esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applying the Methodology to the Example of Dabar Power Tunnel
### Instabilities around the Contour Leading to Overbreaks
#### 3.2.1 Deterministic Analysis
The previously described deterministic method based on key block theory was applied to determine the unstable wedges around the contour of the unsupported excavation. Only unstable wedges along the length of the unsupported section, which corresponded to 3 m on average, were considered. Figure 5: Procedure for shear testing along discontinuities: (**a**) shear device used for testing; (**b**) engineering–geological inspection and evaluation of joint samples; (**c**) placing of samples in mould prior to cementing; and (**d**) cemented samples prepared for testing [28]. Deterministic stability analyses were conducted on dozens of arbitrarily selected cross-sections with the aim of establishing a general pattern. The typical results of wedge stability analysis at two selected tunnel sections together with the measured contours of the over-profiles are presented in Figure 7. It can be observed that the contours of the unstable wedges and the measured contours of the over-profile excavation are in good agreement. Evidently, the agreement in this respect cannot be exactly due to the limitations of the approximations associated with numerical analysis and the simplification of geological conditions, which are more complex than can be objectively ascertained by descriptive mapping.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 3 Methodology
### Modeling 5G Network Parameters
\[PL_{1}= 20\log_{10}(40\pi d_{3D}f_{c}3)+\min(0.03h^{1.72},10)\log_{10}(d_{3D}) \tag{7}\] \[-\min(0.044\text{ h}^{1.72},14.77)+0.002\log_{10}(h)d_{3D}\]  
\[PL_{2}=PL_{1}(d_{BP})+40\log_{10}(d_{3D}/d_{BP}) \tag{8}\]  
Calculation of path loss for RMa on NLOS  
\[PL_{RMA-NLOS}=\max(PL_{Rma-LOS},PL^{\prime}_{RMA-NLOS})\quad\text{for 10 m}\leq d_{2D}\leq 5\,\text{km} \tag{9}\]  
where  
\[PL^{\prime}_{RMA-NLOS} =161.04-7.1\log_{10}(W)+7.5\log_{10}(h)-(24.37-3.7(h/h_{BS})^{2}) \tag{10}\] \[\log_{10}(h_{BS})+(43.32-3.1\log_{10}(h_{BS}))(\log_{10}(d_{3D}-3 )+20\] \[\log_{10}(f_{c})-(3.2(\log_{10}(11.75h_{UT}))^{2}-4.97)\]  
where \(d^{\prime}_{BP}\) denotes the break-point distance; \(f_{c}\) is the center frequency in GHz; \(h_{BS}\) and \(h_{UIT}\) are the antenna heights and user height, respectively; \(d_{2D}\) is the 2D distance between the UE and RRU in meters; and \(d_{3D}\) is the 3D distance in meters calculated as  
\[d_{3D}=\sqrt{d_{2D}^{2}+(h_{RRU}-h_{UE})^{2}}\]  
Note: Channel modeling involves two equations, PL\({}_{1}\) and PL\({}_{2}\), which vary based on the distance between the transmitter and receiver. There exists a critical distance known as the break point distance. Equation (1) is used for distances within this limit, while a different equation is applied for distances beyond it, reflecting changes in channel characteristics. The break point distance and subsequent loss characteristics are defined according to specifications outlined in the 3 GPP document for each model type. More details can be found in [52], while RRU specifications details are presented in Table 2. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**RRU Specifications** & & & \\ \hline
**Specifications** & **Midband** & **Midband** & **mmWave** \\ \hline Frequency Range & 2.6 GHz & 3.6 GHz & 28 GHz \\ Band & n7 & n78 & n257 \\ Duplex Mode & FDD & TDD & TDD \\ Channel Bandwidth & 5, 10, 15,...50 & 10, 15,...100 & 20, 100, 200, 400 \\ Selected Bandwidth & 20 MHz & 40 MHz & 100 Mhz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RRU Specifications [38].
