esa-sceva/satcom-chunk-collection huggingface dataset:
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 3 Resilient Moving Object Index
### Index Structure  
RMOI consists of a global index, a local index, and prediction models. Each node of the global index, denoted by GlobalRMOI, contains a Minimum Bounding Rectangle (MBR) and time interval, as illustrated in Figure 2. The MBR is used to specify the minimal spatial area covered by the contained sub-trees, while the time interval is also used to specify the minimum time range. Each leaf node has the corresponding partition identification number (\(Pid\)), and there are \(tpn\) leaf nodes. GlobalRMOI is influenced by the partitioning mechanism, which depends on the value of the \(LP\). In general, GlobalRMOI is a combination of k-d-B-tree [7] and R-tree [3]. However, when \(\beta=1\), GlobalRMOI is similar to k-d-B-tree, and it only reflects the space-based partitioning. Comparing to the rest \(\beta\) values, this scenario gives the maximum spatial-locality and the least object-locality. Alternatively, when \(\alpha=1\), R-tree dominates the structure of the GlobalRMOI, as this is a scenario when it only depends on object-based partitioning. In this case, RMOI guarantees full trajectory-preservation where all the segments (\(Segs\)) of a trajectory (\(Traj\)) reside in one partition, which equals one GlobalRMOI leaf node.  
The local index, denoted by LocalRMOI, consists of STRtree [35] and interval B-tree [36]. STRtree is a packed R-tree that uses the Sort-Tile-Recursive (STR) algorithm. Unlike the GlobalRMOI, the LocalRMOI favors the temporal dimension, where the interval B-tree is kept separately from STRtree. Each interval  
Figure 1: Partitioning phase in Resilient Moving Object Index (RMOI) with \(\alpha=3\) and \(\beta=2\).  
Figure 2: RMOI structure.  
node is associated with a subtree of the local STRtree allowing us to traverse the local index based on the interval B-tree, as seen in Figure 2.  
The prediction model is one of the essential components in RMOI. It is responsible for capturing the differences in computation resources, storage availability, nature of data, and query types. The goal of the prediction model is to determine the value of the Locality Pivot (\(LP\)) in constant time without an overhead on top of the index construction. RMOI uses polynomial regression on different features by applying a low-degree polynomial transformation. The model consists of two parts: a Compulsory Prediction Model (CPModel) and a Discrete Prediction Model (DPModel). The CPModel is used as a cold start when there is no hint of the incoming query types, while DPModel takes into consideration the frequency of requests for various query types and their impacts. The DPModel depends on another model, the query Impact Factor Model (IFModel), when there is more than one query type. The IFModel is used to distinguish between query types and determine the heaviness of each type. Both models are already trained on real datasets and do not need to be fit again. For simplicity, RMOI only incorporates the final polynomial equations without the need for further training.
## 3 Candidate Selection and Localization  
The candidates are first selected and located using a rotation-invariant descriptor and the second stage uses the rotation-estimation method. For computational efficiency, the ring-projection transformation and the image pyramid technique [19] are used during the candidate selection process. These techniques identify the best matching results by using low-cost features in each image pyramid level. In the candidate-selection and localization process, the selection of a template image for the proposed method is a crucial step. This template image is used in the template preprocessing phase to generate hybrid-projection values. The candidate-selection and localization uses these hybrid-projection values. Therefore, the features of the template image in the template preprocessing phase considerably influence the matching results. In the hybrid-projection transform, the hybrid-projection values are calculated using a circular region between \(R_{l,\text{min}}\) and \(R_{l,\text{max}}\) on image pyramid level \(l\). For illustrating the hybrid projection calculation, a concise example is illustrated in Figure 4, the hybrid projection values on current image pyramid \(l\) are calculated from the \(R_{l,\text{min}}=~{}20\) to \(R_{l,\text{max}}=200\). And the details of hybrid projection calculation are described in the following subsections.  
Figure 3: Architecture of the proposed algorithm.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance
### The Increasing Expression of Efflux Gene  
_The hefA_ gene code for the efflux gene plays a vital role in multidrug resistance [93]. [PERSON] et al. investigated the efflux gene, _hp1165_, and _hefA_ expression on the biofilm-forming cells compared to planktonic cells by quantitative PCR. The expression was significantly higher, and there was an association between biofilm formation and antibiotic resistance [94]. Increasing resistance-modulation-division (RND) efflux pump gene expression on the biofilm forming was also reported [95]. Other efflux genes, _hp0939_, _hp0497_, and _hp0471_, were highly expressed in the clinical isolates with multidrug resistance strains. When these genes were knocked out, the strains were more antibiotic-sensitive, and the biofilm formation was significantly reduced [96].
