esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Modular Software Architecture for Underwater Vehicles Using Systems Engineering
## 3 Modular Software Architecture Definition
### Class Structures  
Pionero500, as an exploration system, mainly benefits from modularity by increasing functions regarding data acquisition from its surrounding environment, thus making instrumentation fundamental for the ROV. This premise oriented the development of a class model that uses the concept of instrument as an abstraction, with functionality added by specification, while sharing some common members. Figure 6 shows a simplified version of the main class structure. An abstract class Instrument is at the top, with multiple children classes inheriting from it. As an abstract class, Instrument functionality is overridden, serving as a template for every child, providing them with essential methods that must be present in all instruments. These generic methods are:  
* Init: runs instrument initialization routines, usually for establishing communication with the main processor. It should be called at least once per operation of the vehicle.
* Stop: terminates the operation of the instrument as intended, according to its requirements. Frequent routines implemented by this method are serial buffer clearing and controlled power-off.
* Read: handles data exchange between the instrument and processing unit. Its main function is to accumulate and parse incoming data, but also can implement specific routines, such as requests and process responses.
* Compose: consolidates information in an standardized manner by using a type-defined data structure. The structure is the same for every instrument. This is necessary to facilitate data exchange between the instrument and the whole system.  
After inheriting Instrument abstract class, each instrument class implements specific functionality to the inherited methods, while preserving the parent's calling properties, allowing the main application to call overridden methods iteratively. This is a key aspect of a modular model, as the addition of modules only requires code development of its own implementation, with little to no intervention of main code.
## 3 Results
### Molecular Weight Distribution Analysis of EPSs  
The molecular weight distribution of the V-EPS and T-EPS was determined by HPSEC, as illustrated in Figure 3A,B, respectively. It can be seen that V-EPS consisted of three high molecular weight populations (Figure 3A). The main peak was eluted between 5.9 and \(<\)7.5 min covering the mass range between 47 \(\times\) 10\({}^{4}\) g/mol and 68 \(\times\) 10\({}^{4}\) g/mol, while the other peaks covered the range between 10 \(\times\) 10\({}^{4}\) g/mol and 0.9 \(\times\) 10\({}^{4}\) g/mol. The first three peaks represented 34%, 25%, and 29% of the total peak area. The low molecular weight compounds (RT 8.182 and 11.081 min) occupied a very small percentage of the total peak area (11%) and therefore a small percentage of EPS.  
Considering the T-EPS elution profile, there was no significant difference in the range of high molecular weight of V-EPS and T-EPS, which was illustrated as a similarity in elution pattern. This suggested the similarity in the structure and molecular weight. Three high molecular weight populations were recognized and eluted early before 7.0 min at 5.9, 6.0, and 6.2 min. Different peaks represented 27%, 26%, and 43% of the total peak area and  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **V-EPS** & **T-EPS** \\ \hline Yield, g EPS/100 g \({}^{1}\) (g EPS/L) \({}^{2}\) & 18.4 (4.0) & 20.7(4.5) \\ Total carbohydrates \({}^{3}\), \(w/w\)\% & 75.4 & 79.0 \\ Mannnose & 30 (37)\({}^{4}\) & 32 (36) \\ Glucose & 18 (22) & 22 (25) \\ Xylose & 13 (19) & 16 (21) \\ Galactose & 11 (14) & 11 (12) \\ Uronic acids & 7.0 (8) & 6.3 (7) \\ Rare sugars test & + & + \\ Acetyl content, \(w/w\)\% & n.f & n.f. \\ Protein, \(w/w\)\% (N \(\times\) 5.78) & 11.8 & 11.0 \\ \hline \hline \end{tabular}  
* per dry weight biomass; \({}^{2}\) to a liter of cultural liquid; \({}^{3}\) Mannnose equivalent; \({}^{4}\) Values in brackets represent the sugar composition in mol\(\times\); n.f.\(-\)not found.  
\end{table}
Table 1: Yield, monosaccharide composition, and protein content of EPSs.  
Figure 3: HPSEC elution pattern of EPS produced by: (**a**) _Vishniacczyma victoriae_; (**b**) _Tremellomycetes_ sp.  
cover the range between 68 \(\times\) 10\({}^{4}\) g/mol and 51 \(\times\) 10\({}^{4}\) g/mol. An additional small peak (<10,000 g/mol) was eluted later after the main peaks at RT 11.0 min, and it occupied only 4% of the total peak area.