###### Abstract  
Accurate intent detection-based chatbots are usually trained on larger datasets that are not available for some languages. Seeking the most accurate models, three English benchmark datasets that were human-translated into four morphologically complex languages (i.e., Estonian, Latvian, Lithuanian, Russian) were used. Two types of word embeddings (fastText and BERT), three types of deep neural network (DNN) classifiers (convolutional neural network (CNN); long short-term memory method (LSTM), and bidirectional LSTM (BiLSTM)), different DNN architectures (shallower and deeper), and various DNN hyperparameter values were investigated. DNN architecture and hyperparameter values were optimized automatically using the Bayesian method and random search. On three datasets of 2/5/8 intents for English, Estonian, Latvian, Lithuanian, and Russian languages, accuracies of 0.991/0.890/0.712, 0.972/0.890/0.644, 1.000/0.890/0.644, 0.981/0.872/0.712, and 0.972/0.881/0.661 were achieved, respectively. The BERT multilingual vectorization with the CNN classifier was proven to be a good choice for all datasets for all languages. Moreover, in the majority of models, the same set of optimal hyperparameter values was determined. The results obtained in this research were also compared with the previously reported values (where hyperparameter values of DNN models were selected by an expert). This comparison revealed that automatically optimized models are competitive or even more accurate when created with larger training datasets.  
intent detection; fastText and BERT embeddings; CNN; LSTM; BiLSTM classifiers; hyperparameter optimization; English; Estonian; Latvian; Lithuanian; Russian +
Footnote †: journal: Applied Informatics, Vytautas Magnus University, Vlieikos Str. 8, LT-44404 Kaunas, Lithuania
## 2 Materials and Methods
### Alum Sludge Pre-Treatment and Alum Sludge/PBAT Composite Preparation  
Figure 1 illustrates the complete process for the pre-treatment of alum sludge and the preparation of composite samples using PBAT. The alum sludge was collected from the water treatment plant in the form of a dewatered cake (Figure 1a). It was firstly oven-dried at 110 \({}^{\circ}\)C for 24 h to remove excess moisture and improve processability. The oven-dried sludge, which formed small crumbs (Figure 1b), was then subjected to ball milling in roller bottles containing zirconia balls (Orto Alresa Roller Mill), operating at 10 rpm for four hours. The milled material was then sieved using a shaker stack to obtain distinct size fractions: particles smaller than 63 \(\upmu\)m (denoted as 63 \(\upmu\)m particles; Figure 1c) and particles smaller than 250 \(\upmu\)m (denoted as 250 \(\upmu\)m particles; Figure 1d). The images highlight the differences in particle size distributions between these two alum sludge fractions. Additional SEM observations, presented in the Supplementary document, reveal that the sludge particles exhibit irregular shapes, heterogeneous surfaces, and a porous texture. These morphological characteristics are consistent with findings reported elsewhere [26]. The two sets of sludge samples were stored in airtight containers to maintain their properties for subsequent composite processing.  
PBAT pellets were dried in an oven at 110 \({}^{\circ}\)C for four hours to remove any absorbed moisture (Figure 1e). Approximately 200g of PBAT polymer was used to prepare both neat and composite samples. A single-screw melt extruder (Davis Standard BC-32) was operated at an outlet temperature of 150 \({}^{\circ}\)C and a screw speed of 8 rpm to process the material, producing filaments (Figure 1f) using various formulations. The extruded filaments were pelletised using a Rieter Primo 50 pelletiser. The resulting pellets (Figure 1g) were then compression-moulded at 160 \({}^{\circ}\)C to form 1 mm thick sheets, which were subsequently shaped into dogbone specimens for mechanical testing (Figure 1h).  
For the composite sample preparation, alum sludge particles (63 \(\upmu\)m and 250 \(\upmu\)m particle fractions) were incorporated into the PBAT matrix at a filler concentration of 5 wt.%. These composite samples underwent mechanical testing and soil burial testing to evaluate the influence of particle size distribution on composite performance. Based on the results, additional composite samples were prepared using the 63 \(\upmu\)m fraction sludge, with filler concentrations of 2 wt.%, 10 wt.%, and 20 wt.%, to further investigate the effect of filler concentration on composite properties.