## 3 Results
### Summary of the Selected Articles
#### 3.1.4 Medicine
In Table 6, it is possible to see the important terms that highlight the characteristics of AR in medicine, which are simulation, entertainment, visuals, learning, rehabilitation, and precision. Described are the problems or difficulties with AR applications (-), benefits or positive experiences (*), an intermediate point where even though there were issues, it was possible to find solutions (+), or articles that did not show information about the branch suggested (/). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Medicine**} \\ \hline
**Databases** & **Articles** & **Simulation** & **Training** & **Visualization** & **Learning** & **Rehabilitation** & **Precision** \\ \hline \multirow{3}{*}{IEEE Xplore} & Effects of virtual reality and augmented reality on induced & * & / & / & / & / & / \\  & anxiety [49]. & & & & & & \\ \cline{2-7}  & Projected augmented reality to drive osteotomy surgery; & & & & & & \\ Implementation and comparison with video see-through technology [57]. & & / & - & * & / & / \\ \hline \multirow{3}{*}{Science Direct} & Augmented reality-based visual-haptic modeling for thoracoscopic surgery training systems [17]. & / & * & / & / & / & / \\ \cline{2-7}  & The design choices for the development of an augmented reality game for people with visuospatial neglect [50]. & / & / & / & * & / \\ \cline{2-7}  & Chapter 4—Augmented reality, virtual reality, and new age technologies demand escalates amid COVID-19 [11]. & * & * & / & - & / \\ \hline \multirow{3}{*}{Google Academic} & Medical augmented reality visualizer for surgical training and education in medicine [51]. & / & / & - & / & / & * \\ \cline{2-7}  & Augmented reality for guideline presentation in medicine: Randomized crossover simulation trial for technically assisted decision making [52]. & / & / & / & / & / & * \\ \cline{2-7}  & Providing dementia education with augmented reality: a health sciences and medicine feasibility pilot study [58]. & * & * & - & - & / \\ \hline \multirow{3}{*}{[PERSON] \& [PERSON]} & Augmented reality for guideline presentation in medicine & & / & / & / & / & / \\ \cline{2-7}  & Randomized crossover augmented reality registration [53]. & & / & - & / & / & * \\ \cline{2-7}  & Influence of sampling accuracy on augmented reality for laparoscopic image-guided surgery [54]. & / & / & - & / & / & * \\ \hline \multirow{3}{*}{[PERSON]} & Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education [56]. & * & / & / & * & / & / \\ \cline{1-1} \cline{2-7}  & A workflow to generate patient-specific three-dimensional augmented reality models from medical imaging data and example applications in urologic oncology [55]. & * & / & / & * & / & / \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The main AR characteristics found in the articles for medicine.
## 2 Technology Background
### Different Color Spaces  
The purpose of a color space (also called color model or color system) is to facilitate the specification of colors in some standard [22]. A color space is represented by matrix in mathematics, and typically, the matrix consists of three or four dimensions (e.g., RGB, HSI, and CMYK). The RGB color space is the most common color space used in electronic devices, such as cell phones and monitors. In the RGB color space, each color can be acquired by the addition of three primary colors: red, green, and blue. Therefore, this color space is an additive color space. The original stained blood smear image is represented by the RGB color space in the RGB model.  
The HSI color space reflects the human vision system, where H denotes hue, S denotes saturation, and I denotes intensity. The I component is independent of color information, and the H and S components are closely related to the way humans perceive color. These characteristics make the HSI color space suitable for color detection and analysis. The schematic the of RGB and HSI color models is shown in Figure 2.  
The CMYK color model is a subtractive color model and it is used in color printing. CMYK refers to the four inks used in color printing: cyan, magenta, yellow, and black. In the CMYK color space, C denotes cyan, M denotes magenta, Y denotes yellow, and K refers to the key plate (black).  
The Lab color model is based on the way human perceive color, and it can describe all the color that human can see. This model minimizes the correlation between different color components. Color management system utilizes Lab as color patch to color transform one color space to another color space. Every color model has its own specific features, and the original stained blood smear image in the RGB color space shows useful features that benefit WBC segmentation when translated to HSI and CMYK color spaces. The details are discussed in the following section.
