esa-sceva/satcom-chunk-collection huggingface dataset:
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 3 FLATSIM Architecture
### Service Access  
Processed areas are selected through a series of call for research proposals, with an independent committee evaluating the scientific objectives, feasibility, and team skills, bearing in mind that the amount of square kilometer processed per year is limited. This call is currently dedicated to the French scientific community. Products are freely available for the French scientific community. The international community will access the final displacement rate maps (after thorough validation, possible partial reprocessing, tying up to geodetic reference frame, and a first data analysis by scientific teams) through peer-reviewed publications with a reference to the FLATSIM service.
## 4 Results and Discussion
### Pollution Source Identification of Monitored Variables  
Principal Component Analysis  
PCA was applied to the normalized data and was able to identify three principal components (PCs) using the [PERSON] criterion [66] based on loading higher than 0.5. The scree plot graphs are used widely to identify the number of PCs to be retained to understand the underlying data structure [26]. Based on the scree plot and the eigenvalues >1 criterion, three factors were chosen as principal factors. The variables with eigenvalues lower than 1 were removed due to their low significance [67]. In this study, the scree plot (Figure 4) shows the sorted eigenvalues from large to small as a function of the number of PCs. This figure shows a pronounced change in slope after the third eigenvalue; three components were retained (Table 9). After the third PC (Figure 4a,b), beginning with the upward curve, the remaining components were circumvented. It was used to classify the number of PCs to be retained in order to figure out the underlying data  
Figure 3: Box plot of the most discriminating parameters, BOD (mg/L), EC (\(\mu\)S/cm) and Na+ (mg/L) and Wilks’ lambda showing skewedness of discriminatory distribution toward high concentration. structure [25]. Consequently, a new set of data is obtained that may explain the variation of data set having fewer variables. Moreover, scree plots are used to visually evaluate which components or factors elucidate the maximum variability in the data. The PCA results, which include the loadings (participation of the original variable in the new one), are summarized in Table 9. The FA in LHW extracted three factors by retaining the PCs through varimax rotation that explained 72.5% of the total variance for the wet season. An eigenvalue offers a degree of the importance of the factor, and factors having the highest eigenvalues are the most significant. Eigenvalues of 1.0 or more are considered significant. [PERSON] et al. [26] additionally categorized the factor loadings as'strong',  
Figure 4: Factor loadings derived from scree plot and eigenvalue for LHW and three factors are retained for dry (**a**) and wet (**b**) seasons.
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.8 Machine Learning (ML)-Based NOMA Communications
For instance, the authors of [163] created a web-based ML clustering technique that can accommodate additional users up to a predetermined threshold.
# Phase Reversal Method for Damage Imaging in Composite Laminates Based on Data Fusion
## 2 Materials and Methods
### Phase-Reversal Method
In that case, the wave propagation along each path can be considered to be the same within the margin of error; otherwise, the anisotropy of the wave's velocity difference will influence the accuracy of the damage imaging. Assuming that the amplitude of the guided wave is constant during the propagation process, we can construct a Fourier signals, \(F^{*}(\omega)\), using the phase spectrum, \(\Phi^{\omega}\), derived by PRM. \[F^{*}(\omega)=A^{\omega}cos\Phi^{\omega}+iA^{\omega}sin\Phi^{\omega} \tag{5}\]  
Figure 1: Schematic diagram of the PRM. Then the inverse Fourier transform can be used to reconstruct the initial damage scattering signal at the scattering source. \[u^{*}(t)=\frac{1}{2\pi}\int_{--\infty}^{+\infty}F^{*}(\omega)e^{i\omega t}d\omega= \frac{1}{2\pi}\int_{--\infty}^{+\infty}(A^{\omega}cos\Phi^{\omega}+iA^{\omega} sin\Phi^{\omega})e^{i\omega t}d\omega \tag{6}\]  
Three adjacent sensors constitute a group. Theoretically, at the damage scattering source, the initial damage-scattering signals reconstructed by each group of sensors should have similar waveforms. Therefore, the damage imaging index, \(DI\), is defined as a similarity index of the signals, and it is calculated by the following:  
\[DI_{n}(x,y)=\frac{C_{n}(x,y)}{max(C_{n}(x,y))} \tag{7}\]  
where \(C_{n}(x,y)\) represents the signal correlation coefficient of the reconstructed damage scattering signal at position \((x,y)\); \(n\) represents the sequence number of a group of sensors, and the maximum value of \(n\) is the number of linear sensors in the same direction, i.e., \(m-2\). The signal correlation coefficient is defined by the following:  
\[C_{n}(x,y)=\frac{Cov\big{(}u_{1}^{*}(t)|(x,y)\,,u_{2}^{*}(t)|(x,y)\big{)}}{ \sqrt{Var\big{(}u_{1}^{*}(t)|(x,y)\big{)}\cdot Var\big{(}u_{2}^{*}(t)|(x,y) \big{)}}} \tag{8}\]  
where \(C_{n}(x,y)\) is defined as the cross-correlation coefficient at location \((x,y)\) between the \(u_{1}^{*}(t)\big{|}_{(x,y)}\) and \(u_{2}^{*}(t)\big{|}_{(x,y)}\); and \(u_{1}^{*}(t)\big{|}_{(x,y)}\) and \(u_{2}^{*}(t)\big{|}_{(x,y)}\) are the reconstructed scattering signal at point \((x,y)\).
