esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 2 Materials and Methods
### Methodology and Modelling
#### 2.1.4 Marine Debris and Seawater Model  
In the ocean, natural and anthropogenic materials tend to be aggregated together and to form patches of mixed objects, including macro-plastics [20]. Once aggregated into sufficiently large patches of varying shapes and sizes, the detection from satellites becomes possible.  
In our model, we consider patches made out of marine debris material. To generate the patches, we take into account the following:  
* The shape: in this proof of concept, the patch can either be a rectangle or a circle. These simple shapes are consistent with the targets used by [10].
* The size: if it is a rectangle, two dimensions are required for the length and the width whereas if it is a circle, only the radius is required.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Band**} & **Central Wavelength** & **Spatial Resolution** & **Nyquist Frequency** & **Cut-Off Frequency** & **Ratio** \\  & **(nm)** & **(GSD in m)** & \(f_{n}\) **(m\({}^{-1}\))** & \(f_{e}\) **(m\({}^{-1}\))** & \(f_{e}\) \\ \hline B01 & 443 & 60 & 0.008 & 0.43 & 53.75 \\ B02 & 490 & 10 & 0.05 & 0.39 & 7.8  
* The type of material: can be specified among a list of predefined material types. Based on the material, the corresponding reflectance values are loaded from a database of spectral reflectance values.
* The fraction of material: a number between 0 and 1 which defines the proportion of material contained in the patch. If the value is 0, there is only seawater and if the value is 1, the patch is 100% filled by the material. This parameter is used as an attenuation factor to model patches where the material does not completely cover the water (as a cover percentage) or to model other types of attenuation such as biofouling. Furthermore, it can be used to weaken the amplitude of the spectral reflectance that are measured in conditions too far from reality. Within a patch, the same attenuation is applied to each pixel.
* The rotation: a number in the \([-45^{\circ},45^{\circ}]\) range that defines the rotation of the patch. In real conditions, the material patch might not be aligned with the pixels. This is only applied when the shape is a rectangle.
* The bands: the interval of wavelengths of the bands considered for the satellite.  
As for seawater, it typically has a low reflectance, except in cases of sun glint (due to specular effects). As we do not take into account geometrical and lighting parameters in this first proof of concept, we chose to model all of the seawater pixels the same way by loading a default seawater spectral signature (see Section 2.2.1). This simplistic model does not take into account particularities such as turbid water or specular conditions.
# Algorithms for Drug Sensitivity Prediction
## 2 Data Characterizations
A dose response curve for each cell line and a specific drug is generated by observing the cell viability at different drug concentrations to fit a curve through the observations as shown in Figure 2. A dose-response curve is often approximated by a sigmoidal, linear or a semi-log curve. An example of sigmoidal curve generation is shown in Equation (1) [23]. \[y=A_{inf}+\frac{A_{0}-A_{inf}}{1+(\frac{x}{EC_{50}})^{h}} \tag{1}\]  
where \(A_{0}\) and \(A_{inf}\) represent the minimum and maximum reduction in cell viability, \(EC_{50}\) represents the drug concentration required to reach 50% of \(A_{inf}+A_{0}\) (average of the minimum and maximum cell viability attainable by the drug) and \(h\) is the Hill slope. Rather than using the entire cell response curve as the measure of sensitivity, a specific feature of the curve such as AUC (Area Under the dose response Curve) or \(IC_{50}\) (drug concentration required to reduce cell viability to 50% of initial response) is used to represent sensitivity. The IC\({}_{50}\)'s are usually converted to sensitivity values between zero and one using a logarithmic mapping function, such as \(y=1-\frac{\log\left(IC_{50}\right)}{\log\left(MaxDose\right)}\)[31; 32]. Each feature of the dose response curve can produce its own set of concerns. For instance, for responses where 50% cell viability cannot be reached, \(IC_{50}\) is often equated to \(dose_{max}\), which does not capture the exact behavior of the dose response curve. AUC can be problematic when low doses are used and an initial increase in cell viability is observed, which will result in negative portions for the AUC.
