esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Quantitative Real-Time PCR  
After the laboratory simulation experiment, the water samples in each box were passed through a suction filter device for bacteria collection, the microorganisms were cut off on a 0.22 \(\upmu\)M filter membrane, and then DNeasy powersoil Kit (Qiagen, Germany) was employed to extract the total DNA in the water samples. The total biomass of bacteria was determined by qPCR using primers Eub338(5\({}^{\prime}\)-ACTCCTACGGGAGGGCAG-3\({}^{\prime}\))/Eub518(5\({}^{\prime}\)-ATTACGCGGCTGCTG-3\({}^{\prime}\)) targeted on the 16S rRNA gene [27]. The standard curve was constructed by drawing the logarithm of the concentration of seven gradient dilutions of genomic DNA and the threshold period (Ct value) produced by qPCR analysis. _E. coli_ genomic DNA was used as the standard template for quantitative detection of bacteria. QPCR was performed in a Step One Plus \({}^{\text{TM}}\) real-time PCR system using faststart universal SYBR Green master (Rox) (Roche, Switzerland). Each PCR reaction included 1 \(\upmu\)L DNA template, 2 \(\upmu\)L 10 mM bacterial primers Eub338 / Eub518, 10 \(\upmu\)L SYBR green mix, and 7 \(\upmu\)L H\({}_{2}\)O for a total of 20 \(\upmu\)L. The cycling program consisted of denaturation at 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s, 54 \({}^{\circ}\)C for 30 s, 72 \({}^{\circ}\)C for 20 s. Melting curve analysis was established by increasing the temperature from 60 to 95 \({}^{\circ}\)C.
## 6 Conclusions and Future Works  
VO systems are widely applied in pathfinding robots, autonomous vehicles operating in industry, etc. DL has had impressive results in building visual SLAM and VO systems. However, DL requires a large amount of data to train the model. In this paper, we introduced the TQU-SLAM benchmark dataset, collected from an RGB-D image sensor (Intel RealSense D435) moving in the corridors of three buildings of a particular length (FO-D is 230.63 m, OP-D is 228.13 m). The ground-truth data of the TQU-SLAM benchmark dataset include 6-DOF camera poses/camera trajectory and a 3D point cloud. It was also tested to estimate VO using some traditional features and features extracted from DL, such as VGG based on the PySLAM framework. Among them, the ORB2 features have the best results (\(Err_{d}=5.74\) mm), and the ratio of the number of frames with detected keypoints of the SHI_TOMASI feature is the best (\(r_{d}=98.97\%\)). Shortly, we will renormalize the TQU-SLAM benchmark dataset and prepare many types of ground-truth data for evaluating visual SLAM and VO models. We will build a pretrained model on the TQU-SLAM benchmark dataset and perform comparative research on DL models for visual SLAM and VO systems.  
**Author Contributions:** Methodology, T.-H.N. and V.-H.L.; Writing--original draft, T.-H.N., V.-H.L., H.-S.D., T.-H.T. and V.-N.P.; Writing--review & editing, V.-H.L. and H.-S.D.; Visualization, H.-S.D., T.-H.T. and V.-N.P.; Supervision, V.-H.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research is supported by Hung Vuong University under grant number HV23.2023.  
**Data Availability Statement:** The data presented in this study are available in this paper.  
**Conflicts of Interest:** The paper is our research, not related to any organization or individual. It is part of a series of studies on visual SLAM and VO systems.
## 3 Results  
### Evaluation of the Assimilation System  
#### 3.1.1 Comparison of Forecast Field  
In order to evaluate the performance of the assimilation system, aggregated root-mean-square-error (RMSE) vertical profiles of horizontal wind field, temperature, and water vapor mixing ratio (Qv) forecast at 24-h are shown in Figure 5. The RMSE is calculated within the inner domain, verified against the \(0.25\times 0.25^{\circ}\) ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). The RMSE of 2016 is calculated by averaging the RMSE of 11 24-h forecasts in 2016, and so does 2017. For 2016, POD has its greatest impact on horizontal wind above 600 hPa (Figure 5a). For temperature forecast (Figure 5b), POD is close to CON experiment, with slight improvement near the surface. For water vapor mixing ratio (Figure 5c), the RMSE is slightly decreased in the middle and lower levels. The statistic results from 2017 are shown in Figure 5d-f). Clear reduction of RMSE in POD shows in the middle and lower levels for horizontal wind. Above 500 hPa, POD reveals almost the same profiles with CON. Similar to the 2016 statistic results, POD has little impact on temperature forecast. POD outperforms CON in the water vapor mixing ratio forecast in the lower pressure levels. Overall, though the average RMSE of 24-h forecast is sensitive to different pressure levels of different variables, generally the improvements can be found with assimilation.  