## 1 Introduction  
Hemocompatibility testing provides information about whether a medical device or medical-device material can come in contact with human blood without adverse clinical effects. The standard for evaluating blood contact medical devices, ISO 10993-4 (2017) [1], is crucial in selecting potential tests. However, the tester must determine the specific testing methods and protocols. Two types of tests exist for evaluating thrombosis: (1) static tests to examine material-mediated thrombosis and (2) dynamic tests to evaluate flow-mediated thrombosis. Static tests are easier to perform and produce more consistent test results than dynamic tests. The ISO 10993-4 standard lists several potential static-test methods. However, the standard does not contain detailed testing protocols. Only two complete test methods have currently been standardized and approved by the FDA, namely (1) ASTM F2382-18: partial thrombolistin time (PTT) and (2) ASTM F2888-19 [2]: standard practice for platelet leukocyte count. The selection of an appropriate animal study design and blood parameters (species, anticoagulation, and collection method) in the design of tests to identify thrombosis is crucial for in vitro thrombotic studies. Considering the differences between species, human blood must be used, if possible; however, a large amount of blood is required and the use of human blood is limited [1].  
However, safety studies involving artificial blood vessels (ISO 7198:2016) and blood gas exchangers (ISO 7199:2016) require the use of animal models that simulate clinical environments and animal blood in in vitro environments. A few studies on blood compatibility evaluation of medical devices using animal blood have been reported [3,4].  
The standard (ISO 10993-4:2017) requires that the differences in the responsiveness of different species should be considered during blood contact when using animal blood and models [1]. However, only limited studies have been conducted on the validation of animal blood use in tests to confirm platelet activity, hematology, coagulation, and complement system activity, which are the main mechanisms inducing thrombogenesis due to contact between blood and substances [5,6].  
According to previous studies that confirmed the interaction between animal blood and biomaterials, the platelet activity and hematology patterns for different species are similar [7, 8, 9, 10, 11, 12, 13, 14].  
This study was conducted to determine the information required to design and interpret the results of the hemocompatibility evaluation model for the safety evaluation of in vivo, in vitro, and implantable medical devices. The coagulation system (PTT) (ASTM F2382-18) and complement system activation (ISO 10993-4:2017) confirmed the differences in the responses of different animal species [1,15].
## Materials and Methods
### Deprotection Kinetics Test  
Fmoc-Val-OH (0.1 mmol) was dissolved in a 20% piperidine/DMM solution. The reaction mixture was stirred at room temperature, and 10 \(\mu\)L were taken at time t = 0 (before base addition) and after 4, 6, 8, 10, 15, and 30 min. The samples were diluted with 1.5 mL of CH\({}_{3}\)CN/TFA (1% _v/b_) and injected into an HPLC-UV system. The same test was done with a 20% piperidine/DMF solution. The reaction was considered finished after the disappearance of the peak corresponding to Fmoc-Val-OH. Deprotection was monitored by analytical HPLC on an analytical column (Phenomex BioZen Peptide XB-C18 LC column, 150 \(\times\) 2.1 mm, 2.6 \(\mu\)m) using a Shimadzu SPD 20 A UV/VIS detector, with detection at 220 nm. The column was perfused at a flow rate of 0.2 mL/min with solvent A (water in 0.1% aqueous TFA), and a linear gradient from 5 to 90% of solvent B (acetonitrile in 0.1% aqueous TFA) over 16 min was adopted for peptide elution. Chromatograms are reported in Figures S4-S16.  
