esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Applications of Biosensors in Bacteria Detection
### Recognition of Bacteria Using Biosensors
#### 2.1.2 Mechanical Biosensors  
The two main types of mechanical biosensors are based on quartz crystal microbalance (QCM) or cantilever technology [23]. QCM sensors are label-free piezoelectric biosensors which detect the resonance frequency change caused by the increased mass of the binding material on the sensor surface [23; 65; 103]. QCM sensors are able to detect bacteria as well and apply a variety of bioreceptors (for instance, aptamers [104; 105; 106], DNA [107], antibodies [108; 109; 110], or molecularly imprinted polymers [111; 112]). The most sensitive sensors among them are the aptasensors [104; 105] which can detect even bacterial spores [111]. Due to the development of sandwich-type assays, which apply nanoparticles for signal amplification, the detection of very few bacterial cells have been accomplished, even down to 10 CFU/mL in some cases as [PERSON] and co-authors reported [23; 113]. For the detection of _E. coli_, [PERSON] and colleagues prepared an aptamer functionalized QCM surface, with pre-enrichment with antibodies immobilized magnetic nanobeads [104]. This aptasensor was able to detect 1.46 \(\times\) 10\({}^{3}\) CFU/mL of _E. coli_ within 1 h. Gold nanoparticles can be used to increase the sensitivity of QCM analysis by conjugating to detection probe antibodies [104]. [PERSON] et al. reached a DL of 150 CFU/mL for _Campylobacter jejuni_[108].  
Microcantilever sensor technology offers high sensitivity, rapid response times, and it can be miniaturized for the development of point-of-care sensors. It typically has a bioreactor functionalized microcantilever which oscillates at a certain resonant frequency. Because of the induced mechanical bending, the resonant frequency of the cantilever changes upon an increase in mass on the sensor surface [23]. Microcantilever and nanocantilever sensors can detect even individual cells in a short time (30 min) [114; 115; 116]. The piezoelectric-excited millimeter-size cantilevers (PEMC) using antibodies as bioreceptors have been able to detect one hundred _Listeria monocytogenes_ cells in milk [117], and one _E. coli_ cell in buffer [23; 118].
## 3 Results and Discussion
### Interpretation of Analysis in Various Fermentation Conditions
The FTIR spectra analysis of the beer, shows the presence of -NH bonds of amines between 3000-3400 cm\({}^{-1}\) as well as primary and secondary amine bonds between 2800-3000 cm\({}^{-1}\), C=O bonds of amides and aromatic ketones, C-O bonds of esters between 1300-1450 cm\({}^{-1}\), C-O bonds of primary and secondary alcohols between 1040-1090 cm\({}^{-1}\) and finally Ar-C bonds of aromatics between 850-890 cm\({}^{-1}\). The total concentration of diacetyl in the beer must during and after fermentation results from a combination of the rate of formation of \(\alpha\)-acetolactate, its rate of conversion, and the rate of reduction of diacetyl to 2,3-butanediol and acetone. Any latency of removed diacetyl concentration resulting from late fermentation production cannot be reduced until the beer undergoes further processing, which may require prolonged maturation or other processing or corrective measures to bring the final diacetyl concentration within \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Peak** & **Compound** & **Retention Time (min)** & **Amount** \\ \hline
1 & Acetaldehyde (mg/L) & 4.211 & 7.29 \\
2 & DMS (\(\mu\)g/L) & 4.508 & 21.989 \\
3 & Ethyl acetate (mg/L) & 5.777 & 20.838 \\
4 & Methanol (mg/L) & 5.928 & 2.210 \\
5 & n-Propanol (mg/L) & 9.412 & 13.546 \\
6 & Iso-Butanol (mg/L) & 11.349 & 21.439 \\
7 & Isoamyl acetate (mg/L) & 12.748 & 2.456 \\
8 & Heptanone(mg/L) & 13.035 & 1.000 \\
9 & Butanol (mg/L) & 13.750 & 1.000 \\
10 & Amyl alcohols (mg/L) & 16.760 & 73.130 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Aromatic compounds of a final product of beer under classical fermentation. Figure 3: Chromatogram of a beer sample on a DBWAX column. the desired product target specification (0-40) micrograms per liter. This increase due to bacterial contamination reactions and the presence of oxygen which oxidizes ketones and alcohols is confirmed by Figure 5 which indicates that the oxygen level decreases as a function of time in the beer in parallel with other parameters such as temperature.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 2 Related Work
