esa-sceva/satcom-chunk-collection huggingface dataset:
# MDA-Unet: A Multi-Scale Dilated Attention U-Net for Medical Image Segmentation
## 5 Evaluation Results and Discussion
### Ablation Experiments  
In this subsection, we evaluate the design choices of the integrated modules and their influence on the overall performance. First, we show the effectiveness of adding the MSAM and CAM module. Second, we illustrate the significance of the proposed HHDC module integrated into the spatial attention mechanism, as a comparison with the spatial attention  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **DSC** & **JI** & **F-Score** & **Parameters** &
\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} \\ \hline \multirow{2}{*}{U-Net} & Lung & \(94.2\pm 6.2\) & \(92.3\pm 3.5\) & \(96.5\pm 4.3\) & 7,760,069 & **7** \\  & LV & \(93.9\pm 3.8\) & \(91.9\pm 2.5\) & \(95.2\pm 6.1\) & & \\ \hline \multirow{2}{*}{ResDunet} & Lung & \(95.8\pm 5.3\) & \(92.3\pm 3.5\) & \(96.5\pm 4.3\) & 11,850,069 & 10 \\  & LV & \(95.1\pm 0.3\) & \(91.9\pm 2.5\) & \(95.2\pm 6.1\) & & \\ \hline \multirow{2}{*}{Attention-Unet} & Lung & \(96.5\pm 5.4\) & \(95.3\pm 3.6\) & \(97.3\pm 5.1\) & 31,901,542 & 28 \\  & LV & \(95.6\pm 3.4\) & \(94.4\pm 4.2\) & \(96.5\pm 4.2\) & & \\ \hline \multirow{2}{*}{U-Net3+} & Lung & \(97.9\pm 1.5\) & \(95.9\pm 2.6\) & \(98.7\pm 6.2\) & 26,971,000 & 24 \\  & LV & \(96.1\pm 2.5\) & \(93.5\pm 4.3\) & \(97.1\pm 5.1\) & & \\ \hline \multirow{2}{*}{Proposed} & Lung & \(\textbf{98.3\pm 2.4}\) & \(\textbf{97.3\pm 5.4}\) & \(\textbf{98.9\pm 5.3}\) & 7,830,793 & **7** \\  & LV & \(\textbf{96.7\pm 3.5}\) & \(\textbf{94.5\pm 6.1}\) & \(\textbf{97.5\pm 4.3}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical evaluation and trainable parameters for our model in comparison with other segmentation models. Training time is demonstrated in hours.  
module proposed in CBAM [12] and BAM [13]. Third, we study the model performance for different reduction ratios used in the channel attention module. Finally, we compare the performance of different dilation rates used in the HHDC module. For all experiments, we used the lung dataset for training and testing and the DSC measure for assessing the performance.
###### Abstract  
This research seeks to advance the technological process of 3D digitization in built environments and streamline management processes in the construction sector through digital methodologies. To this end, an integration framework is proposed that combines geographic information systems (GISs) and building information modeling (BIM) digital models, specifically for simulating building facilities maintenance management. Although the proposed methodology is applicable across various geographical contexts and building typologies, to ensure clarity in its development, it was applied to a specific case study. For this purpose, a 3D GIS model was created for one of the campuses of the University of Cantabria in Santander, Spain, along with a BIM model for one of its university buildings. Using these integrated models, facility management was simulated within a 3D environment via a computerized maintenance management system (CMMS). The findings indicated that GIS and BIM digital models could indeed be integrated through straightforward linking mechanisms without compromising the efficiency of information synchronization and management. When comparing 2D facility management approaches with 3D formats, the advantages of 3D visualization became clear. This three-dimensional representation allowed for a more intuitive understanding of spatial dynamics and interactions, facilitating quicker identification of potential issues and more efficient maintenance operations. Consequently, integrating these advanced digital models not only optimizes operational efficiency but also fosters a collaborative environment, fundamentally transforming building facilities management.  