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 2 Methodology  
### Pearson Correlation Coefficient Method  
Accurate power consumption prediction for an SAG mill is crucial for the efficient operation of the beneficiation plants and the rational allocation of electricity. Selecting appropriate input parameters is key to achieving reliable SAG mill power consumption prediction. However, in practical applications, the acquired data may be highly dimensional. In order to solve this problem, dimensionality reduction techniques are usually used for variable screening.  
The PCC method is frequently employed to evaluate the linear correlation between two normally distributed continuous variables, with the correlation degree ranging from \(-1\) to \(1\). If PCC values are greater than \(0\), it signifies a positive correlation between the two variables. If the PCC value is less than \(0\), it signifies a negative correlation between the two variables. A PCC value of \(0\) means there is no correlation between the two variables. The PCC value between variables X and Y signifies the strength of the linear correlation between these two variables. The PCC calculation formula is as follows:  
\[\text{PCC}_{xy}=\frac{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})(y_{i}- \overline{y})}}{\sqrt{\sum\limits_{i=1}^{n}{(x_{i}-\overline{x})^{2}}}\sqrt{ \sum\limits_{i=1}^{n}{(y_{i}-\overline{y})^{2}}}} \tag{1}\]  
where \(\overline{x}\) and \(\overline{y}\) represent the mean values of variables X and Y, respectively. Table 1 provides the classification criteria for correlation coefficients, as per the rules in reference [20].
## 4 Conclusions  
Environmental impact related to waste milling was significant when compared to waste drying, sand, gravel, and limestone indicators, considering the evaluation of 1 kg of material. However, when the evaluation focused on the material incorporated for the manufacture of one slab, the difference was not significant.  
Additionally, with a 20% cement replacement, in the manufacture of all slabs, glass powder (GP2-20) could decrease the environmental impact related to repairs and maintenance by 50%, considering the maintenance horizon of 100 years. Furthermore, the fabrication phase presented the most significant environmental impact. Finally, considering total impact analyzed by the IMPACT 2002+ methodology, a significant difference between the relevance of the maintenance and the fabrication phases was observed.  
The scenario considering a 20% cement substitution for waste glass powder in the concrete fabrication decreased 1.23 kg CO\({}_{2}\) eq. and 0.008 GJ energy use for each kilogram of waste incorporated in the material. Another important finding showed that glass waste could reduce 972 tons of clinker in concrete production, decreasing 2000 tons of cement manufacture of the type CEM III/ A 42.5, equivalent to the Brazilian CPIII.  
This research also showed that using GP2-20 can ensure that the compressive strength is maintained, chloride resistance is increased, and a reduced environmental impact is identified. Additionally, the lesser need for repairs can lead to economic benefits, when considering the long-term analysis of the project.  
Finally, this paper identified that the gain in durability in concrete due to the glass powder incorporation provided a significant reduction in the environmental impact. The technical proposal to prevent suicide also presents an important social value to the community, offering a solution to frequent suicide attempts on the bridge, and access to ambulances and police vehicles, among others.  
Conceptualization, G.G., J.L.C., G.V. and R.Z.; methodology, G.G., J.L.C. and R.Z.; software, G.G.; validation, J.L.C., and G.V.; formal analysis, G.G., J.L.C. and T.A.R.; investigation, G.G., J.L.C., G.V. and R.Z.; data curation, G.G., J.L.C., G.V. and R.Z.; writing--original draft preparation, G.G.; writing--review and editing, J.L.C. and T.A.R.; visualization, G.G.; supervision, J.L.C. and G.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# A Time Integration Method Based on Galerkin Weak Form for Nonlinear Structural Dynamics
## 6 Conclusions  
Taking the nonlinear dynamic Equation (1) as the model problem, a step-by-step time integration method is proposed based on the Galerkin finite element approximation and Newton-Raphson iteration. In summary, the following four conclusions of this method should be noted:  
1. For the proposed method based on the Galerkin FEM, usually time elements of degree \(m=1,2,3\) are used in practical computation. Numerical examples show that such elements can gain a convergence order of \(2m\) for both displacement and velocity results.