Evaluation of DBF and DBF-Base Adduct Formation  
A total of 30 mg of Fmoc-Val-OH was dissolved in a 20% piperidine/DMM solution. The reaction mixture was performed under magnetic stirring at room temperature for 15 min. Then, the solvent was evaporated under vacuum, and the residue was dissolved in dichloromethane and concentrated under vacuum. The crude products were purified by preparative liquid chromatography (PLC) using the mixtures of n-hexane/ethyl acetate (9.5:0.5) as the mobile phase. TLC analysis was performed on precoated glass silica gel plates 60 (F254, 0.25 mm, VWR International).  
The presence of DBF alone and the formation of the DBF-base adduct were directly analyzed by 1H NMR spectroscopy using a Bruker Advance (400 MHz) spectrometer (Figure S17).
## 4 Experimental Results and Analysis
### Datasets  
Table 2 shows sample images from the two datasets we have used to evaluate the efficiency of the U2-net model. The Machine Intelligence group generated the first dataset from the American University in Cairo (MI-AUC) [11]. We contacted the authors, who generously provided us with the link to download it. It contains 14,478 images distributed over 10 different class postures of the distracted driver (Co: Safe Driving, C1: Phone Right, C2: Phone Left, C3: Text Right, C4: Text Left, C5: Adjusting Radio, C6: Drinking, C7: Hair or Makeup, C8: Reaching behind, C9: Talking to Passengers). These images were extracted from videos captured by a camera installed on the car's roof on top of the passenger's seat. Each captured image is either 1920 \(\times\) 1080 pixels or 640 \(\times\) 480 pixels with RGB color model. The second dataset is the first publicly available dataset generated by State Farm's distracted driver competition on Kaggle [14]. It contains 22,424 images distributed over the same 10 classes. These images were captured by a camera installed on the car dashboard. Each captured image is 640 \(\times\) 480 pixels with an RGB color model. Table 3 shows the total number of images (32,833) used from both datasets to train and test the U2 model.  
Figure 3: The general structure of the research methodology.
## 1 Introduction
### Improving Energy Efficiency in Mexico City  
In Mexico City, the need to retrofit existing residential buildings [14] has been highlighted in recent years to prevent buildings' abandonment and obsolescence due to indoor thermal problems. The retrofitting of existing residential buildings to improve energy efficiency or reduce green housing emissions is not applied either [14]. More studies regarding improving energy efficiency in Mexico have only focused on electricity consumption. Mexican government programmes to reduce electricity use nationwide include the Trust for the Thermal Insulation of Housing and the Program for Integral Systematic Savings. Both programmes--managed by the Secretary of Energy [15]--have achieved a national energy use reduction of 3410.72 GWh and 1,534,824 fewer tonnes of concentrated CO2 in the atmosphere [14; 16] obtained by 120,703 thermal insulation actions. Despite the progress and goals set regarding retrofitting existing buildings in the heritage sector, significant challenges remained unsolved.
## 4 Implementation
### HoloLens Application  
#### 4.3.1 Design  
The implemented HoloLens application is based on the use of a 3D model of a hydraulic clutch (provided by Navantia's Turbine workshop) and on several PDF files that document the assembly process.  
The first step of the development consisted of defining the structure of the application and dividing the assembly steps into different sequences that would be later animated with Unity. The structure of the Unity scene and its components is defined as:  
* Camera: This is the Unity camera used to render the objects in the scene. At the beginning of the execution, it must be located in the XYZ (0,0,0) coordinates with no rotation in any of the axes.
* Directional lights: The lights that illuminate the scene in a homogeneous way.
* Manager: The empty GameObject that contains the general logic of the scene: GazeManager, GazeGestureManager (responsible for capturing gestures and the communications with the selected objects by sending an \"OnSelect\" message) and LocalizationManager (this component handles the movement of virtual elements).
* Cursor: The object located at the collision point between the gaze and the rest of the scene. The cursor is placed on the surfaces that can be interacted with to make it clear to the user that they can be selected.
* Panel/panels: Each panel contains buttons that are needed for interacting with the hydraulic clutch. Each button sends a message to the clutch so that the corresponding action is performed.
* Clutch/turbine: The 3D object of the clutch that is composed of all its parts.  
Figure 6 shows a simplified class diagram that represents the main components of the system. Most of the logic of the system is designed to be performed through Unity commands, which allow for keeping the virtual object behavior decoupled from the rest of the IAR application.  
The _ObjectMovementController_ script manages the behavior of the clutch while it is moving. The clutch will always face the user. Furthermore, the clutch will be hovering at the position the user is looking. During the movement process, _UnitySpatialMappin_ will be activated, so that the surface detected by the HoloLens will begin to be visible and the clutch will be prevented from being placed to collide with a real object.  
