esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Quality for Near-Eye Display Based on Holographic Waveguides
## 5 Conclusions  
Developing the AR display is complying with the best tradeoff between optical and ergonomic parameters: optical efficiency and field of view, eye box, brightness uniformity within the field, manufacturing costs, size, weight, etc. The work is about holographic waveguides that were fabricated directly on a holographic optical table by recording an interference pattern in a photoresist layer. In our case, the compromise was an acceptable reduction of the diagonal angular field of view to 35-40 degrees while maintaining other parameters of AR image quality (resolution, uneven brightness, color distortion, etc.), which are responsible for the visual perception of the user.  
Our study focused on how different DOE configurations of holographic waveguides affect image quality: brightness uniformity, noise, chromatism, and aberrations. The recommended optimal optical architecture is based on the analog gratings consisting of five areas. Input DOE, two relay DOEs, and output DOE3 are recorded separately and overlapped with an accuracy of \(\pm\)0.2 mm. The input DOE consists of two symmetric (with a vertical axis of symmetry) halves. Each of the halves has its own oblique orientation of the gratings at the angle of plus and minus 60\({}^{\circ}\).  
In this implementation, the holographic waveguide consists of three separate substrates for the red, green, and blue channels with the photoresist layer turned toward the incident light of the projector. The periods of the diffraction gratings are 357, 421, and 516 nm, which correspond to the central wavelengths for the RGB channels. The recommended relief height of diffraction gratings with a quasi-sinusoidal profile corresponds with the 100-150 nm range. Within the framework of the research, we managed to achieve a level of brightness non-uniformity in the field of about 18%.  
**Author Contributions:** Conceptualization, A.S. and D.L.; methodology, M.S.; software, E.Z.; validation, A.S., D.L., M.S., and O.A.; formal analysis, A.S.; investigation, D.L.; resources, M.S.; data curation, D.L.; writing--original draft preparation, M.S.; writing--review and editing, A.S.; visualization, O.A.; supervision, D.L.; project administration, A.S.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research and the APC were funded by the Priority 2030 program at the Bauman Moscow State Technical University.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Authors thank [PERSON] and [PERSON] for their great support with experiments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network
## 3 Proposed SEFR Protocol  
The capabilities of both the WSN and fog computing are combined to facilitate the on-demand and real-time network operations such as information gathering, storing, and efficient and reliable processing. The restricted resources on the part of sensor nodes impose a lot of research difficulties while designing an optimal solution. This work presents a lightweight secure and fog-based energy-efficient routing protocol to decrease network delay and energy consumption of the network field. Also, it increases the data security between cluster heads to fog nodes and fog nodes to cloud servers against potential threats. The architectural design of the proposed work is depicted in Figure 1.
# A New Spatial Distance Metric for Verification of Precipitation
###### Abstract
For example, some rainy regions that overlap perfectly in both fields could have significantly different precipitation (i.e., two times or more precipitation) while the resulting score value would be perfect. The thresholding also causes the regions with light rain (with precipitation below the threshold) to be ignored--these regions will not influence the score value. Using a single high threshold will cause the light rain regions to be ignored. Using a single low threshold will potentially cause the regions with light rain to be spatially compared with rain intensive regions, regardless of the actual difference in precipitation intensity. Selecting a relatively high threshold also increases the likelihood of the binary fields being zero everywhere. This is a problem since none of the above-mentioned distance metrics can provide results, if any of the two input fields contains only zeroes. One potential solution is to analyze the results by using multiple thresholds. Nevertheless, in this case, the results will still depend on the choice of threshold values, and it is also difficult to compare the results of multiple studies that use different sets of thresholds. Here we present a new measure for global spatial displacement of precipitation that does not require thresholding (although it can still be used if one wishes to). It can thus be used to analyze binary and non-binary fields (e.g., continuous or multi-level fields). Since the input fields can be non-binary, the spatial distribution of values inside the events also influences the resulting estimate of displacement. The aim was to develop a measure that would provide a good and meaningful approximation of the displacement in situations with a single displaced event inside the domain. For these situations, it is relatively easy to subjectively determine a suitable estimate for spatial displacement (this is not always possible in situations with multiple events). At the same time, the measure should not provide results that are highly inconsistent with a subjective evaluation for situations with multiple events. For example, the displacement estimate being zero for situations when the fields are quite different or highly sensitive to small changes in the input fields. In Section 2, the new method is presented, along with a short description of other distance metrics used in the analysis. In Section 3, the behavior of the new metric is analyzed in various idealized and real-world situations. Finally, Section 4 provides the conclusions and discussion.