Figure 6 shows the average RMSE evolution as the forecast time increases for U and V at 10 m, T and water vapor mixing ratio (Qv) at 2 m. RMSE is averaged for all the 22 forecasts. The RMSE of U and V at 10 m increase with forecast time (Figure 6a,b). The improvements of assimilation appear approximately at 6 th hour, after which POD reduces RMSE throughout the remaining forecast period. For T (Figure 6c) and Qv (Figure 6d), POD experiment mainly maintains lower RMSE than CON,  
Figure 5: Vertical profiles of average RMSE of 24-h forecasts for horizontal wind, temperature, and water vapor mixing ratio against the ECMWF reanalysis. The errors in (**a–c**) are obtained from 11 forecasts of 2016, and (**d–f**) from 2017. The RMSE is calculated for the inner domain in Figure 4.  
though the improvement is smaller than wind field. Overall, POD has a positive impact on the forecast field compared to CON, which indicates the improvement of assimilating satellite and radar data with POD-4 DEnVar method.
## 5 Semantic Understanding of Concept Layer  
The concept layer belongs to high-level abstract semantics. The concept layer semantics of the geographic scene is the comprehensive judgment and representation of concepts such as function and pattern. The main application of the remote sensing image processing method is scene classification. In general, high-level semantic information can be acquired based on low-level information analysis, and low-level information can be transferred to higher-level by modeling. Through layer-by-layer refinement, the final representation of the concept layer semantics is closer to the abstract thinking of human beings, and then the geographic scene semantics in the HSR remote sensing images have more practical significance. Therefore, the concept layer semantics of geographic scenes are derived from the visual layer semantics or the object layer semantics.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
###### Abstract  
Multiple input-multiple output (MIMO) is a key enabling technology for the next generation of wireless communication systems. However, one of the main challenges in the implementation of MIMO system is the complexity of the detectors when the number of antennas increases. This aspect will be crucial in the implementation of future massive MIMO systems. A flexible design can offer a convenient tradeoff between detection complexity and bit error rate (BER). Deep learning (DL) has emerged as an efficient method for solving optimization problems in different areas. In MIMO communication systems, neural networks can provide efficient and innovative solutions. This paper presents an efficient DL-based signal detection strategy for MIMO communication systems. More specifically, a preprocessing stage is added to label the input signals. The labeling scheme provides more information about the transmitted symbols for better training. Based on this strategy, two novel schemes are proposed and evaluated considering BER performance and detection complexity. The performance of the proposed schemes is compared with the conventional one-hot (OH) scheme and the optimal maximum likelihood (ML) criterion. The results show that the proposed OH per antenna (OHA) and direct symbol encoding (DSE) schemes reach a classification performance F1-score of 0.97. Both schemes present a lower complexity compared with the conventional OH and the ML schemes, used as references. On the other hand, the OHA and DSE schemes have losses of less than 1 dB and 2 dB in BER performance, respectively, compared to the OH scheme. The proposed strategy can be applied to adaptive systems where computational resources are limited.  
