esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### High-Order Tensor Model for ETC Gantry Data
#### 3.1.2 ETC Gantry Tensor Block and Subtensor Block Models  
This study introduces gantry tensor block models to encapsulate the traffic statistics of a gantry over one-minute intervals. These models facilitate streamlined calculations through a methodically organized sequence. In alignment with high-order tensor models, these blocks adopt time, space, and vehicle user dimensions as their fundamental structure. Each time dimension within a tensor block corresponds to 1 min, comprising 60 distinct time components. The spatial dimension is bifurcated into 2 key factors: gantry number and gantry malfunction. The vehicle user dimension mirrors that of the high-order tensor model, ensuring consistency. Crucially, the process of dimensionality reduction applied to ETC gantry data retains the intrinsic properties of the original high-dimensional dataset within a more manageable, low-dimensional framework. For practical implementation, attributes such as ETC gantry number, capture time, and license plate identification were meticulously selected to construct a subtensor block model, as follows:  
\[\begin{array}{c}\mathcal{X}_{LPR}\in R\ ^{I_{time}\times I_{space}\times I_{ car}}\\ I_{time}=\left[t_{pictme}\right]\\ I_{space}=\left[s_{id}\right]\\ I_{car}=\left[c_{license}\right]\end{array} \tag{1}\]  
Herein, \(\mathcal{X}_{LPR}\) represents the subtensor block of the ETC gantry plate recognition data, encompassing the capture time, \(I_{time}=\left[t_{pictme}\right]\); gantry number,\(I_{space}=\left[s_{id}\right]\); and identified license plate, \(I_{car}=\left[c_{license}\right]\).  
Similarly, the ETC gantry transaction data are synthesized into a subtensor block model, effectively enabling the assessment of transactional patterns:  
\[\begin{array}{c}\mathcal{X}_{trade}\in R\ ^{I_{time}\times I_{space}\times I_{ car}}\\ I_{time}=\left[t_{tradetime}\right]\\ I_{space}=\left[s_{id}\right]\\ I_{car}=\left[c_{license},c_{type},c_{match}\right]\end{array} \tag{2}\]  
Herein, \(\mathcal{X}_{trade}\) represents the subtensor block of the ETC gantry transaction data, encompassing the capture time, \(I_{time}=\left[t_{tradetime}\right]\); gantry number, \(I_{space}=\left[s_{id}\right]\); and vehicle license plate recognition information, vehicle type, and transaction outcome, \(I_{car}=\left[c_{license},c_{type},c_{match}\right]\).
## Nomenclature  
\begin{tabular}{l l} \(\text{A}_{\text{at}}\) (m\({}^{2}\)) & Cross-sectional area of the steam path \\ \(c_{p,f1}\) (J/kg-K) & Fluid-specific heat at constant pressure \\ \(c_{p,C02}\) (J/kg-K) & CO\({}_{2}\)-specific heat at constant pressure \\ D (m) & Heat transfer characteristic length of the cold-fluid stream \\ \(h_{fg}\) (J/kg) & Latent heat of condensation \\ \(h_{c,f1}\) (J/m\({}^{2}\)-s-K) & Convective heat transfer coefficient \\ \(h_{st,in}\) (J/kg) & Specific enthalpy of the steam at the turbine inlet \\ \(h_{st,out}\) (J/kg) & Specific enthalpy of the steam at the turbine exit \\ L (m) & Length \\ \(\hat{m}^{\prime}_{fl}\) (kg/m\({}^{2}\)-s) & Fluid mass flow rate per unit area \\ \(\hat{m}^{\prime\prime}_{v}\) (kg/m\({}^{3}\)-s) & Condensation rate \\ \(\hat{m}_{st}\) (kg/s) & Steam mass flow rate \\ \(\hat{m}_{CO2}\) (kg/s) & CO\({}_{2}\) mass flow rate \\ Nu & Nusselt number, N\({}_{\text{u}}\) \\ P (m) & Perimeter of the cold-fluid flow path \\ \(P_{slot}\) (Pa) & Storage container pressure \\ \(P_{fl}\) (Pa) & Fluid pressure in the condenser \\ \(\hat{Q}_{slot}\) (J/s) & Isothermal heat released rate during steam condensation \\ \(\hat{Q}_{CO2}\) (J/s) & Rate of heat absorbed by CO\({}_{2}\) stream \\ \(T_{CO2,in}\) (K) & CO\({}_{2}\) temperature at the condenser inlet \\ \(T_{CO2,out}\) (K) & CO\({}_{2}\) temperature at the condenser outlet \\ \(T_{st}\) (K) & Steam temperature \\ \(T_{fl,in}\) (K) & Cold-fluid temperature at the condenser inlet \\ \(\mu_{JT}\) (K/Pa) & Joule-Thomson coefficient \\ \(T_{fl}\) (K) & Fluid temperature in the condenser \\ \(T_{stor}\) (K) & Fluid temperature in the storage container \\ \(\rho_{fl}\) (kg/m\({}^{3}\)) & Fluid density \\ x (m) & x-direction \\ \(v_{x}\) (m/s) & Steam velocity in x-direction \\ \(\hat{W}_{t}\) (J/s) & Turbine work \\ \(\lambda_{\text{n}}\) (J/m-s-K) & The fluid thermal conductivity \\ \(\xi\) & Dimensionsless distance \\ \(\rho_{u}\) (kg/m\({}^{3}\)) & Density of liquid water \\ \(\rho_{v}\) (kg/m\({}^{3}\)) & Density of the vapor \\ \(\phi\) & Volume fraction of the vapor \\ \(\phi_{in}\) & Vapor fraction of the steam at the condenser inlet \\ \end{tabular}