building information modeling (BIM); geographic information system (GIS); digital twin; facility management; computerized maintenance management system (CMMS) Article  
A New Approach to 3D Facilities Management in Buildings Using GIS and BIM Integration: A Case Study Application _Appl. Sci._**2024**, 14, 11097. [[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))
## 3 Numerical Results and Discussion
### Total Period Number  
Current research in thermal radiation manipulation using periodic structures is focused on two main areas. The first is to create highly selective narrow-band thermal emitters with wavelength, directional, and polarization selectivity. These emitters hold promise for applications in IR sensors and various IR sources. Secondly, researchers are exploring the use of PCs to design wide-band thermal emitters that exhibit near-black-body thermal emission within a specific wavelength range while significantly reducing emissions outside that range. The primary driving forces behind advancements in broadband selective thermal emitters include applications in thermophotovoltaic energy conversion, solar-thermophotovoltaic conversion, and solar absorbers/reflectors. Consequently, changing \(M\) is one of the ways to control the bandwidths of thermal radiation in the desired frequency ranges. The optimization of transmittance loss bandwidth serves as a pivotal performance parameter for such multilayer structures (THz filters, thermal emitters). The aim is to achieve a more refined bandwidth, thereby enhancing the quality factor (Q) for narrow band filters. This sought-after enhancement can be effectively realized through the strategic augmentation of the periodic number \(M\). In order to study the effect of the changes in total period number \(M\) on thermal radiation, several period numbers are applied to the structure. As shown in Figure 5, the thermal radiation spectrum is plotted for \(M=1\), \(3\), and \(5\) for the TE mode. Thermal radiation fluctuations increase within a specific frequency range as the period number \(M\) increases. As a result of increasing \(M\), desirable band-gaps and peaks occur. Furthermore, the number of periods can be changed to tailor the position of the band-gaps. As \(M\) increases, the width of the PBGs decreases and switches to higher frequencies, as shown in the figure. However, these changes are so minor that they may be negligible. In general, the width of these gaps is proportional to the period number in the opposite direction. As a result, the number of periods has a significant impact on how band-gaps are configured and adjusted. In the preceding calculations, the loss in the semiconductor material InSb has been omitted. In practical applications, however, losses in the semiconductor material are unavoidable. The introduction of losses invariably results in the degradation of the filter's performance.  
Figure 4: Thermal radiation power spectrum (dashed rectangle depicts the omnidirectional spectrum) of the 1D MQP PC (with FB and ML sequences) as a function of frequency for TM polarization at T = 300 K and M = 1, under several incident angles \(\theta=0^{\circ}\), \(30^{\circ}\), \(50^{\circ}\), and \(85^{\circ}\). Here, the lattice constants are considered \(d_{A}=12\) μm, \(d_{B}=6\) μm, \(d_{C}=8\) μm, and \(d_{D}=6\) μm.
## 3 Experimental Protocol  
(1) Moisture Sensor Calibration Experiment  
The moisture sensor used in this paper obtains the moisture in the medium by the electrical conductivity of the medium to be measured. It is mainly used to measure the moisture in vegetable greenhouses and fruit trees. Since both soil and coal dust are in granular form, moisture is present within and between the particles. Therefore, soil moisture sensors (referred to as moisture sensors) can be used to measure moisture in granular coal bodies. However, the moisture sensor needs to be calibrated before it can be used to measure the moisture in the coal. The specific calibration steps are as follows:  
* Step 1: Longdong coal samples with the particle size of 0.18-1 mm were taken and dried under a vacuum environment for 24 h.
* Step 2: Take 4 clean water buckets and pour 5 kg of dried coal samples into each of them.
* Step 3: Take three of the buckets containing coal samples and add 0.714, 1.667 and 3 kg of 20% CaCl\({}_{2}\) solution to configure the coal samples with 10, 20 and 30% moisture, respectively.
* Step 4: Insert the moisture sensor to be calibrated into the above four coal samples in turn and obtain their readings.
* Step 5: Find the relationship between the reading value and the theoretical value and perform calibration.
2. Water Evaporation Experiment in Goaf Experiment steps on the effect of air leakage on the evaporation of water from the goal:
* Step 1: Take 60 kg of dried Longdong coal samples with particle size in the range of 0.18-1 mm and lay the coal samples flat into the experimental chamber, as shown in Figure 2.
* Step 2: Insert the 15 moisture sensors into the 15 measurement point locations (M1-M15) as shown in the figure and place the two air temperature and humidity sensors at measurement points 16 and 17.