The _TurbineCommands_ script manages the animations and all the messages sent to the clutch from any other part of the scene (mainly from the buttons), while the Sharing module scripts are in charge of managing the shared experience subsystem. Moreover, there is an IoT module, which is in charge of managing the interactions with surrounding IoT networks. Finally, it is worth mentioning the _LocalizationManager_ script, which handles the changes made on the user interface so that she/he can select from the different supported languages.
## 3 SSMA Broadcast Protocol
### BSS Scheduling  
As shown in Figure 3, all BSSs are of identical size. All nodes at the same level share one BSS. A receiving BSS allocated to level \(i\) completely overlaps with the sending BSS allocated to level \(i-1\), since the nodes have to receive a broadcast message while their parents broadcast the message. Accordingly, a broadcast start time, \(bStartTime(i)\), a receiving start time, \(rStartTime(i)\), and the latest sleep time, \(latesSleepTime(i)\) of a node at level \(i\) are expressed as follows:  
\[bStartTime(i)=\begin{cases}sTime,&i=1(sink)\\ sTime+len(BS)+(i-2)\times len(BSS),&2\leq\ i\leq H-2\end{cases} \tag{5}\]  
According to Equation (5), a sink starts broadcasting at \(sTime\) and nodes at level \(i\geq 2\) compete for broadcasting in \((i-1)\)th \(BSS\) as illustrated in Figure 3.  
\[rStartTime(i)=\begin{cases}sTime,&i=2\\ sTime+len(BS)+(i-3)\times len(BSS),&3\leq\ i\leq H-3\end{cases} \tag{6}\]  
All nodes wait for receiving a broadcast message within BSS allocated to their parent level.  
\[latesSleepTime(i)=sTime+len(BS)+(i-1)\times len(BSS),&2\leq i\leq H-1 \tag{7}\]  
Nodes always go to sleep at least at the end of their sending BSS while they can go to sleep early as soon as they finish rebroadcasting.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 3 The Proposed Method
### GPS/SD RISS Integration
#### 3.1.2 The GPS System Model  
Two states are normally added to model the GPS receiver clock errors which are the receiver clock bias \(\delta b_{r}\) and the receiver clock drift \(\delta d_{r}\). The GPS receiver clock drift is modelled first using a random walk process, and the receiver clock bias is integral of that.  
\[\dot{\delta b}_{r}=\delta d_{r} \tag{21}\]\[\delta\dot{d}_{r}=\sigma_{d}\omega_{G} \tag{22}\]  
where \(\omega_{G}\) is white Gaussian noise and \(\sigma_{d}\) is the standard deviation of clock drift white noise. Thus, the state vector for the added GPS error states becomes:  
\[\delta X_{GPS}=[\delta r_{b},\delta r_{d}]^{T} \tag{23}\]  
Combining the two-state vectors from the RISS side and the GPS side, we obtain the complete state vector for the GPS/RISS integrated system:  
\[\delta X=[\delta\varphi,\delta\lambda,\delta h,\delta v_{e},\delta v_{n}, \delta v_{u},\delta A,\delta a_{o},\delta b_{z},\delta r_{b},\delta r_{d}]^{T} \tag{24}\]  
Now that we have all the error models; thus, we can form the system transition matrix as given in Equation (25). Writing the actual terms in the system transition matrix will make it look awkward; hence, we used some notation to write a compact version of that. Other than the zeros and ones, the terms in the matrix are on the form \(T_{j,k}\) where \(j\) is the equation number from which we obtain that term and \(j\) is the order of the term in the same equation. For instance, \(T_{16,4}\) means the fourth term in Equation (16) (or portion of that as underlined in the equation itself).  