## 3 Experiment  
### Study Area  
Istanbul was selected as the location for predicting traffic incidents in our study due to the high frequency of accidents and traffic incidents in the metropolis, which can lead to significant traffic flow disruptions in an already heavily congested city. During the data collection phase, the coordinates of the accidents and incidents were recorded and assigned a 6 th geohash code with the use of ArcGIS software. The study's data entail records of traffic accidents such as vehicle collisions and barrier collisions, as well as incidents like vehicle malfunctions, road fires, and traffic-related altercations. The total number of traffic incidents in Istanbul was recorded within 682 geohash areas, with each geohash area measuring 1.22 \(\times\) 0.61 km\({}^{2}\). Basic statistics of traffic incidents occurring in all defined areas were extracted, and the frequencies of traffic incidents occurring in these areas were obtained. When the 16-month data used in the study were examined, it was seen that there were no frequent accidents in most of the 682 defined areas. Narrowing the study area is predicted to improve the accuracy of predicting accident locations in Istanbul, which is a densely populated metropolis comprising 682 distinct zones that exhibit varying characteristics and accident rates. Due to the high occurrence of traffic incidents such as accidents, vehicle breakdowns, and road deformations in Beylikduzu-Bakirkoy, with a total of 5114 incidents, and the similarity of road conditions across all defined locations, this region was chosen as the study area. The substantial number of accidents/incidents in the region presents an opportunity to estimate the risky region with a large dataset. Furthermore, the adjacent geohash areas within the same region allow for the problem to be evaluated within a homogeneous region in Istanbul. Figure 3 shows the 10 geohash areas selected for the analysis and also the region specifically covered by geohash code 0.  
The number of traffic incidents for 10 geohash areas with similar characteristics selected in this region is shown in Table 3. According to the table, 378 incidents occurred in the area with the fewest traffic incidents, and 654 accidents/incidents occurred in the area with the most traffic incidents.  
The study defined a time interval in four periods, as outlined in Table 4, during which traffic incidents occurred at the identified locations. The highest frequency of incidents took place between 8 a.m. and 8 p.m., attributed to increased commuting for work, school, hospital, and other reasons. Few accidents occurred during sleeping hours, given the reduced traffic on roads.
## 1 Introduction
The pre-process step used the rule-based method to delete the severe corrupted pulses, and the post-process step applied the self-constructing neural fuzzy inference network (SoNFIN) to determine the level of SQI for each PPG pulse. A commercial medical device, medis(r) CS2000, using the ICG technique was used to measure the SV as the reference. Ten healthy adult males participated in this experiment. The beat-to-beat SVs were measured by our device [4] and medis(r) CS2000 about three minutes, simultaneously. Then, we used the error rate of measuring SVs to define the levels of SQI for each PPG pulse. The error rate represents the accuracy of SV measurement. The results showed that the SVs measured by the PPG pulses with the higher SQIs had the lower error than SVs by the PPG pulses with the lower SQIs.