## 2 Materials and Methods
### Immunohistochemistry Localisation of GLO1 and Argyrimidine in the Reproductive Tract Organs  
Ovarian, oviductal and uterine tissues were prepared as described in [PERSON] et al. [28]. Endogenous peroxididases were inhibited by incubating the slides with 3% H\({}_{2}\)O\({}_{2}\) in Methanol for 25 min. After blocking with 10% goat serum for one hour, the slides were incubated with mouse monoclonal antibody against GLO1 (1:200, #133214, Santa Cruz, Santa Cruz, CA, USA) or argpyrimidine (1:100, Biologo, Kiel, Germany) diluted in 1% (wt/vol) BSA/PBS at 4 \({}^{\circ}\)C overnight. For detection, the secondary antibody Dako EnVision + System-HRP labelled Polymer anti-mouse (1:1 in PBS; Dako, Jena, Germany) and diaminobenzzidine (DAB; WAK-Chemie Medikal, Steinbach, Germany) were used. As negative control, the control reaction of the HRP-conjugated secondary goat anti-mouse IgG without the primary antibody was used. The DAB reaction was stopped after 4-6 min. Nuclei were counterstained blue with haematoxylin for 5 min. All steps were performed within the same experiment, examined microscopically during the same session, using an identical microscope (Leica DMi8, Wetzlar, Germany) and camera settings. Description of immunohistochemical evaluation is provided in the supplemental (Figure S4). The software Leica Application Suite X was used for image acquisition and editing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antigen** & **Name of Antibody** & \begin{tabular}{c} **Manufacture/Company** \\ **Catalogue NO.** \\ \end{tabular} & **Type** & **Dilution** \\ \hline GLO1 & GLO1 (D-5) & santa cruz, sc-133214 & MM & 1:50 \\ \hline Vinculin & Vinculin (7F9) & santa cruz, sc-73614 & MM & 1:300 \\ \hline \multirow{3}{*}{Mouse IgG} & \
\begin{tabular}{c} Peroxidase-conjugated \\ AffiniPure Donkey \\ anti-mouse IgG (H + L) \\ \end{tabular} & Jackson ImmunoResearch, 715-035-150 & DP & 1:300 \\ \hline \multirow{2}{*}{Mouse IgG} &
\begin{tabular}{c} Secondary HRP Conjugate \\ Goat anti-mouse IgG (H + L) \\ \end{tabular} & ProteinSimple, 042-205 & MP & undiluted \\ \hline \hline \end{tabular} MM: mouse monoclonal; DP: donkey polyclonal, MP: mouse polyclonal.  
\end{table}
Table 1: List of primary and secondary antibodies used for the Simple Western(tm) method.
## 4 Application in Tunnel Smooth Blasting
[58; 59] proposed a quantitative classification of rock mass (Q-system) as a rock tunneling quality index, on which design and support recommendations for underground excavations are based. The two systems mentioned above are the most influential classification systems proposed to date. Additionally, some other classifications have been developed which are widely adopted in different fields, such as the New Austrian tunneling method (NATM), size-strength classification, International Society for Rock Mechanics (ISRM) classification, geological strength index (GSI), and the BQ method used in China [54; 55; 56; 57; 58; 59; 60]. According to the rock mass classification systems of the corresponding standard [60; 61], the bedded structure tunnel investigated in the present study has a surrounding rock ranking of III.
