esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Light Emission in Nanocrystals
### Nanocrystals of Direct-Band-Gap Materials  
The majority of III-V and II-VI semiconductors are direct band gap materials. In this case, in the framework of the envelope function approximation,  
\[\mathbf{p}_{if}=\mathbf{p}_{cv}\langle F_{c,i}|F_{v,f}\rangle, \tag{4}\]  
where \(\mathbf{p}_{cv}\) is the standard bulk-like momentum matrix element of inter-band transitions, and \(F_{c,i}\) and \(F_{v,f}\), in case of the photon emission, stand for the envelope functions of the initial state in the conduction band and the final state in the valence band, respectively. The scalar product of these envelope functions determines selection rules for such transitions.  
In the simplest case, the QD is treated as an infinitely deep spherical potential well with radius \(R\) and the conduction and valence bands are assumed simple and parabolic. Consequently, the electron and hole ground states are described by the same envelope function and the scalar product in (4) equals to unity. It means that the radiative recombination rate turns out to be almost independent of the QD radius. This dependence manifests only in the transition energy, \(E_{if}\). In the limiting case of a strong quantum confinement regime, where \(R\ll a_{ex}\) (\(a_{ex}\) denotes the effective exciton Bohr radius), the exciton binding energy can be neglected. Then, the model of infinitely deep spherical potential well yields \(E_{if}(R)=E_{g}^{\infty}+\hbar^{2}\pi^{2}/2\mu R^{2}\), where \(E_{g}^{\infty}\) is the band gap energy of the bulk semiconductor and \(\mu\) is the reduced effective mass of the electron-hole pair.  
The above assumption concerning the extreme strong confinement regime is not quite realistic; usually the QD radius can be just slightly smaller than \(a_{ex}\) (for instance, \(a_{ex}=5.6\) nm for CdSe [12]) and the exciton binding energy can be estimated as [3]\(E_{b}\approx 1.79e^{2}/(\varepsilon R)\). Furthermore, the most common semiconductors have a degenerate valence band with light and heavy hole sub-bands, so that the confined holes are described by two envelope functions, which are completely different from the electron's one [81]. Thus, the matrix element (4) does depend on \(R\) (see, e.g., chapter by [PERSON] in the book [12]). Still, the dependence of \(\tau_{R}^{-1}\) on the QD size is rather weak for direct-gap materials as compared to, e.g., silicon NCs considered below. Using typical parameters for CdSe QDs, \(\varepsilon\approx 10\), \(\varepsilon_{1}\approx 2\) and \(\mathbf{p}_{cv}^{2}/m_{0}\approx 10\) eV [81],  
\[\tau_{R}^{-1}[\mathbf{s}^{-1}]\approx 3\times 10^{8}E_{if}(R)\,. \tag{5}\]  
Thus, \(\tau_{R}\) is of the order of a nanosecond, in accordance with experimental observations.
# Performance Analysis of Ambient Backscatter NOMA Systems
## 3 OP Analysis  
This section evaluates the OP performance of AmBC-NOMA systems. Considering the assumption of no interference between channels, we analyzed the outage behavior of the \(d\)-th user and the BD, whose asymptotic outage probability expressions were further obtained.
## 4 Results and Discussion
### Comparative Analysis of Different Models  
To comprehensively evaluate the performance of the proposed model, this section first analyzes the prediction results presented in Section 4.1, focusing on two aspects: the stability of prediction performance and the prediction errors during different cooling phases. Additionally, an expanded dataset beyond the original training and testing sets was introduced for further validation. This additional dataset covered various system conditions, aiming to extensively assess the generalization and adaptability of the proposed model.