## 3 Analysis and Results
### Analysis of Cell-Based Spatial Separation  
The analysis of speed homogeneity zones was divided into two types: a macromoesoscopic regional analysis based on uniform cells along the highway route and a meso-microscopic analysis segmented by specific sections of the route. This approach allows for both a broader overview of speed patterns across large areas and a more detailed examination of speed variability within individual segments of the highway.
# Non-Thermal Methods for Ensuring the Microbiological Quality and Safety of Seafood
## 7 Conclusions  
Consumer demand for safe, high-quality seafood with added value has made the non-thermal emerging technologies a vital tool for the seafood industry to ensure food safety and quality. Historically, ice has been used for the preservation of perishable seafood from ancient times. However, nowadays, it is not a practical solution, because seafood is shipped worldwide and needs to retain its microbiological safety without adverse effects on the sensorial and nutritional values. On the other hand, thermal technologies can successfully reduce the microbial pathogens of interest, but high-temperature treatments can affect the quality parameters at a non-acceptable level for the consumer or the industry demands.  
Non-thermal technologies such as HHP, US, PEF, NTAP, and EW reviewed in the current paper have shown to be capable of inactivating the most common foodborne pathogens found on fish and seafood products maintaining the organoleptic and sensory characteristics. There is an emphasis on applying HHP and PEF to pre-packed and liquid seafood products within the food industry to retain their high quality on appearance and sensory attributes. At the same time, most US, NTAP, and EW studies have focused on the effects of fish processing, showing minor quality degradation compared with conventional thermal treatments. Several advantages accompany all these: reduced energy consumption, reduced product/waste products, reduced costs, improved product quality, and shorter production times, resulting in increased production. The synergistic effects among non-thermal and other technologies offer new potentials in the seafood industry, aiming to enhance the quality of seafood throughout the application of hurdle technology, while preserving the important foods' naturalness. Besides, their application has some limitations, as not all these methods are in the same development stage. The seafood industry must consider these issues following the consumers' willingness to buy natural products with excellent sensory experience. Therefore, further research is necessary to improve the promising non-thermal emerging technologies to the level that will be applied universally as safe by the seafood industry and produce seafood products accepted as natural by consumers.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Ionospheric Electron Density Model by Electron Density Grid Deep Neural Network (EDG-DNN)
## 2 Materials and Methods
### COSMIC_DNN Model Introduction
When the input is \(x\), _RELU_ can be expressed as:  
\[\sigma(x)=RELU(x)= \begin{cases}0,x\leq 0\\ x,x>0\end{cases} \]  
_RELU_ has a fast computation speed and its convergence speed is much faster than sigmoid. It can solve the gradient disappearance problem. The optimisation algorithm used in this study was Adam (adaptive moment estimation). Adam can efficiently solve the problem of local deep learning, and the effect is better than other random algorithms [29]. The electron density dataset used in this study spans the years 2006 to 2013, and the electron density profiles for each year are combined into a single file. The data from each year are separated into training, validation, and test sets in the ratio of 8:1:1. The training and validation sets are combined and trained for a total of 500 epochs, and the batch size is set to 5120, using the loss function MAE (mean absolute error loss) utilized in the training. ReduceLROnPlateau from Keras is used in the study to automatically adjust the learning rate, with an initial learning rate of 0.1 and MAE as monitor; when MAE has 20 epochs without change, the learning rate is adjusted to 1/2 of the original. Figure 3 depicts the composition structure and basic algorithm of the neural network. Figure 4 shows the loss variation curves of the training and validation sets. Figure 5 depicts the entire model process from input to output. Figure 3: Composition and basic algorithm of DNN neural network. The data in the test set will be used to test the prediction effect of EDG-DNN, analyze EDG-DNN by comparing the effect with IRI-2016, and demonstrate the effect of the model response to the magnetic storm period by comparing the change in ionospheric electron density before and after the magnetic storm.
