esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Protection and Multi-Database Data-Driven Models
## 5 Conclusions and Future Work  
This paper investigated how data masking methods impact the performance of data integration and of machine-learning-based data-driven models. The experimental results show that most of the masking methods we implemented provide good protection even when two individually masked databases are integrated. As the protection increases, the performance of data-driven models decreases. A trade-off of protection and data utility can be achieved through parameterization. A limitation of this study, and of most studies on the effects of masking methods, is that the level of protection depends on both the concrete database and the user requirements. That is, there is no universal solution. In any case, a take-away message of our research is that we can protect data so that correct database integration is avoided while data-driven models can be built successfully, to a certain extent, from the integrated data.  
Based on the explorations in this work, there will be several directions for future research. Firstly, in data integration, we can also consider the impact of reidentification from the point of view of the importance of common attributes, in addition to the number of common attributes. Secondly, more fine-tuning of machine learning algorithms can be conducted for better data utility, given the same protection level. Third, we have considered supervised machine learning for numerical data. Extensions for non-numerical (e.g., categorical) data and for unsupervised machine learning (e.g., clustering) are also of relevance. Another line of work is to consider over-representation of individuals [31] and other biases, such as temporal bias [32] and spatial bias [33], applied to the data and see if they affect the results; for example, whether over-representation of some types of users affects the quality of database integration. Finally, our focus was on numerical data. The extension of this work to masking methods for non-numerical data, as well as to protection by means of synthetic data, is another research direction.  
Methodology, L.J. and V.T.; Software, V.T.; Writing--original draft, L.J. and V.T.; Writing--review and editing, L.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation.  
The authors declare no conflict to interest.  
Figure 7: Results for the CASC data set masked using MDAV and for linear regression, comparison for \(nC=1,\ldots,6\). Result of a single execution (**left**), mean results of 20 executions (**middle**), and variance of 20 executions (**right**).
# Wind Lidar and Radiosonde Measurements of Low-Level Jets in Coastal Areas of the German Bight
###### Abstract  
For wind energy, the knowledge of the available wind resource is essential. Therefore, specific wind phenomena at the altitude range of wind turbines are currently the focus of investigations. One such specific feature is the low-level jet (LLJ). The article analyses LLJ properties at two locations in the German Bight: A wind lidar system for measuring wind profiles at heights from 50 m to 500 m a.g.l. (above ground level) was first installed at the offshore island of Heligoland, Germany, and then at the coastal island of Norderney, Germany, for one year. The LLJ is defined here as a maximum horizontal wind speed in the vertical profile of horizontal wind speed followed by a minimum wind speed, independent of the mechanism or origin of the phenomenon. The two sites showed a similar annual and diurnal distribution of LLJ events with a maximum occurrence in spring and summer and during the night, and a most frequent jet core height of around 120 m a.g.l. Based on radiosondes launched from Norderney at midnight and noon, it is shown that LLJ events at noon are most frequent when atmospheric conditions are stable. A case study shows the horizontal extent of an LLJ event over at least 100 km by simultaneous wind lidar measurements at four sites in the German Bight and mesoscale simulations with the weather research and forecast (WRF) model.  
vertical wind profile; wind energy; coastal effects; low-level jet; wind lidar; wind shear 133050839
## 7 Characterisation Simulations  
In order to understand and predict the performance of GMOD as a GRB detector and ensure that the correct response is used if the event location is known, it is important to have an accurate spacecraft and detector model. The engineering qualification model (EQM) of EIRSAT-1, developed using MEGAlib, is described in [29]. This model needed to be updated to reflect the changes to the configuration of GMOD in the flight model. The first hardware design change was in the mounting of GMOD in EIRSAT-1. In the EQM, GMOD was mounted to the GMOD motherboard. In the FM, GMOD is suspended above the motherboard, and mounted in the PCB stack via, on a structural bracket, the GTRB. The second hardware change was that ceramic capacitors were used on the GMOD EQM MB. The GMOD FM motherboard has metal framed capacitors, as these are more robust against PCB flexure. Simulations were then performed with this updated model to correlate with measurements in the lab and validate the updated EIRSAT-1 model.  