## 2 Mathematical Formulation
### Objectives Formulation
#### 2.3.1 Energy Consumption  
The energy consumption of freezer units followed Theorem 1 and the assumptions previously identified. Figure 1 shows an example of the evolution of the energy consumption with the amount of stock. The example considered 3 individual freezer units, and the second freezer was only used after the first was completely full.  
The energy consumption of the cold storage \(E_{cold}\) can then be formulated as shown in Equation (2). Note that the square brackets with no upper horizontal segments correspond to the floor operator, while the square brackets with no lower horizontal segments correspond to the ceiling operator.  
\[E_{cold}=\left[\frac{\alpha_{2}}{1+\exp\left(-\left(\frac{S_{cold}-1}{\alpha_{ 1}}-\left[\frac{S_{cold}-1}{\alpha_{1}}\right]\right)\cdot\frac{\alpha_{5}}{ \alpha_{4}}+\alpha_{5}\right)}+\left[\frac{S_{cold}}{\alpha_{1}}\right]\right] \cdot\alpha_{3}\quad[EU] \tag{2}\]Variable \(S_{cold}\), the total stock in the cold storage, can be calculated as shown in Equation (3).  
\[S_{cold}=\left(X^{[n_{i}\times 1]}\right)^{T}\cdot\left(A^{[n_{i}\times n_{k}]} \cdot ET_{cold}^{[n_{k}\times 1]}\right)\quad[IU] \tag{3}\]  
Regarding the energy consumption of the warehouse, \(E_{warehouse}\), Theorems 2 and 3 were considered, along with the assumptions provided. The formulation of the energy consumption of the warehouse is shown in expressions (4) and (5).  
\[E_{warehouse}=FC_{warehouse}+E_{WH_{transport}}+E_{WH_{HVAC}}\quad[EU] \tag{4}\]  
\[\left\{\begin{array}{l}E_{WH_{transport}}=S_{warehouse}\cdot\beta_{1}\quad[ EU]\\ E_{WH_{HVAC}}=\beta_{2}-S_{warehouse}\cdot\beta_{3}\quad[EU]\end{array}\right. \tag{5}\]  
Variable \(S_{warehouse}\), the total stock in the warehouse, can be calculated as shown in Equation (6).  
\[S_{warehouse}=\left(X^{[n_{i}\times 1]}\right)^{T}\cdot\left(A^{[n_{i} \times n_{k}]}\cdot ET_{warehouse}^{[n_{k}\times 1]}\right)\quad[IU] \tag{6}\]  
Tanks are generally simpler in terms of their energy consumption. For these reasons, the total energy consumption of the tanks is linearly proportional to the rate of fullness of the tank.  
\[E_{tank}=\sum_{kt=1}^{n_{kt}}\frac{\gamma_{kt}\cdot S_{tank_{kt}}}{Lim_{kt}} \quad[EU] \tag{7}\]  
The stock on each tank can be calculated as shown in expression (8). Contrarily to the stocks in the warehouse and cold storage, \(S_{tank}\) has a size of \([1\times n_{kt}]\), that is, a stock for each tank.  
\[S_{tank}=\left(X^{[n_{i}\times 1]}\right)^{T}\cdot\left(A^{[n_{i}\times n_{k}]} \cdot ET_{tank}^{[n_{k}\times n_{kt}]}\right)\quad[IU] \tag{8}\]
# Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles
## 4 The Proposed Model
### Activity Replacement Strategies  
Based on the hypotheses presented in Section 3, we devised four activity replacement strategies, applied in two phases. In the first phase, we do not provide any mechanism for quality enforcement, such as a central authority. However, we provide quality enforcement in the second phase. The following descriptions apply to the activity replacement strategies:  
1. **ReplaceS:** Replaces an activity with the help of a strong tie from the SIoV.
2. **ReplaceW:** Replaces an activity with the help of a weak tie from SIoV.
3. **ReplaceM:** Replaces an activity with the help of vehicles' data of the person on SN with highest degree.
4. **ReplaceL:** Replaces an activity with the help of vehicles' data of a random person on SN.  