The deep learning model using a feed-forward neural network shows an accuracy of 99% with a lower false alarm rate. As per the study presented by [16], traditional defense systems such as firewalls and IDSs suffer continuous database updates to detect threats. To create more reliable systems with better detection rates and fewer false alarms, intrusion detection systems need to take a new approach that makes use of machine learning models. This study solved the imbalanced class problem with 99% accuracy by using the synthetic minority oversampling technique. Classification algorithms play an essential part in assisting IDS in identifying various types of attacks, as demonstrated by a comparative analysis of the UNSW_NB15 dataset conducted by [17]. As per this study, the Random Forest classification model is more credible as compared to other classification problems with an accuracy of 97.49%. In [18], the performance of various classification machine learning models has been implemented using Apache Spark and compared with related research based on the UNSW_NB15 dataset. This study states the Random Forest model is more accurate and provides the highest accuracy compared to the Decision Tree and Naive Bayes. The experiment conducted by [19] discusses the importance of network intrusion detection systems in identifying cyber threats, and the various approaches used to develop such systems, including classical, hybrid, and ensemble methods. To attempt to increase the IDS system's accuracy, this study has proposed stacking machine learning models with Mutual Information Gain and Extra Tree Classifier feature selection techniques. Tested on the UNSW-NB15 dataset, this model yields an accuracy of 96.24% and performs better than any other recent competing model. The classification and regression trees (CART) model analysis by [20] shows the hybrid model performs well using effective feature selection with the help of the Random Forest algorithm to find the most important features combined with CART for the classification of different attack classes and shows the accuracy of 95.37%. In [21], the comparative analysis of the UNSW_NB15 dataset and CICIDS2017 has been carried out. This research work concludes the SVM model with the naive Bayes feature embedding method gives an accuracy of 98.92% on the UNSW_NB15 dataset. It has been demonstrated that the highest accuracy of the IDS on the UNSW_NB15 dataset is 98.92%. This accuracy needs more enhancement to reduce the probability of false detection which affects the overall accuracy of the system. Hence, this paper is trying to enhance the system performance to exceed that threshold of accuracy by using in-depth EDA, feature selection and supervised learning models.
# Sensor-Level Mosaic of Multistrip KOMPSAT-3 Level 1R Products
###### Abstract  
High-resolution satellite images such as KOMPSAT-3 data provide detailed geospatial information over interest areas that are evenly located in an inaccessible area. The high-resolution satellite cameras are designed with a long focal length and a narrow field of view to increase spatial resolution. Thus, images show relatively narrow swath widths (10-15 km) compared to dozens or hundreds of kilometers in mid-/low-resolution satellite data. Therefore, users often face obstacles to orthorectify and mosaic a bundle of delivered images to create a complete image map. With a single mosaicked image at the sensor level delivered only with radiometric correction, users can process and manage simplified data more efficiently. Thus, we propose sensor-level mosaicking to generate a seamless image product with geometric accuracy to meet mapping requirements. Among adjacent image data with some overlaps, one image is the reference, whereas the others are projected using the sensor model information with shuttle radar topography mission. In the overlapped area, the geometric discrepancy between the data is modeled in spline along the image line based on image matching with outlier removals. The new sensor model information for the mosaicked image is generated by extending that of the reference image. Three strips of KOMPSAT-3 data were tested for the experiment. The data showed that irregular image discrepancies between the adjacent data were observed along the image line. This indicated that the proposed method successfully identified and removed these discrepancies. Additionally, sensor modeling information of the resulted mosaic could be improved by using the averaging effects of input data.  