Section 7.1 provides an overview of the MEGAlib software v4.0 used to update the model and run the simulations. Section 7.2 outlines the updates made to the spacecraft geometry model. Section 7.3 presents the results of the simulations with a comparison to the physical measurements to confirm that the model was representative of the performance of the physical spacecraft.  
Figure 15: Each panel shows the counts recorded in a GMOD trigger as a function of time. A \({}^{137}\)Cs source was used to trigger GMOD; the source was briefly positioned at each face of the spacecraft for \(\sim\)10 s. The protected trigger data was plotted. The maximum counts were recorded when the source was at the +Z face; each panel shows this max count value as a dashed green line to facilitate trigger comparison.
## 2 Materials and Methods
### Data Processing
#### 2.3.4 Estimation of Thermal Diffusivity, \(\alpha\)  
The thermal diffusivity \(\alpha\), can be experimentally determined by exposing a sample of the material to a high-intensity short duration heat pulse in one face, while recording the temperature evolution from the opposite face, i.e., in transmission mode. This approach, originally proposed by [PERSON] (2005), is commonly referred to as the \"[PERSON] method\" or the \"Flash method\", and it counts with a standard test method E1461 ([PERSON], 1961) and standard practice E2585 ([PERSON], 1961) from the ASTM. According to these standards, the diffusivity can be obtained by solely knowing the specimen thickness \(L\) and estimating the time when the temperature reaches half the maximum value \(t_{1/2}\)([PERSON], 1961):  
\[\alpha=\frac{0.139}{t_{1/2}}L^{2} \tag{3}\]  
This technique was later refined using different time parameters (partial times) to consider heat losses. The resulting technique is called the partial times method ([PERSON], 1961), from which the following equations are proposed to estimate the diffusivity ([PERSON], 1961; [PERSON], 1961):  
\[\alpha=\frac{L^{2}}{t_{5/6}}\left[0.818-1.708\frac{t_{1/3}}{t_{5/6}}+0.885 \left(\frac{t_{1/3}}{t_{5/6}}\right)^{2}\right] \tag{4}\]  
\[\alpha=\frac{L^{2}}{t_{5/6}}\left[0.954-1.581\frac{t_{1/2}}{t_{5/6}}+0.558 \left(\frac{t_{1/2}}{t_{5/6}}\right)^{2}\right] \tag{5}\]  
\[\alpha=\frac{L^{2}}{t_{5/6}}\left[1.131-1.222\frac{t_{2/3}}{t_{5/6}}\right] \tag{6}\]  
Standard E1461 recommends employing small (from 6 to 30 mm in diameter) and thin (1 to 6 mm) circular discs to reduce non-uniform heating and to minimize heat losses and is strictly valid on measurements performed on fully dense (non-porous), homogeneous, and isotropic (opaque) materials. In the present study, the samples are anisotropic and non-homogeneous (i.e., CFRP), hence, results are of course subjected to incertitude. The samples however are relatively thin (3.4 mm) so heat losses are minimal, and although the samples are considerably larger (180 \(\times\) 120 mm\({}^{2}\)) than what is suggested by the norm, the use of xenon flash lamps illuminating the whole surface aids in reducing non-uniform heating (when compared to laser spot stimulation). In any case, the results presented herein can be considered sufficiently accurate for the purpose of this work. More precise measurements of the diffusivity are out of the scope of this study.
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
###### Abstract  
The high viscosity and low flow properties of some crude oil make them difficult to extract from oil reservoirs. This study investigated the mechanisms responsible for the enhancement of oil recovery using fractured dolomite core models. Bacterial strains, _Nocardiia cyriacj Georgica, Bacillus_ species, and _Pseudomonas putida_, isolated from Libyan oil fields, had the ability to biotransform heavy crude oil by reducing its viscosity and converting heavier components into lighter ones. The efficiencies of the three bacterial strains were assessed using sand-packed column experiments through the injection of bacteria to mimic in-situ oil recovery. _The optimum bidransformation values of Libyan Bouri crude oil were determined as 77.1_, 61.2, and 61.1% using the _Bacillus_ sp., _P. putida_, and _Nocardiia cyriacj Georgica_, respectively, at 55 \({}^{\circ}\)C. Viscosity analyses showed that these strains resulted in the reduction of the viscosity of the crude oil at two different temperatures of 37 and 55 \({}^{\circ}\)C. The highest recovery of residual oil was about 11.3% using _Bacillus_ sp. The study confirmed that the selected bacterial species were capable of displacing additional oil under simulated oil field conditions.  