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 3 Offloading Probability Analysis
### Offloading Probability in BS Mode  
In BS mode, the user can only be associated with the BS while guaranteeing the successful content transmission via the cellular link, that is, the requested content cannot be served from any nearby device transmitter. The offloading probability in BS mode can be obtained as  
\[\begin{array}{ll}P_{3}=\sum_{k=1}^{K}\ p_{k}\mathbb{P}_{b,n}P(SIR_{k}^{b} \geq\xi)\end{array} \tag{11}\]  
where \(\mathbb{P}_{b,n}\) is the coverage probability of the BS for a requesting user, for which there is no available neighboring D2D transmitter to handle the request as follows:  
\[\begin{array}{ll}\mathbb{P}_{b,n}&=\sum_{n=0}^{\infty}\big{(}1-(1-q_{k})^{n }\big{)}\text{Pr}(N_{k}=n)\ \prod_{\begin{subarray}{c}i=1\\ i\in\Phi_{u,k}^{\prime}\end{subarray}}^{n}\ (1-\Omega_{i})\\ &=e^{-\pi\lambda_{u,k}^{\prime}R^{2}}\sum_{n=0}^{\infty}\frac{(\lambda_{u,k}^{ \prime}\pi R^{2})^{n}}{n!}\big{(}1-(1-q_{k})^{n}\big{)}\big{(}1-(1-e^{-\lambda _{i}\tau})\big{)}^{n}\\ &=e^{-\pi\lambda_{u,k}^{\prime}R^{2}(1-e^{-\lambda_{i}\tau})}\Big{(}1-e^{-\pi q _{k}\lambda_{u,k}^{\prime}R^{2}e^{-\lambda_{i}\tau}}\Big{)}\end{array} \tag{12}\]  
The probability of successful content transmission for a user served by BS \(i_{0}\) is given by \[P(SIR_{k}^{b}\geq\xi)=P\left[\frac{P_{b}h_{b}r^{-\alpha}}{\sum\limits_{i\in\Phi_{b} \backslash\{i_{0}\}}P_{b}h_{i}r_{i}^{-\alpha}+\sum\limits_{i\in\{\Phi_{k,k}^{b }\backslash\Phi_{u}^{b}\}}P_{u}h_{i}r_{i}^{-\alpha}}\geq\xi\right] \tag{13}\]  
Similar to (6), we have  
\[P(SIR_{k}^{b}\geq\xi)=\int\limits_{0}^{\infty}\mathcal{L}_{I_{b}}\ (r^{\alpha}\xi P_{b}^{-1}) \mathcal{L}_{I_{u}}(r^{\alpha}\xi P_{b}^{-1})f_{r_{b}}(r)dr. \tag{14}\]  
Similarly, the Laplace transforms \(\mathcal{L}_{I_{b}}(r^{\alpha}\xi P_{b}^{-1})\) and \(\mathcal{L}_{I_{u}}(r^{\alpha}\xi P_{b}^{-1})\) are given by  
\[\mathcal{L}_{I_{b}}\ (r^{\alpha}\xi P_{b}^{-1})=e^{-\pi\lambda_{b}r^{2}Z_{1}( \xi)} \tag{15}\]  
\[\mathcal{L}_{I_{u}}\ (r^{\alpha}\xi P_{b}^{-1})=e^{-\pi\ v\ \lambda_{u}\ (\frac{P_{b}}{P_{b}})^{\frac{ 2}{2}}\ r^{2}\ [(\Omega\ Z_{1}(\xi)-Z_{2}(\
u_{0}))\ q_{k}+Z_{2}(\
u_{0})]} \tag{16}\]  
The PDF of the distance \(r_{b}\) between the user and a serving BS is \(f_{r_{b}}(r)=2\pi\lambda_{b}re^{-\lambda_{b}\pi r^{2}}\)[33].  
By substituting (15) and (16) into (14), we can obtain  
\[P(SIR_{k}^{b}\geq\xi)=\frac{\lambda_{b}}{C_{3}\ q_{k}+C_{4}}\, \tag{17}\]  
where \(C_{3}=v\lambda_{u}(\frac{P_{b}}{P_{b}})^{\frac{2}{2}}(\Omega Z_{1}(\xi)-Z_{2} (\hat{r_{0}}))\) and \(C_{4}=\lambda_{b}(Z_{1}(\xi)+1)+v\ \lambda_{u}(\frac{P_{b}}{P_{b}})^{\frac{2}{ \alpha}}Z_{2}(\hat{r_{0}})\).  