Keywords:MIMO systems; deep learning; detection; labeling; ML criterion; detection complexity; BER performance +
Footnote †: journal:  
_Article_
## 4 System Implementation
### Implementing the Experiment Layer
Figure 6 shows the capture methodology designed to meet the rapid state changes in an experiment and the monitoring flow of the management element software. The manager element stores the information in the temporary database in the form of steps according to the changes taking place in the didactic experiment. In this work, the definition of a step is the storage of the status of all the inputs and outputs used in the didactic experiment, and each change in one of the inputs and outputs is considered a new step, and, again, the element stores the step. * Camera: For the camera of the didactic plant, a low-cost webcam connected to the remote laboratory computer was used. This camera is activated by the didactic twin layer and the manager element at each step where a state change occurs in the experiment. The camera serves two purposes: the first is to contribute to the data collection environment, and the second is to provide the student with visual access to the correct and incorrect images of the experiment. The next section will describe how the data collection environment was implemented to capture the digital twin of the experiments. * Data Collection Environment: To present the environment used for data collection, it is divided into two parts: the hardware utilized to execute remote laboratory applications and the optical setup. The hardware used to execute applications in the remote laboratory was a personal computer with the following specifications:
* Model: Legion 5i;  
Figure 6: Data capture flow in the didactic experiment. * Processor: Intel Core i7-11800H;
* Dedicated Graphics Card: GeForce RTX 3060, 6 GB;
* Memory: 16 GB RAM. The optical setup used for data capture featured the following specifications:
* Camera: Logitech C920s Full HD Webcam;
* Resolution Used: 1280 \(\times\) 720;
* Frame Rate (FPS): 10;
* Lighting: Uncontrolled. This was the environment used for testing the system during the implementation of experiments for both teachers and students.
## Materials and Methods
### GC-MS Conditions  
Chromotographic separation was performed using an Agilent 6890N (Palo Alto, CA, USA) gas chromatography system combined with an Agilent 5975 quadrupole mass selective detector and splitless injector equipped with a BP-20 (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness) fused silica capillary column supplied provided by SGE (Darmstadt, Germany) with helium (Helium N60, Air Liquid, Alges, Portugal) as the carrier gas at a flow rate of 1 mL/min (column-head pressure: 13 psi). An insert of 0.75 mm i.d. was used and the injector temperature was fixed at 235 \({}^{\circ}\)C. The GC oven temperature was held for 1 min at 80 \({}^{\circ}\)C, and ramped to 220 \({}^{\circ}\)C at 50 \({}^{\circ}\)C/min (held for 5 min). The manifold, GC-MS interface, and quadrupole temperatures were held at 180, 220, and 180 \({}^{\circ}\)C, respectively. In addition, MS detection was performed in full scan, the ion energy used for the electron impact (EI) was 70 eV, and the source temperature was 180 \({}^{\circ}\)C. The electron multiplier was set to the auto-tune procedure. The mass acquisition range, made in full scan mode, was 30-300 m/z, 1.9 spectra/s. The TMA and DMA identification was carried out by comparison with the mass spectra and retention times of pure standards using the same instrumental conditions, and by matching the mass spectra with the data system library (NIST05).
## 2 Materials and Methods
### Method
#### 2.2.2 Texture Measurement  
The texture of meat samples was assessed using the Stable Micro Systems (SMS) TA XT Plus texture analyser (Stable Micro Systems, Godalming, UK). The movement of the upper probe was controlled by a computer. The instrument allows for the use of various probes to measure different types of shears, compressive, and torsional stresses applied in multiple directions and over varying durations, thereby enabling a comprehensive analysis of the samples' textural properties.  
Texture profile analysis (TPA) was performed, and the following attributes were measured: (1) Hardness: The resistance [N] at maximum compression during the first compression cycle. It represents the force required to achieve a specific deformation and reflects the hardness of the sample at the initial bite. (2) Cohesiveness: The ratio [-] of the positive force during the second compression cycle to that of the first cycle (downward strokes only). It indicates the strength of the internal bonds that maintain the structure of the sample. (3) Springiness: Expressed as a ratio [mm-\(\cdot\)mm\({}^{-1}\)] or percentage of the product's original height. Springiness is typically measured by dividing the height during the second compression cycle by the original compression distance. (4) Chewiness: The force [N] required to chew a solid sample until it reaches a steady state for swallowing. It is calculated as the product of hardness, cohesiveness, and springiness [18; 19; 20]. The meat samples were diced (40 mm \(\times\) 40 mm) with a height of 20 mm. A P/75V steel cylinder plate was used as the probe. The pre-test speed of the probe was 10 mm/s, with both the test speed and post-test speed set to 1 mm/s. Maximum compression was set to 50% of the sample height (10 mm) and lasted for 1 s, after which the sample was allowed to relax. The sampling frequency was 50 Hz, and each sample was measured three times.  
A limitation of this investigation may arise from the uneven distribution of adipose and connective tissue in mammalian muscles. Although the number of samples was considered adequate, there is a possibility that some pieces contained more or less connective tissue than average. However, since no outliers were observed, this effect was deemed to have a minimal impact on the results.