viscosity; _Nocardiia cyriacj Georgica; Bacillus_ species; _Pseudomonas putida_; dolomite core; biotransformation
# B-Line Detection and Localization in Lung Ultrasound Videos Using Spatiotemporal Attention
## 5 Experiment Results
### Temporal B-Line Localization  
The temporal localization is designed to find the B-line frames in the LUS video by predicting the importance of each frame for the classification task. We already showed that the temporal information improved the classification accuracy (Table 3). Additionally, temporal attention can discriminate frames that contain B-lines. To this end, Table 4 shows the IoU between the predicted B-line frames (using different thresholds) and the ground truth annotations, with an accuracy up to 69.7%. The calculation of the IoU is illustrated in Figure 8, top. To put the average accuracy into context, the example in the figure had an IoU of 78%. Furthermore, spatial attention maps are visualized for two B-line frames in the figure showing the ability of the model to localize B-line artefacts spatially and totally, being able to spatiotemporally localize B-line artefacts.  
Additionally, a frame with the highest attention weight that is a representative frame with B-lines was identified on the test set with an accuracy of 88.6%. This could be useful to automatically provide clinicians with an exemplar frame to support the classification result.  
Figure 8: Generated temporal (horizontal axis) and spatial (heatmap overlaid onto B-Mode B-line frames) attentions estimated by our model on an example of an LUS video that includes both B-line and non-B-line frames. The top graph shows the temporal attention weights (in blue) and the corresponding ground truth annotations (in green). Spatial attention maps are visualized for B-line frames (for example frames 16 and 22): the yellow lines show the manual B-line annotations and, the correlation coefficient values (\(r\)), computed as described in the text, are presented at the bottom of each frame for illustration.  
Figure 7: **Top**: An example of polar coordinates applied to a sample B-line frame; the red cross shows the beam source. **Center**: The generated 1-dimensional diagram showing it’s related ground truth (green line, the black line is normal distribution), and **Bottom**: attention map values (red line) across the coordinates. In this example, the correlation coefficient value is \(r=0.71\).
# Study on the Impact of the Coastline Changes on Hydrodynamics in Xiangshan Bay
## 2 Materials and Methods
### Hydrodynamic Analysis
#### 3.3.5 Experimental E  
The distributions of surface residual currents in the surrounding before and after the change in the Area 5 coastline are shown in Figure 13a,b. Overall, compared to the control experiment, the distribution of residual currents in Experiment E changed minimally, except for a small branch of residual current on the east side of the coastline change area, which originally flowed in a southeast-northwest direction but now flows in a southwest-northwest direction.  
Figure 13: Current changes in Area 5. (**a**) Residual currents distribution before the change in coastline; (**b**) Residual currents distribution after the change in coastline; (**c**) Changes in the velocity of the peak flood tidal currents; (**d**) Changes in the velocity of the peak ebb tidal currents.  
Figure 13c,d show the changes in the flood and ebb surface current velocities before and after the coastline change in Area 5. After the coastline change, the change in high tide/low tide current speed repeatedly increased and decreased in this area, but the amplitude and range were not as large as in Area 1 and Area 2. In this study, station E (29.485\({}^{\circ}\) N, 121.52\({}^{\circ}\) E) was selected in the acceleration area to compare the changes in flood and ebb current velocities before and after the coastline change. Before the coastline change, the maximum current velocities for flood tide and ebb tide at Station E were 0.62 m/s and 0.50 m/s, with the average velocities being 0.34 m/s and 0.23 m/s, respectively. After the coastline change, the maximum current velocities for flood tide and ebb tide were 0.87 m/s and 0.67 m/s, with the average velocities being 0.45 m/s and 0.29 m/s, respectively. Compared to currents before the coastline change, the maximum flood and ebb current velocities at this point increased by 0.25 m/s and 0.27 m/s, and the average flood and ebb current velocities increased by 32% and 26%, respectively.