* Step 3: Connect the moisture sensor and air temperature and humidity sensor to the data acquisition device and turn on the computer for data acquisition. Turn on the stepless variable speed extension.
* Step 4: A solution of CaCl\({}_{2}\) with a concentration of 20% was evenly sprinkled into the experimental chamber to bring the moisture from M1 to M15 to 30%.
* Step 5: Use the wind pressure and air volume meter to measure the wind pressure and air velocity at the location of the 17 measuring points of the air inlet and adjust the fan to make the wind pressure and air volume meter indicate 0.2 m\({}^{3}\)/s.
* Step 6: Make the experiment run continuously until the moisture sensor reading at each measurement point does not change and then end the experiment.
* Step 7: Repeat steps 1-5 and adjust the fan so that the airflow at the air inlet is 0.3 m\({}^{3}\)/s for data collection. Make the experiment run continuously until the moisture sensor reading at each measurement point does not change and then end the experiment.
* Step 8: Repeat steps 1-5 and adjust the fan so that the airflow at the air inlet is 0.4 m\({}^{3}\)/s for data collection. Make the experiment run continuously until the moisture sensor reading at each measurement point does not change and then end the experiment.
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
###### Abstract
By overcoming these issues and reducing working time and error rates, the application significantly enhances the user experience for basketball officials. The research presented in this paper has several goals:  
* Create an efficient solution with distinctive features for the digitization and digitalization of endgame match reports for the Basketball League of Serbia, which relies as little as possible on other solutions, packages, and frameworks. * To deliver a more efficient and error-resistant workflow, along with a new or enhanced user experience for basketball officials. * Reduce an official's working time by 30% by providing a workflow that is fast, intuitive, and resistant to errors, thereby decreasing the possibility of errors. * Prove that digital match reporting improves acquired data accuracy. Data accuracy is increased through application data validation methods during data entry on both the structural and UI levels, and visual control methods where digital reports are exposed to various officials who can observe and pinpoint an error during data entry, which occurs during a basketball game. * Digitalization brings various benefits to the Basketball League of Serbia that will emerge during and after the first season of using the software. This research addresses the need for a customized mobile solution that enables the digitalization of basketball endgame match records and generates searchable, digitally signed, and password-protected documents, thus providing a high level of security. The proposed system also offers the potential for template-based report configuration on mobile devices, supporting adaptability across different leagues and possibly other sports. In doing so, it addresses a notable gap in the existing literature. Furthermore, this study aims to demonstrate that such solutions can enhance user experience, reduce working time, and minimize the likelihood of errors, thus increasing data accuracy. To the best of our knowledge, there are currently no domain-specific solutions that combine real-time digitalization of structured game data (via the MZAP Android application) with the digitization of historical paper-based reports (via the in-development MZAP Converter) in a unified and workflow-integrated manner, as proposed in our approach.
## 2 Materials and Methods  
### Sampling Strategy and Sample Preparation  
A total of 95 discrete samples of soils, stream sediments, mine wastes, and flotation tailings from two decommissioned mining sites in the Friuli Venezia Giulia Region (north-eastern Italy) were collected and analysed by means of pXRF, ICP-MS, and DMA-80 for As, Cr, Cu, Fe, Hg, Mn, Pb, Sb, and Zn determination. The two mining sites are the carbonate-hosted \"Alpine-type\"/\"Mississippi-Valley-type\" (MVT) Pb-Zn Raibl mine [20; 21; 22; 23] and the Mt. Avanza \"fahlore-type\" Cu-Sb(-Ag) tetrahedrite mine [24; 25]. The Pb-Zn Raibl site (46.442772\({}^{\circ}\) N, 13.568707\({}^{\circ}\) E) is a base metal hydrothermal ore deposit hosted in a Triassic massive dolomite reef, characterised by a mineralogical assemblage where sphalerite (ZnS), galena (PbS), and pyrite/marcasite (FeS\({}_{2}\)) are the dominant constituents. The host rocks generally show a dominant carbonate composition. The main lithology is Triassic dolostone with a limited presence of shales, sandstones, tuffs, and ignimbrites [26]. At the Cu-Sb(-Ag) Mt. Avanza site (46.618780\({}^{\circ}\) N, 12.755281\({}^{\circ}\) E), the ore is composed of a (Zn-Hg)-rich variety of tetrahedrite, a complex Cu-Sb sulfosalt hosted in Palaeozoic rocks with a more pronounced silicate composition. The ore veins are located at the tectonic interface between the metamorphic Devonian limestones and Carboniferous, siliciclastic metapelites [24].  