# A Termination Criterion for Probabilistic Point Clouds Registration
## 4 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Sequence** & **0.75 Quantile** & **0.95 Quantile** & **0.75 Quantile** & **0.95 Quantile** \\  & **(Cost Drop)** & **(Cost Drop)** & **(100 Iterations)** & **(100 Iterations)** \\ \hline box\_met & 2.26 & 3.95 & 3.36 & 5.02 \\ hauptgebaude & 0.03 & 0.72 & 0.02 & 0.78 \\ pioneer\_slam3 & 0.38 & 0.77 & 0.18 & 0.78 \\ urban05 & 0.50 & 2.12 & 1.77 & 3.33 \\ gazebo\_winter & 0.03 & 0.23 & 0.03 & 0.05 \\ planetary\_map & 1.16 & 2.18 & 0.83 & 1.81 \\ long\_office\_household & 0.66 & 2.00 & 0.62 & 2.07 \\ plain & 0.50 & 0.94 & 0.20 & 1.00 \\ pioneer\_slam & 0.43 & 3.54 & 0.45 & 4.68 \\ stairs & 0.09 & 0.24 & 0.09 & 0.24 \\ gazebo\_summer & 0.20 & 0.65 & 0.13 & 1.02 \\ wood\_autumn & 0.03 & 0.27 & 0.03 & 0.04 \\ apartment & 0.29 & 1.30 & 0.27 & 2.02 \\ wood\_summer & 0.02 & 0.27 & 0.02 & 0.03 \\ p2 at\_met & 1.04 & 2.00 & 0.84 & 2.29 \\ \hline total & 0.44 & 1.79 & 0.47 & 2.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The 0.75 and 0.95 quantiles of the results of PPCR on a comprehensive benchmark [6] with two different termination criterion (100 iterations and the cost drop). \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Sequence** & **Cost Drop** & **N. of Iterations** & **100 Iterations** \\ \hline box\_met & 1.17 & 29.33 & 1.92 \\ hauptgebaude & 0.01 & 31.46 & 0.01 \\ pioneer\_slam3 & 0.19 & 24.29 & 0.08 \\ urban05 & 0.36 & 21.81 & 1.13 \\ gazebo\_winter & 0.02 & 29.97 & 0.02 \\ planetary\_map & 0.59 & 14.82 & 0.42 \\ long\_office\_household & 0.19 & 23.96 & 0.17 \\ plain & 0.26 & 19.32 & 0.06 \\ pioneer\_slam & 0.19 & 27.27 & 0.16 \\ stairs & 0.03 & 24.02 & 0.03 \\ gazebo\_summer & 0.06 & 25.11 & 0.04 \\ wood\_autumn & 0.02 & 29.34 & 0.02 \\ apartment & 0.07 & 23.04 & 0.06 \\ wood\_summer & 0.02 & 30.71 & 0.01 \\ p2 at\_met & 0.50 & 18.55 & 0.27 \\ \hline total & 0.12 & 18.55 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The median of the results of Probabilistic Point Clouds Registration (PPCR) on a comprehensive benchmark [6] with two different termination criterion: 100 iterations and the cost drop. For the cost drop we list also the number of iterations used to reach the solution (under the column _N. of iterations_).