The first two strategies use SIoV alone. The geographic (proximity-based) IoV evolves with time due to presence of co-located vehicles. The vehicles share their data with their networked vehicles reciprocally. This includes data about past experiences. In the case that a vehicle needs to move to a POI against which its past experience was not good, it uses this data to get an alternate POI. However, there are two variations that can be adopted.  
IoV transforms into SIoV, when vehicles start labeling networked vehicles as _weak_ or _strong_ ties, in response to repeated encounters. In _ReplaceS_ strategy, we only use data from a strong tie to get an alternate POI, whereas, in _ReplaceW_ strategy, we use data from a weak tie to get an alternate POI. Hence, a comparison between these two strategies would give us answers regarding the first hypothesis.  
Figure 1: Model schematic: The activity life cycle explained.  
Figure 2: Point of Interest (POI) quality update mechanism.  
The remaining two strategies (third and fourth) use SNs to disseminate vehicles' data acquired through SIoV. In addition to SIoV, a SN also exists between the users of the vehicles--the inter-person network. In the following sub-section, the model of social network is detailed. In these two strategies, the SN is used to get a person whose vehicle data would be used to suggest an alternate POI. In _ReplaceM_ strategy, we acquire the vehicle data of the highest connected person in SN to get an alternate POI from. In _ReplaceL_ strategy, however, we acquire the vehicle data of a random connected person in SN to get an alternate POI from. Hence, a comparison between these two strategies would give us answers regarding the second hypothesis.  
The complete set of four strategies act for both the phases. Hence, a comparison between related strategies across two phases would give us answers regarding the third hypothesis. Table 1 presents the possible situations and relates them to the three hypotheses.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 4 Data Description  
The traffic flow data collected from four dual-loop detectors on I-80 freeway segments was used to evaluate the proposed approach. The detectors recorded the traffic flow observations of three traffic measures including the flow rate, speed, and occupancy every 30 s. In the study, the lane-by-lane traffic flow observations were aggregated into 5-min intervals at each detector station in order to obtain stable traffic data. The data were acquired through the California Performance Measurement System (PeMS). Figure 3 illustrates the locations of the detector stations. The study period was two months long, from 5 June to 5 August 2007. For the regime identification task, the whole data set was used, because the developed identification model was unsupervised, meaning it did not require label information. For the short-term traffic flow forecasting task, the whole data set was divided into two parts, one for model training and the other for model evaluation. The training data set included the traffic flow observations from 5 June to 20 July, and the evaluation data set included the traffic flow observations from 21 July to 5 August.
## 2 Method
### Fixed Point Stability Analysis
Since \(0<\alpha\leq 1\) and \(0<\beta\leq 1\), condition (35) requires:  
\[\frac{\partial Z^{t}}{\partial E^{t-1}}=\beta\frac{dV}{dF} \tag{25}\]5Using expression (28), (39) can be written as:  
\[\frac{\partial Z^{t}}{\partial E^{t-1}}=\beta\frac{dV}{dF} \tag{25}\]6  
from which the following condition on \(\varphi\) can be derived:  
\[\frac{\partial Z^{t}}{\partial E^{t-1}}=\beta\frac{dV}{dF} \tag{25}\]7  
Next, we consider condition (36) and rewrite it as:  
\[\frac{\partial Z^{t}}{\partial E^{t-1}}=\beta\frac{dV}{dF} \tag{25}\]8  
Using again expression (28) we obtain the following condition on \(\varphi\):  
\[\frac{\partial Z^{t}}{\partial E^{t-1}}=\beta\frac{dV}{dF} \tag{25}\]9  
Hence, we conclude that a fixed point \(\bigg{(}\widetilde{Z},\widetilde{F}\bigg{)}\) is locally stable if:  
\[\frac{\partial F^{t}}{\partial Z^{t-1}}=\alpha(1-\beta)\frac{dS}{dZ} \tag{26}\]0  
with the lower and upper limits being effectively binding inside the range \(0\leq\varphi\leq 1\), that is for:  
\[\frac{\partial F^{t}}{\partial Z^{t-1}}=\alpha(1-\beta)\frac{dS}{dZ} \tag{26}\]1  
\[\frac{\partial F^{t}}{\partial Z^{t-1}}=\alpha(1-\beta)\frac{dS}{dZ} \tag{26}\]2  
In light of the behavioral interpretation of parameters \(\alpha\) and \(\beta\) provided in Section 2.1 we note that, in the particular case of a population of memoryless travelers (\(\beta=1\)) completely free of inertia (\(\alpha=1\)), the range of \(\varphi\) values ensuring fixed point stability reduces to the following interval, which is symmetrical around \(\varphi=0.5\):  
\[\frac{\partial F^{t}}{\partial Z^{t-1}}=\alpha(1-\beta)\frac{dS}{dZ} \tag{26}\]3subject to (46). In the remainder of this subsection, we show how more specific fixed point stability results can be derived by assuming explicit forms of the link cost functions.