## 6 Conclusions  
NS offers a number of significant advantages in supporting diverse and extreme requirements for latency, throughput, capacity and availability in 5G future communications networks. By slicing a physical network into logically independent virtual networks, the characteristics of applications can be better taken into account by implementing their own resource allocation policies in each slice. In order to satisfy extreme service demands, such as ultra-low latency, high bandwidth and good user experience, how to coordinate the infrastructure network characteristics and QoS with an appropriate policy is the most important research issue when deploying the E2E slices into the backhaul transport network.  
In this paper, we first consider the substrate network characteristics and topology-aware slices (i.e., including VNUs and virtual links) and model the pair-decision resource allocation to construct mapping relationships on account of mapping virtual nodes and links in a coordinated way. To further minimizing the latency, we formulate a latency-optimal VNF resource allocation problem to improve the extreme QoS (such as URLLC) and user experience (such as eMBB). By enclosing the formulation with ILP and solving the problem with the branch-and-bound scheme, an optimal deployment path is found out. Here, latency is characterized by jointly taking into account the network capacity, DCs' locations and link bandwidth. Furthermore, a node importance metric is employed to analyze the DCs' availability and priority in the substrate network. Simulation results show that the proposed scheme can provide lower transport network latency, with a reduction of up to 30% and 41.63% compared to the NSDP and RFPA respectively. The analysis of average traffic distribution, substrate link load and serviceability prove that our proposed scheme achieves a better network load balance and serves a larger scale of concurrent slice requests.  
Conceptualization, W.L., Y.Z. and L.F.; methodology, W.L., Y.Z. and L.F.; software, Y.Z.; validation, Y.Z. and L.F.; formal analysis, Y.Z. and L.F.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., L.F. and F.Z.; visualization, Y.Z.; supervision, W.L. and X.Q.; project administration, W.L.; funding acquisition, W.L., L.F., P.Y and X.Q. This work has been supported by National Science and Technology Major Project (Grant No. 2018 ZX030110004) and State Grid Science and Technology project \"Analysis of Power Wireless Private Network Evolution and 4G/5G Technology Application. The authors declare no conflict of interest.
## 3 Proposed Signal Processing Algorithm  
The proposed signal processing algorithm consists of five steps applied to sampled signal of WLI fringe scans. They are:  
1. Normalization and cross-correlation
2. Peak and zero crossing detection
3. Matched filtering
4. Hypothesis test
5. Fine Tuning  
Each procedure is explained briefly below.
## 4 Evaluation Method and Simulation Requirements  
To illustrate the domain adaptation problem and our proposal, we embedded the proposed AR HUD interface in our fourth generation VR driving simulator. The simulator is designed to be customisable and to accommodate different in-vehicle systems and interfaces that require evaluation in various driving conditions, road networks, and traffic flow scenarios.  
The evaluation process entailed a pre-test questionnaire aiming to gather information regarding driving experience, mobile and computer technologies, as well as driving habits and other demographic information such as age and gender amongst others. In turn, the users were introduced to the driving simulator and they were provided with 10 min approximately to familiarise themselves with the simulation system. During the familiarisation process, the drivers experienced a collision-scenario free traffic flow in sunny/clear weather conditions. After the familiarisation round, each user drove the simulation with and without the HUD interface. The order was selected randomly for every user to minimise trial order bias.  
On the final third stage of the evaluation process, commencing the actual driving simulation, each user was provided with a post-test questionnaire aiming to acquire the driver's subjective feedback regarding the usability of the proposed system. A succinct debriefing session completed the acquisition of the user's feedback. It offered an opportunity for users to provide further suggestions and thoughts related to their experience with the proposed AR HUD interface.  
The drivers' and systems' performance were measured primarily by the number of collisions that occurred per trial, yet the simulator recorded several other human responses and performance variables during each simulation. These measurements were recorded continuously, providing a refined view of the driving patterns and accident propensity that arose throughout every moment in the simulation [2,23].  
The subjective feedback questionnaires were designed to respond to an explicitly designed for the purpose, technology acceptance model (TAM). This customised TAM was intended to identify and quantify the drivers' system experience and their intention to accept and adopt this technology for future vehicles or incorporate retrospectively on the existing car-models. As this paper is presenting the collision results with and without the proposed AR HUD system as well as the individual driver performance, it is not this paper's remit to present the TAM analysis.