From each original sample, two aliquots of 0.5-1 kg were collected. Rock and plant fragments were removed, and the aliquots were manually pre-homogenised in situ. Once in the laboratory, the samples were dried in the open air to prevent Hg volatilisation and sieved using a 2.0 mm sieve. The first aliquot was heterogeneous and prepared via a \"fast lab-preparation\" mode similar to the \"intrusive-unprepared\" mode reported by the U.S. EPA [11], with a very similar approach to the GTK procedure described by [PERSON] and [PERSON] [10] for sampling preparation. This \"fast lab-preparation\" mode simply consists of drying and sieving the sample using a 2.0 mm sieve and placing it into a pXRF sample cup. This procedure is a good compromise in terms of time if compared to the in situ mode (\"point-and-shoot\"), during which the analyser probe is placed in direct contact with the soil surface for a real-time measurement. The only differences between the procedure followed in this study and the GTK are represented by the use of 5 g of the sample placed in sample cups with a window X-ray film (Kapton) instead of plastic bags and a counting time of 60 s (three beams with different keV of 20 s each) instead of 120 s. The second aliquot of each sample was treated following a \"lab-type preparation\" [5], which is the \"intrusive mode\" described by the U.S. EPA [11], consisting of a time-consuming procedure in which 5 g of the sample were dried, milled in a tungsten carbide (WC) mill or an agate mortar, and homogenised into a fine powder.
# Finite Element Analysis on Wall Fluid Shear Stress on Cells under Oscillatory Flow
## 3 Results
### ARV on Cells Was Mediated by Cell Spacing, FSS Level, and Oscillatory Frequency  
One cell group was placed at the positions of y = 30 mm (#1), 20 mm (#2), 10 mm (#3) at a low FSS level of 0.1 Pa (x = 1 mm) (Figure 3A). A fluid-structure coupling interaction based on FEM was performed to obtain the ARV on the central cell. For all the three positions with low FSS level, the average ARV was positive and relatively stable over time under 0.25 Hz oscillatory flow (Figure 4A). However, with the increase in oscillatory flow frequency, the ARV fluctuation became increasingly sharp during pressure reversal (Figure 4B,C). When the cells in a hexagonally packed group approached each other, the ARV on the central cell surface also increased. At a high oscillatory frequency of 1 or 4 Hz, this increase in ARV was not evident when the cell spacing was 20 or 40 \(\mu\)m. However, the fluctuation range became wide. At a high FSS level 2.0 Pa (x = 10 mm), the average ARV of wall FSS at three positions fluctuated with different amplitudes (Figure 4D-F). The fluctuation during pressure reversal intensified with the increase in oscillatory flow frequency from 0.25 Hz to 4 Hz. When the cells approached each other, the ARV increased similarly to that at a low FSS level. For 4 Hz oscillatory flow, one additional cycle of oscillatory flow was added to comprehensively analyze the effect of oscillatory flow on ARV.  
Figure 4: Effects of cell spacing and oscillatory frequencies on average relative value (ARV) at two FSS levels. (**A**) and (**B**) ARV with different cell spacings in one oscillatory cycle with oscillatory frequencies of 0.25 and 1 Hz, respectively, for a low FSS level. (**C**) ARV with different cell spacings in two oscillatory cycles with oscillatory frequency of 4 Hz for a low FSS level. (**D**) and (**E**) ARV with different cell spacings in one oscillatory cycle with oscillatory frequencies of 0.25 and 1 Hz, respectively, for a high FSS level. (**F**) ARV with different cell spacings in two oscillatory cycles with oscillatory frequency of 4 Hz at a high FSS level.