# Changes in Spinal-Reflex Excitability during Static Stretch and/or Explosive Contraction
## 4 Discussion
### Statistical Trends and the Combined Effect  
We observed there was a 14% reduction (ES = 0.27) after static stretch and a 42% increase (ES = 0.63) after explosive contraction in the spinal-reflex excitability. Although moderate, the calculated ES after vertical jumps (contraction condition) supports the general idea that muscle contractions (preconditioning) acutely produce the post-activation potentiation (PAP) effect, especially on spinal-level excitability [49,50]. Considering the importance of VM activation during functional movements [51,52], the facilitative effect after explosive contraction also has the practical implication that a countermovement vertical jump is an appropriate pre-exercise activity. We speculate that the increased spinal-reflex excitability in our study was attributed to acute adjustment in neural adaptation due to the Ia presynaptic inhibition [53] and/or motor unit recruitment [54]. The increased spinal-reflex excitability seen in our study gradually decreased toward baseline after a 20 min measurement interval between the post-condition at 0 and 20 min (Figure 4); this prolonged increase has been reported in previous studies [55,56]. Along with no change in vertical jump heights, our observation of spinal-reflex excitability suggests that factors, other than neural activation (e.g., contractile response, temperature change), must play a role in performance enhancement as a PAP stimulus [54]. The Ia spinal-reflex more likely responds to low-intensity contractions [57], which also partly explains why our results did not show statistical differences. An antagonistic effect was expected in the combined condition (stretch and contraction). However, the percent change in spinal-reflex excitability (a 17% reduction) in this condition was similar to that in the isolated stretch condition, suggesting that the tensional stimulus dampens the contraction stimulus.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 2 Materials and Methods
### Random Forest (RF)  
RF, an efficient machine learning algorithm, consists of traditional decision trees. A random forest classifier can be defined as a collection of tree-type classification algorithms. Since it has more than one tree in its structure, it is a method that increases the correct class value [31; 40]. According to the RF algorithm, firstly, a decision tree based on subsets is created with the training data. A feature subset is used within each node. Thus, the risk of overlearning is reduced. In the last stage, the prediction with the highest value belonging to the predicted class is selected [30]. RF is preferred because it is a powerful model for detecting complex relationships between the target variable and the independent variable. It is also suitable for categorical and numerical data. RF has been proposed as a successful model in predicting human behavior, such as online exam data.
## 3 Rigid Transformation of the Coordinate System
\begin{array}{c}X_{R}\\ Y_{R}\\ 1\end{array} \right]=\mathbf{T_{1}}\mathbf{T_{2}}\cdots\mathbf{T_{n}}\left[ \begin{array}{ c}X_{C}\\ Y_{C}\\ 1\end{array} \right]=\left[ \begin{array}{cc}\overset{R}{C}\mathbf{R}&\overset{R}{C} \mathbf{t}\\ 0&1\end{array} \right]\left[ \begin{array}{c}X_{C}\\ Y_{C}\\ 1\end{array} \right]=\overset{R}{C}\mathbf{T}\left[ \begin{array}{c}X_{C}\\ Y_{C}\\ 1\end{array} \right] \tag{9}\]where \(n\) is the number of transformations from the image coordinate system to the robot coordinate system, \(\overset{R}{C}R\) represents the synthetic rotation matrix, \(\overset{R}{C}t\) represents the synthetic translation matrix, and \(\overset{R}{C}T\) represents the final transformation matrix. From Equations (1) and (4), the linear relationship between the coordinate value of the image coordinate system and the coordinate value of the world coordinate system is only affected by the height \(Z_{B}\) of the sampling point from the camera optical center. Because the robot coordinate system is equivalent to the world coordinate system, the transformation matrix of the robot coordinate system and the image coordinate system should also have a certain relationship with the sampling height:  
\[\overset{R}{C}R=\left[ \begin{array}{cc}\mathbf{A}_{11}&\mathbf{A}_{12}\\ \mathbf{A}_{21}&\mathbf{A}_{22}\end{array} \right],\ \overset{R}{C}t=\left[ \begin{array}{c}\mathbf{T}_{x}\\ \mathbf{T}_{y}\end{array} \right]\]  
Suppose that  
\[ \begin{array}{ll}\text{A}_{11}=f_{1}(H)&\text{A}_{12}=f_{2}(H)&T_{x}=f_{3}(H) \\ \text{A}_{21}=f_{4}(H)&\text{A}_{22}=f_{5}(H)&T_{y}=f_{6}(H)\end{array}  \tag{10}\]  