2. When a reduced Gauss integral method is applied in computation, the present method is unconditional stability.
3. For linear elements, a feasible time integration scheme with controllable numerical damping has been constructed by modifying the test function and introducing a special integral rule.
4. This is a self-starting and one-step method, which can effectively solve both linear and nonlinear structural dynamic problems. Representative numerical examples have shown its reliability and efficiency, and according to comparisons of results with the trapezoidal rule, [PERSON] \(\theta\)-method, and Bathe method, the present method performs well in stability with controllable numerical dissipation, and its computational efficiency turns out to be superior as well.  
The proposed time integration method is of comprehensive capabilities in nonlinear dynamics. Furthermore, since it originates from FEM, self-adaptive FE techniques can be directly introduced into it, and then an adaptive integration algorithm can be established with the size of time step being automatically produced, leading to more efficient solutions of nonlinear dynamic problems.  
[PERSON] directed this study, conceived the main idea of the method and finalized the manuscript. [PERSON] constructed the algorithm, derived equations, performed calculations, and prepared the draft of the manuscript. [PERSON] proofread the equations and data.  
This work was supported by the National Natural Science Foundation of China (No. 51508305). The authors are solely responsible for the content.  
The authors declare no conflict of interest.
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 2 Materials and Methods
### Methods
#### 2.2.4 Evaluation of Color Uniformity  
Color uniformity refers to the ability of a display to consistently render color tones across different screen areas. To evaluate color uniformity, monochromatic test patterns in red (255, 0, 0), green (0, 255, 0), and blue (0, 0, 255) were employed [8]. During assessment, these patterns were displayed on HoloLens 2, and their color coordinates (\(u^{\prime}\), \(v^{\prime}\)) were measured at various locations using a color spectrometer (MAVOSPEC BASE). Measurements were taken at five positions: top left, top right, center, bottom left, and bottom right [9].  
For these measurements, the color spectrometer was centrally positioned and vertically aligned with the HoloLens 2 screen, and a two-axis linear stage facilitated movement among the five measurement points. The test setup is illustrated in Figure 7.  
The color coordinates for each measurement point were compared in pairs. The deviations between the color coordinates (\(u^{\prime}\), \(v^{\prime}\)) were calculated using Equation (3),  
\[\Delta u^{\prime}v^{\prime}=\sqrt{\left({u^{\prime}}_{1}-{u}{\
u}_{2}\right)^{ 2}+\left({\
u}_{1}-{\
u}_{2}\right)^{2}} \tag{3}\]  
where \(u^{\prime}\), \(v^{\prime}\) and \(u^{\prime}\), \(v^{\prime}\) represent the coordinates of two measurement points and \(\Delta u^{\prime}v^{\prime}\) indicates the distance between them.  
For primary display devices, the maximum distance between color coordinates must not exceed 0.01. No quantitative criteria are defined for secondary displays [4].  
Figure 6: Comparison of luminance responses of a display device (measured values) and the GSDF [5].
## 3 Dynamic Object Identification and Tracking
### Tracking Experiments  
The experiments conducted in real-world conditions involved three or four participants and included overlapping events. Figure 4, Test 1: Participants moved freely. Figure 5 a,b, Test 2: Three participants walked in circles, while a fourth participant entered the scene, passing through the others and overlapping the radar's field of view. Figure 5 c,d, Test 3: Three participants moved side to side with an inclined motion.  
Table 2 provides a summary of the Multiple Object Tracking Accuracy (MOTA) scores for Tests 1-3, as described in the previous section. The experiments were recorded for approximately 30 s and the cumulative MOTA scores were calculated at 10 s intervals. On average, the system achieved a MOTA of 82.69% at the experiments' end, demonstrating reliable tracking performance under varying conditions. These results indicate that the system effectively recognizes and tracks individuals and provides reliable input data for the neural network, enabling further analysis and enhancing its potential for real-world applications.