## 5 De-Noising Filter Method and Experimental Results  
The recent developments in the \"Deep learning\" field have allowed the use of its algorithms for GNSS applications [24; 25; 27; 34]. In [34], an intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and Kalman Filter (KF) has been proposed to improve the accuracies of positional components as well as orientation components in real time. This scheme is able to overcome the limitations of KF. In [25], a \"deep learning\" algorithm was proposed to denoise the MEMS IMU output signals. The used algorithm is a Long Short Term Memory (LSTM) that was employed to filter the MEMS gyroscope outputs, in which the signals were treated as time series. LSTM is an artificial recurrent neural network (RNN) architecture that is useful for classifying, processing and making predictions based on time series data. Therefore, with a combination of the works presented in [25; 34], LSTM is implemented in this paper to learn and compensate for the residual errors of robust-EKF in order to improve the position accuracy. Hence, the rEKF-LSTM hybrid schemes are  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RMS Error (m)** & **GPS** & **GAL** & **GLO** & **GPS/GAL/GLO** & **Robust-EKF GPS/GAL/GLO** \\ \hline
**RMS-E** & 1.42 & 1.34 & 31.00 & 8.11 & 0.74 \\ \hline
**RMS-N** & 1.46 & 1.2 & 6.65 & 2.74 & 0.75 \\ \hline
**RMS-U** & 3.94 & 3.08 & 25.7 & 9.50 & 1.82 \\ \hline
**3D-RMS** & 4.43 & 3.56 & 40.81 & 12.73 & 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMS Errors in Position for the five scenarios at station ABMF in 24 h.  
Figure 6: Comparison of position errors between robust-EKF and WLSE.  
proposed as a method capable of learning how the state vector behaves based on the dynamic of the filter. The details of rEKF-LSTM hybrid schemes and the experimental results for GNSS data are detailed in the upcoming Sections 5.1 and 5.2.
## 4 EE Improvement Issues for Each Component
### RF Component  
As mentioned beforehand, \"RF components\" refers to the DAC, mixer, and filter. Since an LSAS has an RF component for each antenna, the power consumption of RF components is significant. Figure 4 shows the RF power consumption versus the number of TX antennas, \(N_{t}\). In the case of \(P_{rf}=5\) W and \(N_{t}=600\), \(P_{RF}\) becomes 3000 W which is definitely not acceptable. There have been several studies aiming at overcoming this problem. It has been shown that since an LSAS can give significant channel gain, using coarse RF components with very low power consumption could be acceptable [8].  
It is better to maintain the same level of RF power consumption regardless of TX antenna increase. Assuming low power RF components are well-designed, we chose \(P_{RF}=6\) W, when \(N_{t}=600\), similar to current BS systems.  
Figure 4: \(P_{RF}\) as \(N_{t}\) increases.  
Figure 3: Examples of antenna form factor with 400 (20 \(\times\) 20) antennas, antenna spacing 0.5\(\lambda\)\(\sim\)2\(\lambda\), and carrier frequencies of 2 GHz and 6 GHz.  
There are several studies related to the RF component of LSAS [34; 35; 36]. In [34], a novel single-RF LSAS transmitter was presented. It comprises only a single power amplifier and does not require any mixer. It was shown that the total power efficiency of the transmitter in the LSAS regime is significantly higher than the total power efficiency in classical design. In [35], authors examined the effects of hardware impairments on a LSAS single-cell system. The simulations were performed using simplified, well-established statistical hardware impairment models as well as more sophisticated and realistic models based upon measurements and electromagnetic antenna array simulations. They demonstrated that low-resolution digital to analog converters decrease the overall power consumption while maintaining low average user received error vector magnitude and controlled unwanted emissions. The result showed that in LSAS, there is room for relaxing the stringent hardware requirements typically in place on current MIMO systems. In [36], authors analyzed the impact of RF hardware imperfections at the BSs by studying an uplink communication model with multiplicative phase-drifts, additive distortion noise, noise amplifications, and inter-carrier interference. According to their work, the key to cost-efficient deployment of large arrays is low-cost antenna branches with low circuit power, in contrast to today's conventional expensive and power-hungry BS antenna branches. Such low-cost transceivers are prone to hardware imperfections, but they showed that the huge degrees-of-freedom would bring robustness to such imperfections.