where \(H\) is the sampling height. Therefore, Equation (9) can be expressed as  
\[\left[ \begin{array}{c}X_{R}\\ Y_{R}\\ 1\end{array} \right]=\left[ \begin{array}{cc}f_{1}(H)&f_{2}(H)&f_{3}(H)\\ f_{4}(H)&f_{5}(H)&f_{6}(H)\\ 0&0&1\end{array} \right]\left[ \begin{array}{c}X_{C}\\ Y_{C}\\ 1\end{array} \right]\]  
The above formula establishes a rigid transformation model of the image coordinate system to the robot coordinate system at an arbitrary height \(H\). The following is an experiment to verify the correctness of the model.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 5 Conclusions
This is easily achievable in the case of maintenance work, where multiple locations are likely to be visited. If the device is switched on as soon as the building or floor has been entered, the localization will be reliable by the time a maintenance location has been reached. Especially when using a head-mounted device, the localization can keep observing the surrounding space without interfering with work or requiring special attention. A maintenance worker could then be offered information about the room they are in, could see contextual displays for devices that are at a static location in the building, or could visualize structures hidden behind walls and floors [2,5]. Another possible application for this research is indoor navigation for AR devices. With the mapping between the BIM model and the device coordinate frame, the location of the device in the building can easily be determined and a path to a goal can be calculated by using standard navigation algorithms. To mitigate the requirement of observing sufficiently different geometry, improvements to reliability can be made by including more of the available data of an AR device. External sensors as a secondary source of information may further help against self similarities and computer vision as they can be optionally installed in a building. Learning techniques could improve the knowledge of a building over time and could correct repeated mistakes. Thus, if combined further with other approaches, localization could be made fast, accurate, and reliable. Methodology, P.H. ; software, P.H. ; supervision, M.K. ; validation, P.H. ; writing--original draft, P.H. ; writing--review and editing, M.K. This research received no external funding. The authors declare no conflict of interest.
# U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images
As object detection is scalable and flexible, it has been used in unnatural scenes, and its detection effect is remarkable. With the popularity of object detection algorithms based on deep learning in recent years, the object detection method can be used to detect images, tables, and formulas in document images. Documents are composed of text areas and charts, which are complex text data. The tables in a document are rows and columns that represent relevant information content, which is convenient for readers to understand and analyze the text. However, the appearance, typesetting, and layout of tables vary greatly, and the size and style depend on the habits of the writer, thus, during document analysis, the wide variety of sizes, colors, and fonts can produce very different results. Therefore, this study used the object detection method Single Shot Detector (SSD) [27] to identify complex table areas, and integrated U-Net [28], which is an image segmentation model that merges and retains marginal information through its autoencoder features. In addition, dilated convolution was added to compensate for the slight loss of feature information and maximize the retention of table features. Accurate edge information can minimize errors of table detection, facilitate subsequent OCR operation, and improve the overall process efficiency. The contributions of this study include improved SSD and the end-to-end table detection model. VGG in the original SSD architecture was replaced with U-Net to increase edge features, and 8 additional convolution layers were added to increase feature extraction. In addition, the concept of dilated convolution was introduced to reduce information loss during feature transmission. Finally, six feature maps of different scales were selected to detect targets of different sizes. The edge prediction errors of the table are greatly reduced by the proposed method and the detection effect is remarkable. This experiment used the dataset of accident claim documents, as provided by a Taiwanese law firm, and the performance of proposed method was also evaluated on TableBank, Github, and ICDAR13 datasets. The results show that the network architecture based on SSD can achieve good performance.