## 2 Synthesis of Hydrotalcites  
Hydrotalcite minerals with Mg\({}^{2+}\), Fe\({}^{2+}\), Mn\({}^{2+}\), Zn\({}^{2+}\), Cu\({}^{2+}\), and Ni\({}^{2+}\) anions in the position of Mg\({}^{2+}\) ions are often found in natural geological deposits [26] and therefore can be obtained from such sources. However, sometimes it is necessary to synthesize hydrotalcite group materials to achieve sufficient purity or other desired properties.  
The most used method of hydrotalcite synthesis is a coprecipitation of two metal salts in alkaline solution at aconstant pH value of approximately 10. A typical synthesis of a hydrotalcite employs a variation of a hydrothermal method, which often needs optimized reaction conditions to obtain desired product quality [27; 28; 29; 30; 31]. In addition, the hydrolysis reaction is often used [32; 33], as well as the sol-gel method [34; 35]. The co-precipitation method is usually based on the simultaneous precipitation of inorganic salts from basic solutions at constant or at increasing pH [36]. The combustion method of synthesis can be advantageous because it involves a very rapid chemical process, the explosive decomposition of organic fuels, e.g., glycine or urea. Carbon dioxide and water formed during the combustion are employed in producing complexes with the metal ions [37; 38]. Hydrotalcites obtained by the combustion process can be heated until the crystal lattice is destroyed, and subsequently recrystallized in a carbonate aqueous solution [39]. Sulphate-intercalated hydrotalcites for ammonia-nitrogen removal can be prepared using microwave-assisted synthesis. In this procedure the microwave irradiation contributes to the intercalation of sulphate anions to high crystallinity of the resulting material [40]. Another possibility is the preparation of hydrotalcites from industrial wastes. For example, the preparation of hydrotalcite from coal-combustion fly ash was explored. The synthesis was carried out as it is further described. Three molarity Three mol/L leaching solution of HCl, 30 min of aging at 65 \({}^{\circ}\)C and pH of 11.5, followed by crystallization for 12 h at 70 \({}^{\circ}\)C. Prepared hydrotalcite has a large external surface area with low microporosity, and it consists of sub-micron plate-like particles. The structural features of hydrotalcite prepared from fly ash were comparable to the hydrotalcites synthesized from pure raw chemicals with the exception of the presence of calcite [41]. The hydrotalcite-like compounds were also prepared by calcination and subsequent pozzolanic reaction form paper sludge from the newsprint company, using only recycled paper as a raw material [42]. Similarly, the hydrotalcite-like compounds were synthesized by the hydration reaction of activated art paper sludge-time mixtures at 20 \({}^{\circ}\)C [43].
## 3 Formation of Heterogeneous Groups
### Clustering Techniques for the Formation of Homogeneous Clusters
#### 3.1.6 Self-Organizing Map (SOM)  
Self-organizing map (SOM) [41] is a neural network method (the most popular among other neural network methods) used for cluster analysis. It is a clustering technique aimed at discovering categories in large datasets, for example to find customer profiles based on a list of past purchases.  
In SOM the neurons (called nodes or reference vectors) are arranged in a single two-dimensional grid, which can take the form of hexagons or rectangles as in Figure 2:  
Through many iterations, neurons on the grid will merge around areas with high density of data points. So, areas where there are too many neurons may reflect underlying clusters in the data. As the neurons move, they inadvertently b  
Figure 2: Self-Organizing Map (SOM) grids. The neurons are arranged in a single two-dimensional grid, which can take the form of hexagons or rectangles. Through many iterations, they will merge around areas with high density of data points.  
more closely reflect the overall topological shape of our data. The steps required to perform to achieve clustering using SOM are:  
1. Each node is initialized with a weight.
2. A vector from the training data is chosen randomly.
3. Each node is analyzed to calculate which weights are most similar to the input vector that was chosen. The winning node obtained is commonly known as the Best Matching Unit (BMU).
4. Then the neighborhood of the BMU is calculated. Now the number of neighbors decreases over time.
5. The winning weight is rewarded by becoming more like the sample vector. But neighboring vectors also become more similar to the sample vector. When a node is close to the BMU, its weights are also altered.