Keywords:KOMPSAT-3A; strip; sensor modeling; RPCs; mosaic; matching; discrepancy +
Footnote †: journal: Applied
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 5 Experimental Design and Results
### Experimental Results
#### 5.2.2 Trajectory Comparison Results
We additionally incorporated the M2 DGR dataset [30] crafted by Shanghai Jiao Tong University to complement the validation of nonstructured environments. The hardware we employed encompassed a Velodyne VLP-32C LiDAR, GNSS (Ublox M8T), and IMU (Handsfree A9, nine-axis), among others. The M2 DGR dataset contains a significant amount of forestry environments and irregular road surfaces (The bird's-eye view of the point cloud is shown in Figure 7). We aimed to leverage these nonstructured features to challenge the tested systems. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **UKF** & **LIO-SAM** & **LINS** & **LIF-M** \\ \hline Error/m & 0.55 & 0.92 & 0.75 & 0.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ATE translation error. Figure 5: Trajectory comparison diagram (zoom in on different stages to obtain local detail map of (**a–d**) diagram). Figure 6: Comparison of angle offset on a straight route. The experiments involved a challenging section of road, comprising approximately 300 s of forestry road and around 100 s of lakeside steep slope road. When selecting specific paths in nanostructured environments and comparing the rotational offsets between LIF-M and UKF (Figure 8), it is evident that LIF-M exhibits superior stability with smoother variation curves, especially in conditions with dense foliage and reflective surfaces on lakeshores. By constructing a three-dimensional visual representation of the test path (Figure 9), it is evident that LIF-M outperforms UKF in terms of system stability and algorithm accuracy in unstructured environments. This highlights how the utilization of the Lie group space enhances the robot's perception capabilities in three-dimensional environments
# Rain Removal of Single Image Based on Directional Gradient Priors
###### Abstract  
Images taken on rainy days often lose a significant amount of detailed information owing to the coverage of rain streaks, which interfere with the recognition and detection of the intelligent vision systems. It is, therefore, extremely important to recover clean rain-free images from the rain images. In this paper, we propose a rain removal method based on directional gradient priors, which aims to retain the structural information of the original rain image to the greatest extent possible while removing the rain streaks. First, to solve the problem of residual rain streaks, on the basis of the sparse convolutional coding model, two directional gradient regularization terms are proposed to constrain the direction information of the rain stripe. Then, for the rain layer coding in the directional gradient prior terms, a multi-scale dictionary is designed for convolutional sparse coding to detect rain stripes of different widths. Finally, to obtain a more accurate solution, the alternating direction method of multipliers (ADMM) is used to update the multi-scale dictionary and coding coefficients alternately to obtain a rainless image with rich details. Finally, experiments verify that the proposed algorithm achieves good results both subjectively and objectively.  
rain removal; directional gradient prior; convolutional sparse coding; multi-scale dictionary Article  
Rain Removal of Single Image Based on Directional Gradient Priors  
[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]
## 2 Methodology
### Sample Analysis (Chemical, Gravimetric, and Carbonaceous Aerosols)  
After sample collection, the filter papers were kept in exact environmental conditions at a relative humidity of 30-40% and temperature of 20-23 \({}^{\circ}\)C for 24 h as per the US-EPA standard. Before mass analysis, the filter paper with fine and coarse particulates samples was equilibrated for 24 h in silica gel desicators to abolish the effect of humidity and to attain accurate particulate matter measurements. The PM\({}_{2.5}\) and PM\({}_{10}\) masses of each sample were determined gravimetrically by deducting the initial average mass of the blank filter from the final average mass of the sampled filter. Gravimetric analysis is the determination of particulate concentration based on weight difference. Individual filters (Teflon(r), 46.2 mm) were weighed on an electronic micro-balance pre and post field sampling. Particulate matter <2.5 um was collected from ambient air on the filters throughout the sample duration of 24 h. The net variances between pre- and post-sampling filter weights were used to estimate the mass concentration in the ambient air of the city. After post weighing, filters can be stored for a minimum of one year. Using the post-sample and pre-sample filter weights, the total filter mass gain (PM\({}_{2.5}\)) and the concentration of PM\({}_{10}\) were respectively calculated from Equation (1) and Equation (2):  
\[\mathrm{PM}_{2.5}=(\mathrm{M}_{\mathrm{Post}}-\mathrm{M}_{\mathrm{Pre}})(10^{3} )/\mathrm{M}_{\mathrm{Pre}} \tag{1}\]  
\[\mathrm{PM}10=(\mathrm{Wf}-\mathrm{Wi})(106)/\mathrm{V} \tag{2}\]  
Here PM\({}_{2.5}\) is the total mass gain in ug, M\({}_{\mathrm{Post}}\) is the post sample filter weight in mg, M\({}_{\mathrm{Pre}}\) is the pre-sample filter weight in ug, \(\mathrm{W}_{i}\) is the filter paper weight, \(\mathrm{W}_{i}\) is the initial mass of filter paper, and V is the total air sampled in m\({}^{3}\). In the current study, elemental carbon was determined by a two-step  
Figure 1: Study sites within the Faisalabad city and location of the city within the political map of Pakistan.  
combustion method described by [20]. Filters were heated for 2 h at 340 \({}^{\circ}\)C in an oxygen atmosphere to remove organic carbon (OC). The calibration procedure was done using tartaric acid dyed in aluminum foil. While, total carbon was determined by a combustion method, where all material on the filter is combusted in pure oxygen at 1000 \({}^{\circ}\)C and the resulting CO\({}_{2}\) is measured by non-dispersive IR photometry (NDIR, Maihak) [21].