## 3 Methods
### AnDA  
The analog data assimilation (AnDA) is a purely data-driven data assimilation method introducing a statistical operator \(\mathcal{A}\) as a substitute for the dynamical model \(\mathcal{M}\), leading to the following state-space formulation:  
\[\begin{cases}d\mathbf{x}_{k+1}&=\mathcal{A}_{k+1}(d\mathbf{x}_{k})+\mu_{k}\\ dy_{k}&=\mathcal{H}_{k}(d\mathbf{x}_{k})+\varepsilon_{k}\end{cases} \tag{2}\]  
The analog forecasting operator \(\mathcal{A}:\mathbf{d}\mathbf{x}_{k-1}^{a}\mapsto\mathbf{d}\mathbf{x}_{k}^{f}\), where superscripts \(a\) and \(f\) respectively refer to analysis and forecast, is built from the \(K\) most similar states to \(\mathbf{d}\mathbf{x}_{k-1}^{a}\) in the available past state dynamics catalog, supposed to be large enough to describe the space-time evolution of the processes. More precisely, \(\mathbf{d}\mathbf{x}_{k}^{f}\) is sampled from the Gaussian prior \(\mathbf{d}\mathbf{x}_{k}^{f}|\mathbf{d}\mathbf{x}_{k-1}^{a}\sim\mathcal{N}( \mu_{k},\mathbf{\Sigma}_{k})\), where the mean \(\mu_{k}\) and the covariance matrix \(\mathbf{\Sigma}_{k}\) are estimated using the so-called locally linear model [2], i.e., a weighted linear regression between the \(K\) nearest analogs and their successors.  
In the experiments, the diagonal of the observation error matrix \(\mathbf{R}_{k}=Cov(\varepsilon_{k})\) is not assumed constant but its values increase according to a parametric function of the hourly time lag between the observations \(\mathbf{y}_{k}\) and the day to estimation time \(t_{k}\), see Figure 3 below:  
As in [5], a patch-based version of AnDA coupled with an EOF-based representation of the individual patches is used. The anomaly field \(d\mathbf{x}\) is split into 169 vectorized patches \(\mathbf{p}(\mathbf{s},t)\) of sizes \(1^{\circ}\times 1^{\circ}\), corresponding to 20 pixels \(\times\) 20 pixels, with overlapping areas of 5 pixels. An EOF-based decomposition of each individual vectorized anomaly patches is then carried out to deal with the curse of dimensionality. Finally, the whole AnDA algorithm is performed at the patch-level, meaning that both the analog prediction and the assimilation are done onto the lower-dimensional space of their EOF-based representation. A final post-processing step (denoted as post-AnDA) is used to project the prediction onto the original space-time domain and average the overlapping patches to smooth out some blocky artifacts coming from the patch decomposition. On this last point, an improvement can be considered by using a convolutional neural network (CNN) to learn how to reconstruct the whole domain from the set of overlapping patches, as in [6].
# In Vitro Preliminary Evaluation of Bacterial Attachment on Grooved and Smooth Healing Abutments
## 2 Methods
### Tested Bacteria and Culturing Conditions  
The cultured bacterial strains were _Fusobacterium nucleatum_ (ATCC 1594) (_F.n_) from frozen stock cultures grown anaerobically in Sherdler broth (Becton, Dickinson and Company, Franklin Lakes, NJ, USA) and _Porphyomonas gingivalis_ (ATCC 33279) (_P.g_) from frozen stock cultures grown anaerobically in Wilkins-Chalgren broth (OXOID Ltd., Basingstoke, Hampshire, UK). Each of these strains was cultured on five smooth-surfaced and five grooved healing abutments, thus yielding four experimental groups.  