6. The step \"b\" is repeated for \"N\" iterations.
7. End.
## 3 Results
### In Vitro Drug Release of Nanogels and Nanogel-Embedded Contact Lens  
Before entrapment of the drug-loaded nanogels in the pHEMA lens, the release profiles of the LEV-loaded PSBMA nanogels were studied in PBS (pH = 7.4) at 37 \({}^{\circ}\)C using the dialysis bag diffusion method. A known amount of LEV-loaded PSBMA nanogels was placed in a dialysis bag, and the surrounding dissolution medium was sampled at various time points to determine the concentration of LEV via UV-vis. The amount of cumulative drug release was calculated via calibration curves. As seen in Figure 9, initially, the LEV release increased rapidly with time. An initial burst release of approximately 15.6% could be seen in the first 4 h, which may be due to the diffusion of the drug adsorbed on the surface of the nanogels. Drug release continued and reached 73.5% after 96 h. Drug-soaked contact lenses were used as controls. They exhibited a burst LEV release of more than 50% at 18 h, followed by slow release up to 64 h, with a cumulative drug release of 80.9% after 96 h. The fast drug release in the pHEMA hydrogel was mainly driven by physical diffusion due to the drug concentration gradient inside and outside the hydrogel matrix.  
As illustrated in Figure 9, the drug-release rate from the nanogel-embedded contact lenses was much slower than that of the drug-loaded nanogels and drug-soaked contact lens. At 3 days, about 22.6% of the drug had been released, and no obvious burst release was detected. Then, the drug was released at a slower but steady pace from day 3 (72 h) to day 10 (240 h) continuously. After 10 days, approximately 55% of the loaded LEV had been released from the contact lenses. The results suggested that the presence of nanogels in the hydrogel further slows down the drug release. The reason is that during the drug-release process, the certain amount of hydrophobic drug which is placed inside the nanogels must first diffuse through the nanogels into the pHEMA hydrogel matrix and subsequently diffuse through the hydrogel.  
The amount of LEV loaded in the contact lens was determined by the above LEV loading efficiency (Table 2). Thus, the LEV release from 8% nanogels-loaded hydrogel could be calculated according to cumulative release curve, with approximately 42 ug/day for 10 days on average. The recommended eye-drop dosage for a 0.5% solution of LEV is three drops per day. This means that the daily dose of LEV is calculated at 150 ug (1 drop = 50 mL [46]). Considering that the drug-laden contact lenses have a higher bioavailability (at least 50% [15]) than eye drops, our prepared ophthalmic administration formulation is potentially in the therapeutic range.  
Figure 8: Tensile stress-strain curves (**a**) and tensile strength at break (**b**) of pHEMA hydrogels with different nanogels content.
###### Abstract  
Tracking objects over time, i.e., identity (ID) consistency, is important when dealing with multiple object tracking (MOT). Especially in complex scenes with occlusion and interaction of objects this is challenging. Significant improvements in single object tracking (SOT) methods have inspired the introduction of SOT to MOT to improve the robustness, that is, maintaining object identities as long as possible, as well as helping alleviate the limitations from imperfect detections. SOT methods are constantly generalized to capture appearance changes of the object, and designed to efficiently distinguish the object from the background. Hence, simply extending SOT to a MOT scenario, which consists of a complex scene with spatially mixed, occluded, and similar objects, will encounter problems in computational efficiency and drifted results. To address this issue, we propose a binary-channel verification model that deeply excavates the potential of SOT in refining the representation while maintaining the identities of the object. In particular, we construct an integrated model that jointly processes the previous information of existing objects and new incoming detections, by using a unified correlation filter through the whole process to maintain consistency. A delay processing strategy consisting of the three parts--attaching, re-initialization, and re-claiming--is proposed to tackle drifted results caused by occlusion. Avoiding the fuzzy appearance features of complex scenes in MOT, this strategy can improve the ability to distinguish specific objects from each other without contaminating the fragile training space of a single object tracker, which is the main cause of the drift results. We demonstrate the effectiveness of our proposed approach on the MOT17 challenge benchmarks. Our approach shows better overall ID consistency performance in comparison with previous works.  
multiple object tracking; identity consistency; single object tracking +
Footnote †: journal: Applied Physics Letters