## 5 Experiments
### Results
For the communication heavy Sobel application, the amount of (Processing Elements) PEs does not have a huge impact to the overall execution time when using the TL or ML model. All three mappings result in an execution time of approximate 7 min for 1,000,000 iterations. The cycle accurate (CA) model is slower than measuring the mappings with multiple tiles due to the excessive simulation overhead during the polling phase of the communication. The execution time of the JPEG application highly depends on the amount of PEs used to execute them. Measuring single PE mapping takes more than 13 h for 1,000,000 iterations. Executing some actors in parallel and making use of different architecture features like the FPU decreased the execution time to about 5 h. All MicroBlazes were operated with a clock speed of 100 MHz. The simulation time increases with the amount of communication load. Simulating the execution of the JPEG decoder takes up to 1 h 20 min for a mapping using 7 tiles using the TL communication model. The same experiment using the more abstract ML model takes 25 min. The CA model requires multiple days for multi-processor mappings. In our experiments, the simulation with the TL communication model is up to 15 times faster than the execution of the real application on the actual hardware platform. The simulation using the ML model is up to 41 times faster.
## 2 Related Works
### Blood Bank Systems
RFID devices and sensors are among the most advanced technologies developed in recent years, having a significant impact on the physical communication layer of the Internet of Things (IoT), as well as on logistics and robotics. It has taken a significant amount of research to integrate them with various technologies, such as freight systems,and to improve the efficiency of supply chains [38; 39]. An L-resonator-based, chipless RFID tag was used in ref. [10] for detection and tracking in a blood bank management system and identifying blood bags and also as a blood group identifier smart label for donors and patients. However, for essential tasks of either tracking or tracing, RFID alone cannot meet the financial and practical requirements of building an extensive and costly infrastructure of readers [38]. Another technique is a Raspberry Pi has been used with an Android app in [40] to build a system and provide the result on a real-time basis. Additionally, in ref. [41], an automated blood bank system based on an Android app and Raspberry Pi was suggested for getting blood donors together in one location. A message is delivered to a specific blood donor via a GSM modem. A proposal was made in ref. [42] to notify blood bank officials and donors about the need for blood donation using IoT technologies. Ultrasonic sensors are used for distance measuring connected to blood bank refrigerators, allowing a mobile application to determine the condition of any blood type. An LCD monitor in each refrigerator displays the current amount of each blood group via built-in sensors and is integrated with the IoT system. Despite this, there have been continuous losses of acquirable blood from individuals or in inventory because most existing blood bank systems are manual and cannot download the latest update of the donation data and are severely susceptible to human mistakes. Additionally, previous studies have not provided satisfactory solutions to the problem of irregular electric current in developing countries and the risks resulting from temperature deviations from the recommended range. So, providing safe and reliable blood requires a system based on a desktop app, Web app, or mobile app to work with recent technology to manage blood bank processes, avoiding staff errors successfully.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
###### Abstract  
Digital Twins serve as virtual counterparts, replicating the characteristics and functionalities of tangible objects, processes, or systems within the digital space, leveraging their capability to simulate and forecast real-world behavior. They have found valuable applications in smart farming, facilitating a comprehensive virtual replica of a farm that encompasses vital aspects such as crop cultivation, soil composition, and prevailing weather conditions. By amalgamating data from diverse sources, including soil, plants condition, environmental sensor networks, meteorological predictions, and high-resolution UAV and Satellite imagery, farmers gain access to dynamic and up-to-date visualization of their agricultural domains empowering them to make well-informed and timely choices concerning critical aspects like efficient irrigation plans, optimal fertilization methods, and effective pest management strategies, enhancing overall farm productivity and sustainability. This research paper aims to present a comprehensive overview of the contemporary state of research on digital twins in smart farming, including crop modelling, precision agriculture, and associated technologies, while exploring their potential applications and their impact on agricultural practices, addressing the challenges and limitations such as data privacy concerns, the need for high-quality data for accurate simulations and predictions, and the complexity of integrating multiple data sources. Lastly, the paper explores the prospects of digital twins in agriculture, highlighting potential avenues for future research and advancement in this domain.  
digital twins; smart agriculture-farming; agriculture 4.0; digital twin model; precision 11 August 2023