By using (11), (12) and (17), the offloading probability in the BS mode is given by  
\[P_{3}=\sum\limits_{k=1}^{K}\ \frac{p_{k}\lambda_{b}}{(C_{3}q_{k}+C_{4})}e^{-\pi \lambda_{u}^{\prime}\kappa^{\prime}R^{2}\Omega}\Big{(}1-e^{-\pi q_{k}\lambda_ {u}^{\prime}R^{2}e^{-\lambda_{u}\tau}}\Big{)}. \tag{18}\]
## 4 Experiments and Results
### Data Pre-Processing
#### 4.1.2 Dataset Partitioning  
In deep learning, datasets are typically partitioned into the training set, the validation set, and the test set to meet the needs of model training, validation, and testing. The training set is a collection of data used to train the model. By learning from the training set, the model can learn the features and patterns of the data, thus improving the accuracy of predictions. The training set includes input data and corresponding labels, and the model continuously updates its weights to improve prediction accuracy. The training set is the foundation of deep learning model training and has a crucial impact on the model's performance, therefore the training set occupies the largest amount of data. The validation set is a dataset used to evaluate the model's performance. The validation set can help us detect the degree of overfitting of the model and adjust the model's hyperparameters in a timely manner. At the same time, it can also help us choose the best model parameters, such as epoch, learning rate, and batch size. The test set is a dataset used to evaluate the final performance of the model. After the training and validation process was completed, we usually use the test set to evaluate the final performance of the model. In this study, the rock dataset was also randomly divided into the training set, the validation set, and the  
Figure 12: Data augmentation. (**a**) Data augmentation methods; (**b**) Data augmentation result.  
test set with a ratio of 60%, 20%, and 20%, respectively. Therefore, the number of samples for them is 4169, 1389, and 1389, respectively.
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 2 Methodology
### Water Quality Index (WQI)  
One of the most effective tools in evaluating the quality of waters is represented by the Water Quality Index (WQI), showing the risk posed by the pollutants present in the water constituents matrix [16]. This method provides an overview of the quality status characterizing various water resources. The WQI is computed in four steps: (a) the assignment of the weights (\(w_{i}\)) for each physico-chemical parameter based on its importance for the quality of the surface water (Table 2); (b) the calculation of the relative weight (\(W_{i}\)) using Equation (4); (c) establishing the quality rating (\(q_{i}\)) according to Equation (5); (d) the calculation of the subindex for each physico-chemical parameter (\(SI_{i}\)) and aggregation into the WQI. Equations (6) and (7) are Appl. in the last step of the method [16].  
\[W_{i}=\frac{w_{i}}{\sum_{i=1}^{n}w_{i}} \tag{4}\]  
\[q_{i}=\frac{w_{i}}{S_{i}}\times 100 \tag{5}\]  
\[SI_{i}=W_{i}\times q_{i} \tag{6}\]  
\[\text{WQI}=\sum_{i=1}^{n}SI_{i} \tag{7}\]  
where, \(w_{i}\) is the weight of each parameter, \(W_{i}\) is the relative weight, \(q_{i}\) represents the quality rating for each physico-chemical parameter, \(C_{i}\) and \(S_{i}\) represent the concentration and the guideline value according to the drinking water quality guidelines established by the Minister Order 161/2006/OD and the Directive 2008/32/CE [34; 35], \(SI_{i}\) represents the subindex of the \(i\)th parameter.  
The WQI scoRes. classify the studied waters into five different quality categories: (a) excellent--suitable for drinking, irrigation and industrial usages, WQI = 0-25; (b) good--suitable for domestic, irrigation and industrial usages, WQI = 26-50; (c) poor--suitable for irrigation and industrial usages, WQI = 51-75; (d) very poor--suitable for irrigation usage, WQI = 76-100 and (e) unsuitable for drinking purposes and restricted for irrigation, WQI > 100 [40; 41].
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 4 Experimental Study
### Optimization Model  
When considering the avoidance of the extreme situations in production, which mean too few processed products and, on the other hand, the system overload, the space of design variables for optimization was determined as a four-dimensional domain defined by time between arrivals for the entry of each of four products in a reasonable range of 8 min to 16 min, based on the simulation results for this system. In this case, the modelled system showed acceptable values of production goals for all three priority rules.  