Sterilized abutments were screwed to the inner surface of the microtiter plate cover and immersed in a 96-well microtiter plate (96-well flat-bottom Nunclon, Copenhagen, Denmark), each well containing 250 microliters of growth medium suitable for each bacterial strain (_F.n_ was in Sherdler broth and _P.g_, in Wilkins-Chalgren broth). Into each well, ten microliters of bacterial suspension (O.D 0.6 at 600 nm), representing \(1\times 10^{6}\) cells/mL, were added, and the plates were incubated under anaerobic conditions for 48 h at 37 \({}^{\circ}\)C and vortexed automatically for five seconds every 30 min (VesaMax, Molecular Device Corporation, San Jose, CA, USA) in order to guarantee the homogeneity of the bacterial suspension. After 48 h (early colonization), the abutments were removed from the plate. Close direct contact between the bacteria and the abutment surface and bacterial suspension fluids was accomplished by an evaporation process. The abutments were fixated with 2.5% glutaraldehyde in PBS and gold-plated for examination with a scanning electron microscope (SEM) (JSM 840A, Jeol Ltd., Tokyo, Japan), at a 2500x magnification and with a 47.9 micrometer horizontal field and accelerating voltage of 25 kV.  
Bacterial attachment counts were performed on the four vertical quarters of the grooved abutments, both in smooth and grooved areas, while on the smooth abutments, they were performed in the two coronal millimeters (surface area of 958 \(\upmu\)m\({}^{2}\) for each quarter) using the ImageJ  
(\(p\leq 0.0001\)) than those in the smooth areas. The mean \(P.g\) counts were \(\sim\)20 times larger and those of \(F.n\) were over 100 times larger in the grooves compared to on the smooth surfaces.
## 2 RS Systems
### Passive
#### 2.1.4 Global Navigation Satellite Systems Reflectometry (GNSS-R) Reflectometry (GNSS-R)  
GNSS-R is one of the recent RS techniques which utilizes GNSS (e.g., United States Global Positioning System (GPS), Russian Global Navigation Satellite System (GLONASS), European Galileo, Chinese BeiDou, Indian Regional Navigation Satellite System (IRNSS), Japanese Quasi-Zenith Satellite System (QZSS)) Earth-reflected signals to study various geophysical parameters over ocean, land, and atmosphere [33]. In this technique, ground-based, airborne, or spaceborne receivers receive the GNSS signals from the ocean/land surface. GNSS observations rely on the cross-correlation of the reflected signal with the signal received from GNSS satellites [34]. Since the signal is affected by the environment, its shape is different from the main signal, and this variation is studied to derive information about the surface or atmosphere. Ocean surface topography, OSW speed [35; 36; 37], Ocean Wave Height (OWH), soil moisture, SI extent [38], snow depth, ocean precipitation, and atmosphere compositions are some of the applications which are being studied using GNSS observations. Currently, there are multiple satellite missions, airborne campaigns, and ground-based stations that are dedicated to collecting and studying GNSS signals. Some of the well-known satellite missions are United Kingdom Disaster Monitoring Constellation (UK-DMC) [37], TechDemoSat-1 (TDS-1) [39], and National Aeronautics and Space Administration (NASA)'s Cyclone Global Navigation System Satellite (CYGNSS) constellation [40].
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 1 Introduction
The ANCF uses position vector and derivative to describe the element nodes, which can avoid the limitation of the small rotation angle in the traditional finite element method, and describe arbitrary translation, rotation and deformation [15]. The mass matrix in ANCF is constant, and there are no centrifugal force terms and Coriolis force terms in the dynamic equation, which further simplifies the numerical calculation [16]. ANCF was employed to establish the dynamics model of space tether in new applications due to its ability to describe the nonlinearity and large deformation of the tether [17], including the effects of solar wind on an electric sail (E-sail) [18], deployment dynamics and debris capture of tethered space net [19], and tethered spacecraft formation [20]. The ANCF method has also been applied to the dynamics of tethered satellites and partial space elevators [21; 22; 23]. It is necessary to accurately obtain the space elevator system's mechanical properties for its design and construction. The mechanical model established for analysis needs to be as close to the actual situation as possible. Most of the existing mechanical models are based on the \"chain-bar\" and lumped mass tether model, which cannot effectively reflect the flexible behavior of the rope of space elevator system. Thus, in this paper, the ANCF method will be applied to the calculation of the space elevator system, in order to describe the rope flexibility of the space elevator system.