# Image Dehazing Enhancement Strategy Based on Polarization Detection of Space Targets
###### Abstract  
In view of the fact that the technology of polarization detection performs better at identifying targets through clouds and fog, the recognition ability of the space target detection system under haze conditions will be improved by applying the technology. However, due to the low ambient brightness and limited target radiation information during space target detection, the polarization information of space target is seriously lost, and the advantages of polarization detection technology in identifying targets through clouds and fog cannot be effectively exerted under the condition of haze detection. In order to solve the above problem, a dehazing enhancement strategy specifically applied to polarization images of space targets is proposed. Firstly, a hybrid multi-channel interpolation method based on regional correlation analysis is proposed to improve the calculation accuracy of polarization information during preprocessing. Secondly, an image processing method based on full polarization information inversion is proposed to obtain the degree of polarization of the image after inversion and the intensity of the image after dehazing. Finally, the image fusion method based on discrete cosine transform is used to obtain the dehazing polarization fusion enhancement image. The effectiveness of the proposed image processing strategy is verified by carrying out simulated and real space target detection experiments. Compared with other methods, by using the proposed image processing strategy, the quality of the polarization images of space targets obtained under the haze condition is significantly improved. Our research results have important practical implications for promoting the wide application of polarization detection technology in the field of space target detection.  
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Footnote †: journal: applied sciences  
0000-0002-22000-0000-0000-0000-0000-0000-0000-0000-0000-00  
detection plays an important role in improving the target recognition ability, breaking through the detection restrictions of haze conditions.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 3 Experimental Results
### Experimental Results for Scanning Operating Mode
#### 3.2.1 The Type-I Experiment  
As shown in Figure 9, the type-I experiment was carried out in an office building, and two Golay-coded radars were placed on the same side of the 1 cm gypsum board, 1.8 m apart. The tested object was placed at the different distances from 5 to 51 m, with a 2 m interval.  
Figure 8: Experimental detection and classification result for stationary operating mode.  
Figure 6: Measurement scenes for quasi-static human being detection in stationary operating mode.  
Figure 7: Experimental results for stationary operating mode. (**a**) The preprocessing slow-time range matrix. (**b**) The resultant frequency range matrix.  
Considering the uncontrollable breathing movement of personnel, we selected the metal plate beacon as the tested object to ensure the data consistency during the collection of radar echoes. To simulate the chest movement of the human body, the metal plate beacon was controlled by a screw stepper motor to drive the metal plate to vibrate periodically, as shown in Figure 10. In the type-I experiment, the metal plate beacon's vibrating frequency was set as 0.28 Hz, and the amplitude was set as 1 cm.  
Both the proposed \"scanning operating mode\" and the \"FFT-based respiration detection method [29]\" were applied to the collected radar echoes. The corresponding measurement results are shown in Table 3. The detection errors of the experiment for No. 1 and No. 2 radars are shown in Figure 11a,b, respectively. The blue error bars represent the detection errors of single-radar detection results via the \"FFT-based respiration detection  
Figure 10: The metal plate beacon.  
Figure 9: The type-I experimental setup.  
method\". The red error bars represent the detection errors via the \"scanning operating mode\". In addition, the blue '\(\times\)' indicates the failure detection case.
# A Method to Construct an Indoor Air Pollution Monitoring System Based on a Wireless Sensor Network
## 3 Coverage Hole Detection and Healing  
The previous phase of work allowed us to identify the positions of the sensors used to build the air pollution monitoring wireless network and ensure full coverage of the RoI. After a period of time (one hour), it is necessary to ascertain if any of the sensors change its position or leave the RoI.  
Updating the sensor locations allow to check the positions of the deployed sensors. If they remain their positions we keep the same deployed sensor for RoI coverage otherwise we move to the next phase of the work which is the holes detection and healing process. The second phase of project will be divided into two parts: The first is holes detection and coverage based on the VD, and the next is to repair the holes using the PiP algorithm.