Based on observed production results, the suitable values of constraints of production goals for optimization model were also suggested. As for the machine utilization constraints, from the practical point of view referring to [35], the decision was influenced by the fact that the machine utilization more than 80% generally corresponds to moderate and heavy shop load level in job shops. The constraints concerning production objectives applied in all optimization experiments are specified in Table 4.  
The optimization objectives represented five selected aforementioned key performance indicators: Average flowtime, average machine utilization, average work in progress, average number of products, and the average costs per part unit. For MOP solving, they were combined into two types of scalar multi-criteria objective functions. This scalarization procedure generating multi-criteria objective function based on WSM and WPM methods (Equations (3) and (4)) is described in detail in the following paragraphs.
## 7 Long-Range Underground Monitoring
### Technology Description
#### 7.2.2 Weightless  
Weightless is an open LPWAN protocol which was created by the Weightless Special Interest Group (SIG) (now Weightless Alliance) which was formed by companies such as Neul, Landis+Gyr, Cable & Wireless and ARM [10; 100]. Originally, Weightless consisted of three technologies: Weightless-N, -W and -P. Weightless-W was designed to operate in TV whitespace bands and Weightless-N was an uplink-only technology. Weightless-P was a true-bidirectional narrowband technology operating in licensed and unlicensed ISM frequencies. Weightless-P prevailed in the market, and today Weightless simply refers to that technology [100]. The technology offers uplink rates between 0.625 and 100 kbps, uplink data rates between 6.25 and 100 kbps and used time and frequency division multiple access (TDMA and FDMA) and operates on licensed and unlicensed sub-GHz bands (e.g., 470, 510, 840, 868, 902-928 MHz, depending on the operation region) [10; 100].  
Weightless networks consist of base stations, end devices and backend network management services [101; 102]. The network is operated as a private network. The network has a star topology with the base station(s) as central node(s) [103].  
The technology is used for smart-metering electric vehicle charging or forecasting solar power generation. A Taiwan Power Company uses the technology to collect 27 million Meter Messages from over 250,000 m daily. The meters are typically in dense urban and underground (basement) environments [102]. In the context of underground mining there is, to the best knowledge of the authors, no publication or known use case. However, the successful application of the technology in smart metering applications (dense urban environment and basement installations) could suggest that the technology is also suitable for some use-cases in the underground mining sector.
# Predicting Activities of Daily Living with Spatio-Temporal Information
## 4 Description of the Data and Evaluation Method  
This study was run on three publicly available smart-home datasets:  
* MIT PlaceLab Dataset [17] This dataset contains 16 days of user activities with 1805 user-interaction instances. The activities are grooming/dressing, doing/putting away laundry, toileting/showering, cleaning, preparing meal/beverages and washing/putting away dishes. These activities are performed in 4 different locations of the home i.e., kitchen, bathroom, bedroom and office/study.
* van Kasteren Dataset [18] This dataset contains 24 days of user activities with 1318 user-interaction instances. The activities are toileting/showering, going to bed, preparing meal/beverages and leaving house. These activities are performed in 4 different locations of the home i.e., living room, bedroom, bathroom and kitchen.
* Aruba Dataset [19] For this dataset, the November 2010 data is used, which contains 27 days of user activities with 3569 user-interaction instances. The activities are meal preparation, eating, working, sleeping, washing dishes and bed to toilet. These activities are performed in 7 different locations of the home i.e., kitchen, dining, living room, bedroom-1, bedroom-2, bathroom and office.  
In all the datasets, the users annotated the activities themselves meaning that there were ground-truth annotations. It was observed that some activities were not repeated daily such as 'doing/putting away laundry', 'washing dishes' and 'cleaning' on MIT PlaceLab dataset. Since it is important that these activities are seen in the test set, leave-two-days-out cross-validation method is used, i.e., training on 14 days data and testing on the remaining 2 days. As for van Kasteren dataset,  
Figure 2: Representation of Table 1 in ASCII characters and processed based on 30 min time intervals.  
since there is a small number of activity examples per activity, leave-four-days-out cross-validation method is used, i.e., training on 20 days data and testing on the remaining 4 days. Since the activities in Aruba dataset are repeated frequently, leave-nine-days-out cross-validation method is used, i.e., training on 18 days data and testing on the remaining 9 days. Figure 3 shows the cross-validation method applied in this study.