## 4 Methods for Detecting of Microplastic Particles
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Key Parameters and Notes** & **Ref.** \\ \hline FTIR microscopy (\(\mu\)-FTIR) & Microscopic imaging, particle identification based on FTIR spectra. Determination of number, size, shape and material of particles. Problematic detection of particles smaller than 20 \(\mu\)m. & [22; 95] \\ Raman microscopy (\(\mu\)-Raman) & Microscopic imaging, particle identification based on Raman spectra. Determination of number, size, shape and material of particles. & [22; 30; 31; 32] \\ Autofluorescence measurements & Resolution (does not directly provide the number of particles) or fluorescence microscopy. Limited to well defined samples, relatively cheap method but low selectivity. & [89] \\ Fluorescence measurements & Dye binding to microplastic particles followed by microscopic observation. Increased specificity compared to autofluorescence measurements. & [90; 91; 92; 93; 94] \\ Optical photothermal infrared spectroscopy (O-PTIR) & IR-irradiated sample thermally expands, refractive index change detected by visible light. High spatial resolution below 1 \(\mu\)m. & [96; 97; 98] \\ Tunable mid-infrared quantum cascade laser spectroscopy (QCL-IR) & Tunable lasers (1800-950 cm\({}^{-1}\)). Method fast, scans larger area, lower resolution vs O-PTIR. & [96; 99] \\ Surface-enhanced Raman spectroscopy (SERS) & Sample placed on Klarite substrate and analyzed by Raman microscopy. High spatial resolution below 1 \(\mu\)m. & [100] \\ Pyrolysis gas chromatography-mass spectrometry (py-GC-MS) & Sample undergoes pyrolysis followed by gas chromatography separation and mass spectrometry identification. Determination of polymer type and total mass only; not particle number, size, or shape. & [101; 102] \\  & Gold nanoparticles linked with a synthetic peptide Biosensor with functionalized gold nanoparticles & exhibiting a high affinity for PET. Visual or instrumental, colorimetric detection. Theoretical concept has not yet been tested experimentally. & [104] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of methods used for microplastic detection, with particular focus on the detection of PET particles in bottled water.
# Working Face Stability of Box Shield Tunneling under Non-Uniform Support Pressure
## 2 Materials and Methods  
### Distribution Characteristics of Support Pressure in Box Tunnels  
As shown in Figure 1, due to its large section, the boring machine often uses multiple cutter heads and double-screw conveyors to improve the efficiency of excavation and soil removal. The gates of the conveyor are marked with yellow circles in Figure 1a-e. When the tunnel is driven with cutter heads rotating, the cut-down soil enters the chamber through the conveyor gates. A double-screw conveyor mounted behind the bulkhead then discharges the cut soil. (Figure 1f). Since the opening rate of the cutter disc is large, earth pressure in the chamber is highly possible to be fluctuating and unevenly distributed.  
Figure 1: Typical box tunneling machine with multiple cutter heads and double-screw conveyors.  
Based on the characteristics of the equipment, such as cutter and screw conveyors, the cut soil is conveyed out from the screw conveyor gates, which are at the bottom of the working face. As shown in Figure 2a, with the discharge of soil in the area near the outlet of the double-screw conveyors, the soil in the upper parts and the surrounding areas collapses. The discharge of the screw conveyors makes the air pressure in the central area small and unstable. Therefore, the central area is significantly impacted, while the neighboring two parts are almost unaffected. The residual soil on both sides serves as a stable support for the working face, and the support force is defined as \(S_{1}\). The air pressure in the middle area is low and unstable, forming a pressure fluctuation zone. The support force is defined as \(S_{2}\), which is smaller than \(S_{1}\).  
Therefore, the working face is non-uniformly supported, and the support pressure is large on both sides and small in the middle. As shown in Figure 2b, divide the working face into three parts, and simplify the central irregular area of the unstable support pressure into a rectangle (the width of which is \(B\), and the support force is denoted as \(S_{2}\)). The two adjacent areas of stable support pressure are also simplified to rectangles with support force \(S_{1}\).  
In Figure 3, a tunnel excavation of height \(D\) and width \(L\) under a cover depth \(C\) in homogenous soil is considered. The soil obeys the Mohr-Coulomb failure condition, defined by its effective cohesion (\(c^{\prime}\)), effective angle of internal friction (\(\varphi^{\prime}\)), and the total unit weight (\(\gamma\)). At the working face, the middle part with a width of \(B\) is the area of unstable support pressure. In other words, this part (II) is not subject to the full support force. However, the two parts of the working face on both sides (I and III) are entirely supported. The ground surcharge \(\sigma_{s}\) is not taken into account; thus, \(\sigma_{s}\) is assumed to be 0 kPa.  
Figure 3: Profile of box tunneling.  
Figure 2: Distribution characteristics of support pressure.
# Masked Convolutions Within Skip Connections for Video Anomaly Detection
## 3 Methodology
### MaskedConv3D  
Following the explanation provided in Section 3.1, the concept of a MaskedConv3D layer should be relatively straightforward. The fundamental idea remains the same, but instead of establishing a boundary akin to a ring, as in 2D, a MaskedConv3D layer constructs a boundary resembling a hollow sphere in three dimensions. Extending the unique approach from MaskedConv2D, our 3D variant continues to mask the convolutional kernel instead of the volumetric video data, further differentiating our technique from mainstream practices. Given that the size of a Conv3D kernel is depicted as a three-dimensional array, encompassing temporal, height, and width dimensions, MaskedConv3D layers nullify only a subset of the temporal layers.  