## 3 Results and Discussion
### Stakeholder's Influence in Implementing CE Principles
#### 3.4.6 Sub-Contractor and Demolition Contractor  
The sub-contractor and demolition contractor were both seen as having limited influence in implementing CE principles, tying for the lowest ranking among all stakeholders. Respondents view these roles as more focused on carrying out specific tasks, rather than being involved in the bigger strategic decisions related to CE. For sub-contractors, their job is typically to handle parts of the project as directed by the main contractor. Because they are focused on specialized tasks, their ability to influence broader CE initiatives is seen as minimal. They are carrying out the work rather than shaping the sustainability vision. Similarly, the demolition contractor' role comes at the end of the building's life, mainly tasked with deconstructing or demolishing structures. Even though this phase can be important for recycling and reusing materials which are key to CE principles, respondents feel that the demolition contractor's influence is limited. As by the time they step in, most of the critical CE decisions have already been made during the design phase. Their role is more about executing the end-of-life stage, rather than influencing the project's CE approach from the start. However, A1 believed that demolition contractors can influence in deciding which materials can be reused or recycled.
## 3 Methodology
### Deep-SORT  
Deep-SORT [11] is an improvement of the SORT algorithm, integrating appearance information of objects to enhance associations. Data association integrates an additional appearance metric based on pre-trained CNNs allowing re-identification of tracks, after a long period of occlusion. The KF Estimation and the Track management modules are similar to the corresponding SORT modules. An overview of the method is presented in Figure 3.  
As in SORT, the association of detected bounding boxes to tracks is solved by the Hungarian algorithm, using a two-part matching cascade. In the first part, the Deep-SORT method uses motion and appearance metrics to associate valid tracks. The second part uses the same data association strategy as in SORT to associate unmatched and tentative tracks (recently created) with unmatched detections. Motion information is incorporated by the (squared) Mahalanobis distance between predicted states and detections. In addition to the metric computed with the Mahalanobis distance, a second metric based on the smallest cosine distance measures the distance between each track and each measurement appearance features. The appearance features are computed by a pre-trained CNN model. The CNN in the Deep-SORT method was trained on a large-scale person re-identification dataset [41] using deep cosine metric learning [42]. A pre-trained model is provided by the authors in their repository ([[https://github.com/nwojke/deep_SORT](https://github.com/nwojke/deep_SORT)]([https://github.com/nwojke/deep_SORT](https://github.com/nwojke/deep_SORT)) (accessed on 15 October 2021)).
## 3 Materials and Methods
### Data Preprocessing  
Due to the use of the Weizmann database [30] it is assumed that for each video sequence there is a set of binary images, and these images are foreground masks extracted from video frames. One sequence represents one action type and one image contains one silhouette. Frames in which an object is occluded or too close to the edge of the video frame are removed. The direction of the action is checked and, if necessary, the video frames are flipped so that all objects in the sequence move from left to right. Then, each silhouette is replaced with its convex hull, which reduces the impact of some artefacts (e.g., additional pixels) introduced during background subtraction (see Figure 1 for examples). It is indicated in [29] that the use of convex hulls improves classification accuracy.  
Before the actual classification, the dataset is divided into two subsets based on the centroid locations on the consecutive frames. This is related to action characteristics--some of actions are performed by a person standing in place (short trajectory) and the rest contain a person who changes location in every frame (long trajectory). Examples are given in Figure 2. This procedure can be called a coarse classification. It influences subsequent steps of the approach which are performed separately in each subgroup. Therefore, there is a possibility of selecting different features and parameters better suited to the specific action types.  
Figure 1: Sample silhouettes from the [PERSON] database [30] and the corresponding convex hulls.
## 4 Materials and Methods
### Empirical Evaluation for the Measure Phase  
Once there was an implementation of at least a prototype of the platform (beyond mock-ups), a three-part questionnaire was created to evaluate the capacity of the platform as a tool for the improvement of the KM process in Chilean SMEs. This test evaluated mainly usability and usefulness factors such as timely access to information, structure, and ease of the KM process within the system, as well as consistency with everyday work.  