## 6 Harvesting Methods  
Microalgae harvesting is one of the most challenging aspects of the biorefinery process when applied at industrial scale owing to the high operational costs, which is estimated to be approximately 20-30% of the total downstream processing costs [76].  
It is critical to choose the most appropriate harvesting method to apply, dependent on the strain and the culturing conditions, as well as the intended use for the biomass and any derived products. It is important to bear in mind that harvesting microalgae is often a case of removing a lot of water from a small amount of suspended biomass--as such, an increase in the culture density lowers the overall harvesting cost per biomass unit volume. Various approaches can be explored and applied to increase relative biomass densities and lower costs.  
Flocculation is a low-cost way to concentrate biomass before centrifugation [77]. However, residues of the flocculant can be found in the harvested biomass and in the spent medium, which can pose a significant problem for commercial application [78]. Magnetic nanoparticles (Fe\({}_{2}\)O\({}_{3}\)) have been explored for harvesting microalgae. These particles adsorb on the outside of the cells and seem to work better with a cationic polymer coating, improving the adsorption on the negatively charged microalgal cell surface. Through application of a magnetic field, both flocculation and separation from the medium can be achieved in one step and the nanoparticles can be recovered and reused after harvesting [77]. Genetic manipulation of _Chlamydomonas_ has been used to create magnetic cells through overexpression of the iron binding protein ferritin in the chloroplast that, combined with high iron level in the medium right before harvesting, permitted magnetic separation of the biomass, facilitating lower energy cost dewatering [79].  
Filtration methods can also be applied to concentrate biomass or products that have been secreted into the growth media. Micro- or dia-filtration is seen as an expensive alternative to flocculation. New tangential flow filtration (TFF) methods are under development in an effort to use less energy and decrease the time needed for filtration. For instance, the energy consumption necessary to dewater a culture of _Chlorella minutissima_ using TFF to achieve a biomass density of up to 220 g/L (coupling with centrifugation) was less than 1 kWh/kgDW [80].
# A Subjective Study on User Perception Aspects in Virtual Reality
## 4 Subjective Results and Discussions
### Impact of Encoding Parameters
#### 4.2.3 Optimal Encoding Parameters  
In this part, we discuss the optimal encoding parameters for each video content. The question here is \"Given a bitrate, which encoding parameters should be chosen for the best QoE?\". Figures 7-9 respectively show the presence scores, the perceptual quality scores, and the acceptability rates with respect to the bitrates. In these figures, the five marker types correspond to the five chosen QP values. The four line types represent the four selected resolutions.  
To achieve good QoE, the minimal bitrates of Content #1, Content #2, and Content #3 are respectively about 1.5 Mbps, 5.6 Mbps, and 11.9 Mbps for device set D1, and 2.2 Mbps, 9.6 Mbps, and 11.9 Mbps for device set D2. So the bitrate for good QoE considerably varies across different contents. In [23], it was found that there is only a small difference of the perceptual quality between the bitrates of 15 Mbps and 8 Mbps. Also, the finding in [26] is that the perceptual quality at 10 Mbps is only slightly higher than that at 6 Mbps. However, from Figure 8, we can see that this depends on content characteristics. In particular, for Content #3, the difference between 15 Mbps and 8 Mbps or between 10 Mbps and 6 Mbps is significant (i.e., higher than 0.40 MOS). In the next subsection, a discussion of the impact of content motion is presented in detail.