This approach utilises the information from previous and subsequent temporal frames to make educated guesses about the content of the current frame. To illustrate, let us assume a kernel size of \(3\times 3\times 3\), which consists of 27 weights, denoted by \(\{w_{t,i,j}|t,i,j\in\{0,..,2\}\}\). In this case, \(t,i,j\) are identifiers for the temporal dimension, rows, and columns of the kernel, respectively. Among these, only the weight \(w_{1,1,1}\) would be set to zero. This example can be seen in Figure 4.  
The key is that, by leaving central pixels zero-weighted, the layer is required to draw on the three-dimensional spatial and temporal context it has at its disposal in order to infer its value. This is analogous to the way the 2D version leverages spatial context, but with the additional complexity of the temporal dimension, making the 3D version potentially more powerful for tasks involving video or other 3D data.  
Figure 3: This figure demonstrates the concept of weight masking in the MaskedConv2D layer. A traditional Conv2D kernel is depicted, where the central pixel weight is surrounded by zero-value weights. The zero mask creates a “ring” boundary around the central pixel, indicating that the MaskedConv2D layer infers the value of the central area based on the surrounding pixels. This spatial inference contributes to the detection of anomalies by preventing the reconstruction of anomalous patterns and enhancing the discernment between normal and anomalous data.
# Study on the Position Deviation between the Iron Roughneck's Spin-Rollers and the Drilling Tool
## 3 Results and Discussion
### Experimental Results of the Spinner Mechanism with Double Follow-Up Rollers  
The experimental results of the spinner mechanism with double follow-up rollers are shown in Table 5. The data in the table are the results of several consecutive tests after the position of the spinner mechanism has been set. It is evident from the test data that the position deviation of the drill pipe in the \(x\) direction is zero in most cases and 1.76 mm in a few cases, which is very small. Considering the size and distribution of the deviations, it can be surmised that the drill pipe basically does not deviate in the \(x\) direction, indicating that the improved roller structure limits the position deviation in the \(x\) direction. It is evident from the table that the inclination angle in the \(y\) direction ranges from 89.80\({}^{\circ}\) to 89.90\({}^{\circ}\), the deviation angle from the vertical axis is 0.10\({}^{\circ}\) to 0.20\({}^{\circ}\), and the dimensional deviation of the drill pipe in the \(y\) direction is between 3.52 mm and 7.04 mm at the position of the spin-rollers. The operation results show that under these conditions, the spinner mechanism works relatively stably, and the spin action is completed smoothly.  
For the spinner mechanism with double follow-up rollers, there is no deflection of the spin-rollers since the position deviation in the \(x\) direction is limited. Whereas the deviation in the \(y\) direction is small, and the direction of the deviation is consistent with the floating direction of the mechanism. The deviation can be easily corrected in the process of spin, which ensures the centering performance of the drill pipe and its working stability.
## 7 Comparison with Existing Approaches  
We now present ReSVM's improvement over the results presented in the existing literature. Table 7 shows that ReSVM outperforms Chwan, Mase, Tamas, and Hssayeni. It uses a combination of deep features of ResNet-50 and SVM that performs well on datasets containing high intraclass variations such as SFDDD.  