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 4 Experimental Studies
### Utility Evaluation of the Proposed Synthetic Datasets
#### 4.2.1 Realistic Appearance  
The general impression of our synthetic fingerprints can be taken from Section 4.1.3 and Figure 8. Here, we compare the distributions of the NFIQ2 scores for the compiled datasets. Figure 9 depicts the probability density functions of 40,000 samples included  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multicolumn{3}{c}{**Model Snapshot**} & \multicolumn{3}{c}{**Anguli Fingerprints**} & \multicolumn{3}{c}{**URU Fingerprints**} \\ \cline{3-10} TrainDB & Enc & Norm & Epochs & **0.1\%** & **0.01\%** & **0.001\%** & **0.1\%** & **0.01\%** & **0.001\%** \\ \hline \multirow{3}{*}{aug39k} & \multirow{3}{*}{DL} & \multirow{3}{*}{IN} & 15 & **100.00** & **100.00** & 99.77 & 87.84 & 82.61 & 76.47 \\  & & & 30 & **100.00** & 99.77 & 99.20 & 83.29 & 75.11 & 65.90 \\  & & & 55 & 99.43 & 98.52 & 97.50 & 79.09 & 71.81 & 59.31 \\ \hline \multirow{3}{*}{aug39k} & \multirow{3}{*}{PM} & \multirow{3}{*}{IN} & 15 & **100.00** & **100.00** & **100.00** & **95.45** & **95.00** & **93.52** \\  & & & 30 & **100.00** & **100.00** & **100.00** & 95.11 & 94.31 & 93.29 \\  & & & 55 & 99.88 & 99.43 & 98.86 & 93.52 & 91.36 & 88.86 \\ \hline aug39k & \multirow{3}{*}{DL} & \multirow{3}{*}{BN} & 60+60 & 97.95 & 94.09 & 85.34 & 60.34 & 44.66 & 26.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fingerprint reconstruction success (in %); DL—directed line encoding, PM—pointing minutiae encoding; IN—instance normalization, and BN—batch normalization.  
in each of the **AMSL SynFP P2P v1** and **AMSL SynFP P2P v2** datasets. We also plot the distribution of the original 4000 Anguli fingerprints as a reference.  
Figure 8: Results of fingerprint reconstruction. Minutiae are taken from Anguli (**top**) and URU (**bottom**) fingerprints. Note that for Anguli, the reconstruction examples are with **aug39k_DL_BN_60+60 ep** and for URU with **aug39k_DL_IN_15 ep**.  
In comparison to the NFIQ2 scores of Anguli fingerprints, which have a narrow symmetric distribution around the mean of approx. 65, the distributions of synthetic datasets are negatively skewed and wider spread, having significantly higher standard deviations. The mean value of the **AMSL SynFP P2P v1** is approximately 60, which is lower than that of Anguli, and the mean value of the **AMSL SynFP P2P v2** is approximately 75, which is higher than that of Anguli. The NFIQ2 scores validate that the **AMSL SynFP P2P v1** dataset includes a small portion of fingerprints that might be challenging for minutiae extractors, leading to verification errors. In contrast, almost all of the fingerprints in the **AMSL SynFP P2P v1** dataset are of high utility, which is useful for checking that an advanced fingerprint matching algorithm produces almost no verification errors with this dataset.