\[F=\left[\begin{array}{ccccccccc}0&0&0&0&T_{13,1}&0&0&0&0&0\\ T_{14,2}&0&0&T_{14,1}&0&0&0&0&0&0\\ 0&0&0&0&1&0&0&0&0&0\\ T_{16,5}&0&0&T_{16,6}&T_{16,3}&0&T_{16,2}&T_{16,1}&T_{16,4}&0\\ T_{17,5}&0&0&T_{17,3}&0&0&T_{17,2}&T_{17,1}&T_{17,4}&0&0\\ 0&0&0&0&0&0&T_{18,1}&0&0&0\\ T_{12,2}&0&T_{12,3}&0&0&0&0&T_{12,1}&0&0\\ 0&0&0&0&0&0&T_{19,1}&0&0&0\\ 0&0&0&0&0&0&0&T_{20,1}&0&0\\ 0&0&0&0&0&0&0&0&0&1\\ 0&0&0&0&0&0&0&0&0&0\end{array}\right] \tag{25}\]  
The complete state vector for the system can be expressed as:  
\[\delta\dot{x}=F\delta x+Gw \tag{26}\]  
where \(\delta\dot{x}\) is the current (predicted) error state vector, \(\delta x\) is the last error state vector, \(G\) is the noise coupling matrix, and \(w\) is the noise vector. The expanded form of this equation can be written as follows:  
\[\left[\begin{array}{c}\delta\dot{\varphi}\\ \delta\dot{\lambda}\\ \delta u\\ \delta v_{e}\\ \delta\dot{v}_{n}\\ \delta\dot{v}_{u}\\ \delta\dot{A}\\ \delta a_{o}\\ \delta\dot{b}_{z}\\ \delta\dot{r}_{b}\\ \delta r_{d}\end{array}\right]=\left[F\right]\left[\begin{array}{c}\delta \varphi\\ \delta\lambda\\ \delta h\\ \delta v_{e}\\ \delta v_{n}\\ \delta v_{u}\\ \delta A\\ \delta a_{o}\\ \delta b_{z}\\ \delta r_{b}\\ \delta r_{d}\end{array}\right]+\left[\begin{array}{c}0\\ 0\\ 0\\ 0\\ 0\\ 0\\ \sqrt{2\beta_{o}\sigma_{o}^{2}}\\ \sqrt{2\beta_{o}\sigma_{z}^{2}}\\ 0\end{array}\right]w \tag{27}\]
## 5 Discussion  
### Factors Affecting Softening Degree  
In view of the above results, the applicability of various models to the two types of shear curves is obviously different. For the curves with different softening degrees, the appropriate damage distribution should be selected to construct the shear constitutive model. However, the softening degree is affected by many factors. For the same kind of soil, it shows the characteristics of weak softening curve when the pre-consolidation pressure and shear rate are small; when the pre-consolidation pressure and shear rate are large, the softening degree will increase, and it shows the characteristics of strong softening curve [43]. It is shown that the softening degree of the same soil will change under different stress histories and shear rates; thus, the change should be considered in the selection of damage distribution function. Other influencing factors, including moisture content [44; 45], matrix suction [46], and powder content [47], also affect the softening degree by changing the peak strength or residual strength and the corresponding displacement. Therefore, the shear constitutive model of slip zone soil based on statistical damage should fully consider the factors that affect the softening characteristics, such as the type of slip zone soil, deformation speed, stress history, and moisture content, to select the most suitable model to represent the damage evolution process of slip zone soil.
## 4 Results and Discussion
### Relief Characteristics of the Wadi Haly Catchment
This technique provides a detailed and complete understanding of how variations in slope can impact and control soil stability and hydrological behavior by examining the eight neighboring elevation values encompassing each raster point within a proposed sub-catchment and calculating the slope due to the highest elevation difference going downstream. In the present study, Figure 7 illustrates a wide range of relief and topographical signatures that could affect water flow and soil erosion patterns, with basin slope index values ranging from 23.77 for sub-catchment 1 to 138.14 for sub-catchment 8, respectively. In addition to the analysis that was obtained by basin slope index, the Melton ruggedness number (MRn) index was computed to better describe and understand the behavior of the sub-catchment terrain. This index, which is related to flow accumulation and very hard terrain, is estimated by taking the square root of the sub-catchment size and dividing it by the exact difference between the sub-catchment's minimum and maximum elevations. The MRn index is a very useful parameter for defining the overall terrain roughness and because of the intricate correlation between slope, vegetation cover, and water flow, more ruggedness frequently corresponds to higher levels of soil erosion action. In this study, the MRn values vary from 38.60 to 153.69, offering insight into the morphological variations within the catchments. Understanding these parameters is essential for effective watershed management and erosion mitigation strategies, enabling stakeholders to prioritize higher-risk areas and develop appropriate intervention measures. The highest results for all relief factors, including basin relief index, relief ratio index, roughness number index, and relative relief index, are observed in sub-watershed 6 of the Wadi Haly catchment. It is important to note that various research areas exhibit distinct relationships between different relief factors and soil erosion process due to a range of influencing factors. These effective parameters include changes in cover and land use, human activity, climate, geological, and geomorphological characteristics [63]. For example, the study of sub-catchments with resistant soils could reflect a reduced relation between soil erosion and relief signatures, while research catchments with slightly loose and erodible soils may reflect higher rates of soil erosion process even in the presence of lower relief signatures. Additionally, rainfall could easily reach low-relief areas, minimizing soil erosion action. Therefore, it is very important to investigate and understand these elements and how they relate to different relief factors to develop effective soil erosion control programs in specific research regions [40].