## 3 Results and Discussion
It is also found that the open-circuit voltages of PERC silicon solar cells are larger than those of Al-BSF silicon solar cells at the same injection level. That is because the PERC structure featuring an AlOx dielectric rear passivation can decrease the total recombination parameter compared with Al-BSF silicon solar cells. The smaller the recombination parameter, the higher the open-circuit voltage of a solar cell [49; 50; 51]. Figure 9 depicts the effect of the \(J_{01}\)/\(J_{02}\) ratio on short-circuit current density (\(J_{sc}\)) for PERC and Al-BSF silicon solar cells under different injection levels. As can be seen from Figure 9, as the \(J_{01}\)/\(J_{02}\) ratio decreases and the injection level is varied from 200 W/m\({}^{2}\) to 1200 W/m\({}^{2}\), the \(J_{sc}\) of PERC silicon solar cells increases more quickly than that of Al-BSF silicon solar cells at 25 \({}^{\circ}\)C. This is because the PERC solar cell has a higher carrier collection efficiency compared with Al-BSF solar cells. \[J_{d1}=J_{01}\bigg{(}\exp\frac{V+JR_{s}}{n_{1}V_{T}}-1\bigg{)} \tag{2}\]5 Variation in open-circuit voltage (\(V_{\alpha c}\)) with the \(J_{01}\)/\(J_{02}\) ratio for PERC and Al-BSF silicon solar cells under different injection levels. Figure 10 shows the variation in FF with the \(J_{01}\)/\(J_{02}\) ratio for PERC and Al-BSF silicon solar cells under different injection levels. The fill factors of PERC and Al-BSF silicon solar cells decrease with the decrease in \(J_{01}\)/\(J_{02}\). At the same temperature and injection level, the FF of a PERC silicon solar cell is always higher than that of an Al-BSF silicon solar cell. This result agrees with [52].
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 8 Future Work  
We believe that the presented work is not only about the ability to efficiently implement an ANN topology for a communication system on an FPGA, but rather, it is an actual step towards bridging the gap between the communication system layer and the hardware implementation layer for ANN-based designs. There are still many challenges to tackle in terms of system performance improvement, ANN optimization, and efficient hardware implementation. However, we believe that providing a cross-layer analysis approach is critical for any research advancement towards implementable algorithms.  
At the communication system layer, the future work will include modeling real non-static channels that vary with time and evaluate how it affects the performance of the trainable demapper. In addition, an adaptable constellation scheme based on the channel state could be developed to improve the performance of the system. On the ANN layer, there are further possibilities to improve the selection of hyperparameters (number of layers, type of the neurons, etc.). On the hardware layer, future research could focus on the adaptation of resources in a feedback loop to drive the system automatically and dynamically towards optimal implementations. This future work should be studied again in a cross-layer approach to increase the understanding of the impact of each layer on other layers and to show their hidden interdependencies.  
Funding acquisition, S.t.B. and N.W.; investigation, B.H.; methodology, B.H., S.D., M.H. and J.C.; project administration, M.H., S.t.B. and N.W.; software, J.N.; Supervision, S.t.B. and N.W.; validation, J.N. and S.D.; writing--original draft, J.N., S.D. and J.C.; writing--review and editing, B.H., M.H., S.t.B. and N.W. All authors have read and agreed to the published version of the manuscript.  
This work was partly funded by the German ministry of education and research (BMBF) under grant 16 KIS1185 (FunKI) and under grand 16 KISK004 (Open6 GHuB).  
The authors declare no conflict of interest.
## 3 Methodology
### Landslide Control Parameters
#### 3.1.3 Parameters Related to the Seismic Event  
These control parameters provide relevant information about the properties of the seismic event related to the landslide occurrence, such as the peak ground acceleration (PGA) and Arias intensity (I\({}_{\rm A}\)). Other parameters that characterize landslide occurrence include the safety factor, critical acceleration, and Newmark displacement. The safety factor (FS) is a parameter used to evaluate the stability of slopes and determine if they are unstable (FS \(<\) 1) or not (FS \(>\) 1). In this study, FS was calculated using [PERSON]'s proposal [40], considering the friction angle, cohesion, slope, unit weight, and depth of the sliding block. Critical acceleration is a parameter used in seismic stability analyses and represents the minimum seismic acceleration required to overcome the maximum strength of the slope material and trigger a landslide. The Newmark displacement (D\({}_{\rm N}\)) quantifies the expected displacement of a slope during a seismic event, considering different seismic parameters (PGA or I\({}_{\rm A}\)) and the distance to the seismic source. The susceptibility to seismically induced landslides is based on the computed Newmark displacement. According to the criteria set by the authors in [41], areas can be divided into non-susceptible (D\({}_{\rm N}\) \(<\) 1 cm), low susceptibility (D\({}_{\rm N}\) \(=\) 1-5 cm), moderate susceptibility (D\({}_{\rm N}\)\(=\) 5-15 cm), and high susceptibility (D\({}_{\rm N}\)\(>\) 15 cm). All these seismic parameters related to the 2011 Lorca seismic event correspond to those obtained by [27].