One part focused completely on the usability assessment, according to the SUS test, which defines 10 questions that are evaluated on a Likert scale of 1 to 5 [78]. A SUS score between 70 and 80 is considered \"good\", between 80 and 90 is considered \"excellent\", and over 90 is considered the \"best imaginable\" [79]. According to the literature, obtaining a SUS score at each step in an iterative development process allows the experimenter to compare each iteration using a standard tool [80]. Besides, considering the issue of the lack of time present in Chilean construction SMEs as discussed earlier, SUS presents a powerful advantage: SUS makes it possible to get a measure of the perceived usability of a system with a small sample (a total of 8-12 users), resulting in a fairly precise assessment of how people see the system or product [81].  
To complement the SUS test, the second part of the test took into account an overall assessment of the platform, considering elements defined in the ISO/IEC 25010: 2011 quality software standard [82], as well as other elements considered relevant to the project. This part consisted of 11 affirmations about the platform as show in Table 3 which had to be ranked from 1 (totally disagree--worst) to 5 (totally agree--best) to know if the respondent agreed to these statements or not. Considering theadditive nature of this ranking, each evaluator could assign a minimum score of 11 (each affirmation evaluated as 1) and a maximum score of 55 to the system. Considering the ratio between the total score (TS) and the number of affirmations (QA), one can interpret each professional's evaluation using a scale from 1 to 5 [80].  
The last part consisted of open questions, in order to identify in greater detail the perceptions and opinions of users according to their professional experience in the construction sector.
## 6 Simulation and Analysis
### Analysis of Simulation Results
#### 6.2.4 Time Synchronization Errors Test
When the ABg fault occurs at the end (95% from B\({}_{5}\)) of the feeder B\({}_{5}\)B\({}_{6}\) or the ABg fault with 30 \(\Omega\) fault resistance occurs in the middle (50% from B\({}_{5}\)) of the line, TCDP will refuse to trip, while ACDP can still trip accurately and has high sensitivity. Compared with TCDP, the ACDP proposed in this paper has superior performance in resisting time synchronization errors.
# Terahertz Time-Domain Spectroscopy of Glioma Patient Blood Plasma: Diagnosis and Treatment
## 2 Materials and Methods
### THz Spectroscopy  
The blood plasma of patients with glioma, healthy donors, and patients with SCD was studied using the THz time-domain spectrometer T-Spec 800 Teravil (EKSPLA, Vilnius, Lithuania) [55]. The spectrometer has a dynamic range \(>\) 90 dB at 0.4 THz and with two delay lines, fast and slow, provides real-time data acquisition with 10 spectra/s speed and spectral resolution up to 1 GHz. Plasma samples were investigated in transmission mode (Figure 1) using the plastic cuvettes [41; 56]. These cuvettes had sufficient transparency in the THz spectral range [41], and 0.5 mm thick inner gap cuvettes printed on a Designer X PRO 3D printer (PICASO 3D, Russia) made of Watson plastic (BestFilament, Tomsk, Russia) were used [41].  
An empty cuvette was placed in the focus area of the THz radiation between the parabolic mirrors of the THz-TDS spectrometer, as shown in Figure 1, and a reference signal was recorded. Without changing the position of the cuvette, samples of pre-thawed blood plasma with a volume of 50 \(\mu\)L were placed in the cuvette using an automatic dispenser.  
Spectral scanning of the sample was carried out along six spatial points of the cuvette with a step of 0.1 mm vertically and horizontally in the spectral range of 0.2-1.6 THz. Time averaging (over 256 spectra) was performed for each spatial point of the 2D scan. The conversion of the signal from the time domain to the frequency domain was carried out using the Terravil TRS-16 software (TeraVil Ltd., Vilnius, Lithuania). All measurements were carried out at a temperature of 21 \(\pm\) 1 \({}^{\circ}\)C.  
Figure 1: Scheme of the cuvette position in the THz time-domain spectrometer. The gray color shows the path of the THz radiation beam through the cuvette center.