## 5 Results and Discussion
### Energy Consumption, Cost of Energy, and Carbon Emission Evaluations
The CO\({}_{2\text{eq}}\) emissions decreased only in the case of CO\({}_{2}\) PRBC implementation in direct electric heating. The increase in CO\({}_{2\text{eq}}\) emissions was primarily attributed to the increasing energy consumption under every control strategy, but also to the relatively low fluctuations of the electricity mix throughout the day, in terms of price and CO\({}_{2\text{eq}}\) intensity. In other studies where cost or CO\({}_{2}\) MPC is applied, a reduction is observed both in energy consumption and in energy costs, and in carbon emissions; in fact, in the majority of cases the percentage reduction exceeds 10% compared to the reference scenario [16; 17; 18; 19; 20; 22]. Figure 13: Monthly CO\({}_{2\text{eq}}\) emissions for heating.
# Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising
## 5 Conclusions  
A weighted group sparse regularization tensor ring decomposition method for hyperspectral image restoration is proposed. The global spatial-spectral correlation model of hyperspectral images is tensor ring decomposition. Using weighted group sparsity to constrain the spectral dimension of spatial difference images is more reasonable than using total variation regularization. The effectiveness of hyperspectral image restoration is demonstrated by the experimental findings of the LRTRDGS method proposed. The implementation of LRTRDGS provides a means to effectively mitigate noise while preserving the intricate textural features of HSI. For further exploration of the spatial domain of HSI, it is recommended to consider representation-based subspace low-rank learning methods that provide more accurate regularization of nonlocal self-similarity. Additionally, it is worth investigating image compression techniques with a low-rank degree.  
Conceptualization, Y.L.; Methodology, S.W., Y.L. and B.Z.; Software, S.W.; Resources, B.Z.; Writing--Review and Editing, S.W. and Z.Z.; Supervision, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (61967004, 11901137, 11961011, 72061007 and 62171147), the Guangxi Key Laboratory of Automatic Detection Technology and Instruments (YQ23105, YQ20113 and YQ20114) and the Guangxi Key Laboratory of Cryptography and Information Security (GCIS201621 and GCIS201927).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare that they have no conflict of interest.
## 5 Analysis of Experimental Results  
### The First Heating Experiment  
In the first heating experiment, the signals collected at 24 \({}^{\circ}\)C and 80 \({}^{\circ}\)C in the pipeline are illustrated in Figure 11. The two signal groups are very similar in shape, with the structural echoes clearly visible. The local details of the signals are presented in Figure 12.  
Figure 11: Comparison of the reflecting signals from the pipeline at room temperature (24 \({}^{\circ}\)C) and local heating (80 \({}^{\circ}\)C). No.1 to No.5 are the reflection signals from the metal hoops, and No.6 is the end reflection.  
Figure 12: Comparison of the local details of reflected signals before and after locally heated pipelines. No.1 to No.5 are the reflection signals from the metal hoops, and No.6 is the end reflection.  
The local phase difference between the high-temperature and low-temperature signals is illustrated in Figure 12. Metal hoops No.1 and No.2 are in LTZ, where the temperature remains nearly constant, resulting in an almost stable phase for UGW signals. Metal hoop No.3, located in TTZ, experiences a slight temperature change, leading to a small phase shift in the UGW signal. In contrast, metal hoops No.4 and No.5, situated in HTZ, exhibit significant temperature variations, causing substantial deviations in the UGW signals. This demonstrates that the temperature has a significant impact on the phase of UGW signals, and as the temperature increases, so does the phase shift. The phase delays for metal hoops No.1 to No.3 are all 0\({}^{\circ}\), while those for metal hoops No.4 to No.6 are 20.0\({}^{\circ}\), 120.0\({}^{\circ}\), and 139.9\({}^{\circ}\), respectively. Although these values differ slightly from the simulation results, the variations are within an acceptable error range when accounting for differences in the lengths of experimental pipes and other practical factors. It confirms the success of the experiment.