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 2 Data and Methods
### Method of Comparison  
The root mean square difference (RMSD),  
\[R_{d}=R_{d}(X,Y)=\langle(Y-X)^{2}\rangle^{1/2}, \tag{2}\]  
correlation coefficient (\(r_{c}\)), and scatter index (SI) were used as the skill metrics for the comparisons, where \(X\) is the reference value, \(Y\) is the evaluated value, and \(\langle\ldots\rangle\) denotes an average. The correlation coefficient and SI were computed as  
\[r_{c}(X,Y)=\frac{\langle(X-\langle X\rangle)(Y-\langle Y\rangle)\rangle}{ \langle(X-\langle X\rangle)^{2}\rangle^{\frac{1}{2}}\langle(Y-\langle Y \rangle)^{2}\rangle^{\frac{1}{2}}}, \tag{3}\]  
and  
\[SI=SI(X,Y)=\frac{1}{\langle X\rangle}\langle[(Y-\langle Y\rangle)-(X-\langle X \rangle)]^{2}\rangle^{\frac{1}{2}}. \tag{4}\]  
The statistical significance of the estimated skill metrics was tested. The bootstrap method, e.g., References [24; 25], was used for verification, which is described in Appendix B.
## 3 Methodology
### The TOPSIS Algorithm
#### 3.1.1 Input of the Decision Matrix  
The decision matrix D consists of \(m\) rows representing the alternatives among the LEO satellites of interest, and \(n\) columns representing the criteria used in this study: upload speed, download speed, ping, and packet loss. An element \(a_{ij}\) of the matrix represents the value of the criterion \(j\) for the alternative \(i\)[14; 15; 16; 17].  
\[D=\begin{bmatrix}a_{11}&a_{12}&...&a_{1n}\\ a_{21}&a_{22}&...&a_{2n}\\ a_{31}&a_{32}&...&a_{3n}\\.&.&.&.\\.&.&.&.\\ a_{m1}&a_{m2}&...&a_{mn}\end{bmatrix} \tag{1}\]
## 2 Materials, Methods, and Discussion
### Test Site  
The Doncaster area (see Figure 4) has been selected to validate the simulated distortion maps and the CLC2012 map and inspect the influence of an increasing number of SAR images on the LOS velocities. By using the ISBAS approach, a multithreaded solution for the Punnet software was adopted where 28 threads enhance the process model with, parallel, flows of execution within a process.  
Starting from the first available acquisition, on 12-3-2015, an initial stack of 20 Sentinel-1A scenes in ascending mode along satellite track 30 has been considered and then gradually incremented by adding the five temporally successive Sentinel-1A and also -1B images in order to evaluate the effect of the number of SAR images on the standard deviation of LOS velocities (\(\sigma\)). Therefore, the 20 images stack encompasses the time span from the first acquisition until 29-5-2016, the 25 images stack the time span until 9-8-2016, the 30 images stack the time span until 2-10-2016, the 35 images stack the time span until 1-11-2016 and the 40 images stack the time span until 1-12-2016. In all the processing the 18-1-2016 acquisition has been set as the master, the scenes were multi-looked by a factor of 5 \(\times\) 22 in azimuth and range, respectively, and interferograms were generated within a maximum baseline of 200 m and one year in space and time, respectively. The derivation of linear velocities for each coherent point (with coherence above 0.25) was then performed using a reference point at the HOOB GNSS station (latitude: 53.4829\({}^{\circ}\) N; longitude: \(-\)1.3797\({}^{\circ}\) E) located northeast of Doncaster [42]. Coherent pixels cover 80% of the 1750 km\({}^{2}\) extended area with an average density of 51 ISBAS/km\({}^{2}\) (Figure 10). With an average RI-ascending of 0.57, shadowing and layover are not present, while foreshortening, with an extension of 1517.8 km\({}^{2}\), represents 87% of the area.  
The increased number of interferograms for each successive processing led to highlight the two main areas of subsidence north of Doncaster, namely Kirkby and Hensall, through the reduction of tropospheric noise and the consequent decrease in \(\sigma\) which assumes a progressively lower values compared to the average LOS velocity. The ISBAS standard deviation ranges from 5.9 mm/year with 105 interferograms which is 58 times that of the average LOS velocity, to 1.9 with 741 interferograms, which represent less than the average LOS velocity (Figure 11). The potential cause of deformation affecting those sites cannot be related to groundwater extraction from the Sherwood Sandstone Group whose depth here is usually \(\leq\)5 m here, due to the stable piezometric levels of the Carlton Hanger Lane [43]. In this perspective, further research should focus on analyzing the 3D displacement vector and InSAR time series to identify a possible link to the former underground mining activities in the area.  
Figure 10: Sentinel-1A LOS velocities for the Doncaster area with 15 (a), 20 (b), 25 (c), 30 (d), 35 (e) and 40 (f) Sentinel-1 images.