## 2 Unsaturated Subgrade Filler Triaxial Testing System
### Analysis of Experimental Results
#### 3.4.4 Analysis of Constitutive Behavior

Table 5: Model coefficients. Figure 15b shows the yield behavior curves for six different stress paths, where each curve corresponds to a specific path, and the solid circles on each path represent the yield points. As the mean net stress increases, the yield curve shifts upward, indicating that the yield suction of the sample increases with the increasing mean net stress, a trend observed across all stress paths. However, differences exist in the slopes and shapes of the yield curves across different paths. For instance, the yield curves for stress paths 1 and 2 are steeper, indicating that the sample is more sensitive to net stress along these paths, especially at 200 kPa, where the yield suction for stress path 1 approaches 400 kPa, and the yield suction for stress path 2 is slightly lower. In contrast, the yield curves for stress paths 5 and 6 are relatively flat, suggesting that the yield suction of the sample varies less along these paths. Notably, most of the yield curves in the figure exhibit a relatively flat slope in the initial stage, and as net stress increases, the curves gradually become steeper, indicating that in high-stress regions, the sample's yield behavior becomes more sensitive to net stress. For instance, the yield points for stress paths 5 and 6 are nearing saturation around 300 kPa, and in high-stress regions, the increase in yield points gradually slows. This indicates that the yield behavior of unsaturated soils is influenced not only by stress paths but also by stress history, which plays a major role in the yield behavior of the soil. The yield point positions also exhibit clear differences in distribution, as the yield point positions for stress paths 1 and 2 are mostly in the high suction region, while stress path 6 has yield point locations in the lower suction region. In addition, the distribution of yield point positions clearly differs due to the varying effects of the different stress paths on yield behavior, which may be attributed to stress history and loading path. BBM model parameter calibration typically relies on suction-controlled tests, with each sample's calibration process requiring 1-2 months, making it both cumbersome and costly. In comparison, the 3D settlement and consolidation system developed in this study significantly reduces the calibration time to 6 h, greatly enhancing experimental efficiency. This system not only reduces the experimental cycle but also shows clear advantages in terms of ease of operation, accuracy control, and data acquisition, offering a more efficient and economical solution for rapid BBM model calibration.
# A Review of Ghost Imaging via Sparsity Constraints
## 3 Applications of GISC
### GISC LiDAR
A pseudo-thermal light source, which consists of a pulsed laser, a diffuser such as a rotating ground-glass disk controlled by a high-precision motor, and a lens \(f\), forms random speckle patterns on stop 1. The light emitted from the source goes through stop 2 and is subsequently divided by a beam splitter (BS) into an object and a reference paths. In the object path, the speckle pattern on stop 1 is imaged onto the target plane by an objective lens \(f_{0}\). The photons reflected from the target are collected by a light concentrator and then passes through an interference filter into a photomultiplier tube (PMT) connected with a high-speed digitizer (an equivalent time-resolved single-pixel bucket detector). In the reference path, CCD camera is placed on an image plane of stop 1. In 2012, GISC LiDAR was first demonstrated experimentally with a target 900 m away in real atmosphere [18]. A year later, a 3D image of a real scene at a range of approximate 1.0 km was demonstrated [19]. However, compared to the traditional nonscanning LiDAR, GISC LiDAR usually requires many measurements in a long data acquisition time to achieve the information of the object. While in remote sensing applications, either the LiDAR system is often placed on a moving platform such as an airborne craft and a vehicle, or the object is a moving target with high speed, thus the relative motion between the LiDAR system and target during sampling process will result in motion blur inevitably [47; 48; 49; 50]. Besides algorithms and strategies that have been proposed to overcome motion blurs due to constant relative speeds [49; 50], two new approaches are developed recently to further improve the imaging performance. One is to short the sampling time by either increasing the sampling speed or decreasing the number of samples. The other is to adopt the traditional motion compensation techniques to stare at the scene to be imaged during the sampling process.