeling techniques. In this method, the units and the mortar and unit/mortar interface are represented by continuum and discontinuous elements, respectively. Different properties for both units and mortar were separately applied, and this leads to a realistic response of a masonry assembly with more accurate prediction of its local behavior. Despite these advantages, this approach is uneconomic and inefficient in terms of computational time. To overcome this problem, simplified micro-modeling has been used and, according to pervious research, it can give accurate results with a simple modeling procedure. In this type of modeling, the mortar joints are clamped into the unit/mortar interface as a discontinuous element. Expanded units--up to half of the mortar thickness in horizontal and vertical directions--were simulated to continuum elements. This procedure can successfully predict the shear behavior and crack pattern [25; 26; 27].  
Figure 10: Modeling strategies for block masonry. (**a**) Typical masonry sample; (**b**) Detailed micro modeling; (**c**) Simplified micro modeling; (**d**) Macro modeling.  
Figure 9: The resulting shear-stress–normal-stress graph.
# Chemical Analysis of Gunpowder and Gunshot Residues
## 2 Main Components of Gunpowder and Gunshot Residue
### Inorganic Compounds  
The inorganic compounds found in gunshot residue (IGSR) derive mainly from the primer, which traditionally contains antimony sulphide, barium nitrate, lead styphnate, lead dioxide, calcium, silicon, and tin [16; 18; 25; 36]. Due to their high molecular masses and low abundance in nature, antimony (Sb), barium (Ba), and lead (Pb) have been the main target of IGSR analysis [20; 25; 30; 31; 37; 38; 39]. In fact, the ASTM 1588-17 standard [40], the Scientific Working Group on Gun Shot Residue (SWGGSR) guidelines [36], and the European Network of Forensic Science Institutes (ENFSI) recommendations [41] focus on the analysis of these elements through scanning electron microscopy with energy-dispersive X-ray (SEM-EDX). This is a non-destructive technique that provides robust morphological and chemical information about the sample particles [32; 36; 39; 40; 41; 42]. Other methods used for the analysis of IGSR particles, not included in these recommendations, involve atomic absorption spectroscopy (AAS), atomic force microscopy (AFM), proton-induced X-ray emission (PIXE), and neutron activation analysis (NAA). The former provides quantitative and elemental information [13; 15; 42], whereas AFM allows a very high-resolution picture of the GSR particles at nanoscale dimensions [43]. On the other hand, PIXE is a technique that employs a beam of high-energy particles for elemental analysis [44], and NAA is a highly sensitive approach that focuses on the nucleus of the atoms present in the elements of the sample with quantitative capabilities [16; 45]. Moreover, mass spectrometry with inductively coupled plasma (ICP-MS) also proved valuable for IGSR particles analysis [14; 16].  
Chemical analysis of GSR particles mainly targets inorganic elements, particularly heavy metals, as their co-occurrence is characteristic of GSR. However, with the increase in environmental awareness [46; 47], and with the USA [48] and EU [49] pushing to ban the use of lead-containing ammunition, new primer formulations without heavy elements are emerging [39]. In these formulations, heavy elements such as Sb, Ba, and Pb, are now being replaced by copper, zinc, titanium, strontium, iron, nickel, zirconium, steel and aluminium, or other more environmentally friendly organic compounds, such as tetracene, pentaerythnitol tetranite (PETN), trinitrotoluene, tetryl, dextrin, diazodinitrophenol, and diazontrophenol [15; 16; 18; 30; 47].  
Consequently, the IGSR compounds on newer ammunition are less characteristic of gunshot residue since they are naturally present in the environment, and therefore, the probative value of IGSR particles has diminished considerably [12; 30; 31; 50]. To overcome this difficulty, some researchers suggested shifting the target of GSR analysis to organic compounds or to combined analysis of organic and inorganic compounds, which recent studies suggest have higher evidential value [6; 16].
# Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning
## 5 Results  
The study carried on so far aimed to compare different methods in the data-set composition employed for the training of the ANN, highlighting the most effective ones. The obtained results of the day-ahead forecasts were analysed by the indexes shown in Section 3 and led to the following results. The graph in Figure 15 shows the trend of the \(\overline{NMAE}_{\%}\) calculated for the methods in the training-set composition, according to increasing data-set sizes. The best training method, which globally performed better with all the data-sets considered, was undoubtedly C1. Instead, in the short-range training, with only 10 days available in the data-set, method C2 scored the worst result with \(\overline{NMAE}_{\%}\) equal to 6.079. In accordance with the increasing data-sets method, C2 aligned with C1 above 90-130 days. The same trends of the other evaluation indexes are equally shown in Figures 16-18 and confirm the same results. From this perspective, method C2 scored the worst result, with \(\overline{EMAE}_{\%}\) equal to 36.51. According to the \(\overline{NMAE}_{\%}\) shown in Figure 15, methods B1 and B2 generally performed pretty much the same.  
Figure 16: \(\overline{nRMSE}_{\%}\) as a function of the dataset size.  
Figure 17: \(\overline{WMAE}_{\%}\) as a function of the dataset size.  
As a general comment on the reported results, it can be stated that method A is best suited when the availability of historical data is limited (e.g., newly deployed PV plant), while method C1 appears to be most effective in the case of a greater availability of data (e.g., at least one year of power measurements from the considered PV facility). Generally speaking, ensembles composed of independent trials are most effective. The performance of methods B1 and B2 was halfway between A and C, and their effectiveness in the case of newly deployed PV plants became significant after a minimum period of measurement data accumulation (above 60 days).