# Aptamer-Based Biosensors for the Analytical Determination of Bisphenol A in Foodstuffs
## 4 Detection Techniques Applied in the Analysis of Real Food Samples
### Electrochemical Aptasensors
In the latter case, [PERSON] et al. combined a DNA-hybridization strategy and enzymes to obtain a signal amplification. Aptamer strands were firstly attached on magnetic beads (MBs) using the amide-coupling method and then were hybridized with complementary DNA (message DNA, mDNA). The presence of BPA prompted the release of mDNA strands from the magnetic beads. The dissociated mDNA strands were hybridized with capture DNA (cDNA) immobilized on a gold nano-porous microelectrode (np-Au), and the cleavage reaction was activated with Exo III. cDNA strands were subsequently digested, and the dissociated mDNA was used for a new cycle. The amount of cDNA strands on the microelectrode decreased with the increase in the target concentration. Finally, with the assistance of help DNA (hDNA), the residual cDNA strands on the np-Au surface were recognized with report DNA/gold nanoparticles (rDNA/AuNPs), containing methylene blue, to generate an electrochemical signal for quantitative target detection. The adopted three-electrode system consisted of Au wire as the working electrode, Zn plate as the auxiliary electrode, and Zn wire as the reference electrode. This latter approach is clever, but nonetheless, it is accomplished by the employment of several different materials which, also considering the obtained LOD (Table 2), may significantly increase the costs-benefits ratio in a large-scale aptasensor application.
# Ionospheric Error Models for Satellite-Based Navigation--Paving the Road towards LEO-PNT Solutions
## 6 Conclusions  
In this paper, we presented a detailed discussion of various state-of-the-art ionospheric models. Additionally, we introduced IAMM, a new ionospheric model based on interpolation from averaged data collected over 11 years. Our analysis indicates that the model we have developed exhibits a high level of accuracy in correcting errors caused by the ionosphere, especially in static conditions. The results obtained from our experiments clearly show that the model can effectively identify and rectify these errors, resulting in a significantly improved performance compared with traditional IRI and Klobuchar models in static receivers and a more moderate improvement in some of the tested dynamic scenarios. In addition to improving the positioning accuracy, our IAMM model has an advantage in relying solely on the \(\mathbf{Y}\) matrix as a fixed input, making it a generic model that can work offline. This \(\mathbf{Y}\) matrix can be conveniently stored in a device's memory, making it easily accessible, especially for real-time applications. Another essential feature of the model is that it does not require atmospheric or any other external parameters to be available periodically, making it particularly useful for ionospheric studies on satellites that do not broadcast special parameters for ionospheric mitigation, which is the case for most of the LEO satellites in operation today.  
Conceptualization, M.I. and E.S.L.; methodology, M.I. and E.S.L.; software, M.I., A.G. and X.Z.; validation, M.I.; formal analysis, M.I. and E.S.L.; investigation, M.I., X.Z. and E.S.L.; writing--original draft preparation, M.I.; writing--review and editing, A.G., X.Z. and J.N. and E.S.L.; visualization, M.I.; supervision, J.N. and E.S.L.; project administration, J.N. and E.S.L.; funding acquisition, J.N. and E.S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the INdoor navigation from CUBesAT Technology (INCU-BATE) project under a grant from the Technology Industries of Finland Centennial Foundation and the Jane and Aatos Erkko Foundation (JAES), by the LEDSOL project funded within the LEAP-RE programme by the European Union's Horizon 2020 Research and Innovation Program under Grant Agreement 963530 and the Academy of Finland grant agreement 352364, and by the APPROOS project funded within the Horizon 2020 Marie Sklodowska-Curie program grant agreement 956090.  
**Data Availability Statement:** The data are available at: [[https://zenodo.org/records/10058636](https://zenodo.org/records/10058636)]([https://zenodo.org/records/10058636](https://zenodo.org/records/10058636)), DOI 10.5281/zenodo.10058635 (accessed on 10 December 2023).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 5 Fault Diagnosis and Result Analysis of SOM Neural Network  
The standard component kurtosis values are selected for training. The sample number 1 and sample number 2 of the normal gear data set, the sample number 5 and the sample number 6 of the gear with tooth wear data set, the sample number 9 and the sample number 10 of the gear with tooth crack data set, and the sample number 13 and sample number 14 of the gear with tooth break data set are selected to form a training sample matrix input into the SOM neural network for training. After the training of SOM neural network, the remaining 8 sets of samples were input into the standard SOM neural network for gear fault diagnosis as the unknown gear fault test sample matrix.  
In this paper, \(16\times 4\) arranged neurons are adopted, whose topological structure is hexagonal. The structure of the composed SOM neural network is shown in Figure 15.  
Figure 15: SOM neuron structure.  
The SOM neural network neighbor neuron connections as shown in Figure 16. There are 32 neurons in total.Generally, the sequence number of neurons in the lower left corner of SOM neural network topological structure graph is marked as 1, and the sequence number increases successively from left to right and from bottom to top. The 32 nd neuron is located in the upper right corner of Figure 16.