ReSVM outperforms Eraqi, Ali, Dahmane2012, and Dahmane2015 in terms of percentage accuracy on the BU dataset as can be seen in Table 8. ReSVM uses an SVM that scales  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ds** & **SVM** & **ID3** & **MLP** & **AB** & **NB** & **RF** & **k-NN** \\ \hline D1 & 95.50 & 77.50 & 84.00 & 33.00 & 73.00 & 91.50 & 91.00 \\ D2 & 90.46 & 75.60 & 59.78 & 48.67 & 57.14 & 86.70 & 87.30 \\ D3 & 93.44 & 73.77 & 72.13 & 62.29 & 75.40 & 88.50 & 86.88 \\ D4 & 94.50 & 88.50 & 81.00 & 80.50 & 76.50 & 95.50 & 94.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of four datasets with state-of-the-art in terms of percentage accuracy in variants of proposed approach.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Parameters** \\ \hline Decision Tree & criterion = ’entropy’ \\  & in\_samples\_split = 2 \\  & random\_state = 0 \\  & splitter = ’best’ \\ Random Forest & parcriterion = ’entropy’ \\ n\_estimators = 10 \\  & random\_state = 0 \\ k-NN & leaf\_size = 30 \\  & metric = ’minkowski’ \\  & n\_neighbors = 5 \\  & p = 2 \\  & weights = ’uniform’ \\ AdaBoost & n\_estimators = 50 \\  & random\_state = None \\ MLP & activation = ’relu’ \\  & alph = 0.0001 \\  & max\_fun = 15,000 \\  & max\_iter = 50 \\  & random\_state = 0 \\  & solver = ’adam’ \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison using SFDDD dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **[PERSON] [48]** & **[PERSON] [49]** & **[PERSON] [50]** & **[PERSON] [47]** & **ReSVM** \\ \hline SFDDD & 89.90 & 92.70 & 95.00 & 85.00 & **95.50** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison using SFDDD dataset.  
relatively well to high-dimensional data and also reduces the risk of overfitting [60]. It can be observed that all the approaches undergo a degradation in their percentage accuracy in this dataset as compared to SFDDD. One reason for this degradation in performance is the lack of a large amount of training data [62].  
The dataset is smaller in size and has high intraclass variations due to the illumination factor of frames, glasses, eye gaze movements, talking to a passenger, picking up something on the dashboard, sleeping, etc.  
Similarly, ReSVM outperforms [PERSON], [PERSON], [PERSON], and [PERSON] as can be seen in Table 9.
# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 4 Empirical Study
### Data Sourcing and Processing
#### 4.2.3 Travel Distances and Service Threshold Distance  
Furthermore, we limited the service threshold distance of community facilities to 500 m, which is the service threshold distance of the community facilities set in the Chinese National Standard \"Planning and Design Standards for Urban Residential Areas\".
## 2 Materials and Methods
### Parametric Images from Radiofrequency Signals
#### 2.2.3 Attenuation Image  
QUS in soft tissue deals with various ultrasonic parameters estimated from reflected RF signals, including backscatterer size [25], distribution of integrated backscatters [26], and speed of sound [27]. Attenuation is one of the most significant features of soft tissues and provides beneficial diagnostic information related to the pathological status of the scanned tissue. Because attenuation in soft tissues generally demonstrates a linear frequency dependence [28,29], many estimation methods have been developed using this assumption through linear fitting techniques for the center frequency changes along the axial direction.  
For time-domain approaches, the measurement of zero crossing of the received RF signals [30] and the entropy difference between two adjacent segments of narrowband echo signals [31] were utilized to estimate the attenuation properties. In the frequency domain, spectral difference approaches [32,33] calculate the amplitude decay of the backscattered RF signals, while spectral shift approaches [34,35] estimate the downshift of the center frequency from a normalized power spectrum along with the propagation depth. To compensate for the diffraction of focused acoustic waves, the hybrid method was proposed that used the reference phantom to leverage the advantages of the spectral difference and spectral shift algorithms [36].  
In this study, attenuation images were estimated by downshifting the center frequencies of small regions along the propagation path. The entire RF signal is divided into small 2-D overlapping blocks to calculate the power spectrum. The window size should be large enough to satisfy the stationary assumption of the attenuation property and small enough to provide sufficient spatial resolution for the final attenuation image. In this experiment, the window size was set as 5 mm \(\times\) 5 mm and an overlap ratio of 50% was applied to estimate the attenuation image. Each windowed RF signal segment was gated by a Hanning window to diminish the spectral artifacts on both boundaries, and the short-time Fourier analysis [37] was applied to calculate the block power spectrum. The frequency smoothing method, where adjacent estimated frequencies were averaged using a moving average window, was also utilized to further reduce spectral noises in the block power spectrum.  
Because the reference signals for compensating the diffraction effects were not available in the dataset used, the finally estimated attenuation coefficients were not real attenuation coefficients. However, the estimated attenuation features provided meaningful information for the classification task because all data were estimated under the same conditions. Figure 3d shows the sample attenuation image generated from the RF signals.