esa-sceva/satcom-chunk-collection huggingface dataset:
# End-Users' Augmented Reality Utilization for Architectural Design Review
## 3 Research Methodology  
### Procedures and Participants  
The primary purpose of this study is to evaluate the effectiveness of an AR system for an architectural design review task, concerning visual presentation quality, acceptability, and experience from the end-user's perspective. This includes comparing the AR system with other visualization platforms such as a 2D screen and a VR system. For a comparative analysis, fully prepared BIM-based environments using a 2D screen, a VR headset, and an AR headset were employed for a design review task. A total of 30 undergraduate students from the Hong Kong Polytechnic University who are studying construction-related majors were recruited. They were randomly divided into three different groups for each visualization platform. Participants were briefed about the procedures of the design review task using the assigned display platform as a visualization tool before performing a design review. After the design review was conducted by each subject, he or she verbally answered a questionnaire to determine the level of satisfaction regarding the design of specific architectural components (e.g., siding, roofing, walls, floors, and fenestration of the building) and overall atmospheric aspects (e.g., overall shape and volume of the building's interior and exterior) of the building model. In addition, to measure the user experience of the AR system for design review, an additional 46 participants performed AR-based design review activities including rotating viewpoint, navigating through a building, and measuring building elements. After the design review, subjects were required to complete questionnaires in order to rate the quality of visual presentation, perceived acceptability, and user experience for conducting an architectural design review. All 76 participants were provided with the informed consent process for voluntary participation and briefed about the purpose and process of the experiments sufficiently.
Sensors_**2015**, _15_, 24771-24790; doi:10.3390/s151024771  
_Sensors_  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
The Applications of Decision-Level Data Fusion Techniques in the Field of Multiuser Detection for DS-UWB Systems  
**[PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1,*  
1 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; E-Mails: guyebo1987(@gmail.com (Y.G.); [EMAIL_ADDRESS] (Z.S.)  
2 Shanghai Electro-Mechanical Engineering Institute, Shanghai 200245, China; E-Mail: ahminglei1989(@gmail.com  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-451-8641-3506; Fax: +86-451-8640-3135.  
Academic Editor: [PERSON]  
_Received: 11 August 2015 / Accepted: 18 September 2015 / Published: 25 September 2015_  
**Abstract:** In this paper, the decision-level data fusion techniques are extended to the multiuser detection (MUD) field. Then two novel MUD algorithms, that is the chairman arbitrating decision-level fusion criterion (CA-DFC) based MUD algorithm and the veto logic decision-level fusion criterion (VL-DFC) based MUD algorithm, are proposed for DS-UWB communication systems. In CA-DFC based method, the chairman can make his arbitration among the preliminary decisions from sub-optimal detectors by his own rule. In the VL-DFC based method, the undetermined bits in these preliminary decisions are considered to construct a simplified solution space, and then the chairman can make his final decision within this space. Simulation results demonstrate that the performances of CA-DFC and VL-DFC based MUD algorithms are superior to those of other sub-optimal MUD algorithms, and even close to that of OMD. Moreover, both of these proposed algorithms have lower computational complexity than OMD, which reveals their efficiency. Compared with CA-DFC, VL-DFC based algorithm achieves a little improvement in its performance, at the cost of the increment in its computational complexity. Thus, they can be applied to different practical situations.  
**Keywords:** data fusion; decision-level fusion criterion (DFC); multiuser detection (MUD); DS-UWB
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Mechanisms
#### 4.2.5 Synchronization Security  
As discussed in Section 4, synchronization is essential in many underwater applications and scheduling MAC protocols. As discussed in Section 2, due to the characteristics of UWSNs, synchronization security protocols proposed for WSNs are unsuitable for UWSNs. Moreover, it is especially difficult to achieve precise time synchronization in underwater environments.  
Although it is critical among the UWSN issues, none of existing time synchronization schemes [77; 78; 79] take security in consideration. Proper cryptographic techniques can be used to defend time synchronization attacks (e.g., masquerade, replay and manipulation attacks). However, the countermeasures against delay attacks for WSNs [80; 81; 82] are not applicable to UWSNs.
# A Novel Binary QUasi-Affine TRansformation Evolutionary (QUATRE) Algorithm
## 4 Experimental Results and Analysis
### Hyperspectral Imagery
Furthermore, the proposed algorithms run 500 iterators with 20 populations. We performed random 5% sampling 500 times before doing the experiment in order to ensure the robustness. Table 18 describes the classification performance of these algorithms performed on the Indian Pines dataset. The red color means the proposed algorithm performs better than all three compared algorithms on this index. We can see that the proposed four algorithms all get higher OA and Kappa indexes than the three compared algorithms. However, the AA value of all proposed algorithms are slightly lower than LDA-SVM. That is due to the poor performance in classifying the \"oats\" and \"alfalfa\" by the proposed algorithms. It can be seen that the samples of these two classes are smaller than others from Figure 12. LDA-SVM correctly classified a few samples in these two classes that has a greater impact on the value of AA, but has a smaller impact on the value of OA. Figure 13 is the visualization results of the average OA, AA, Kappa values of the BQUATE2-S1i-SVM, BQUATE2-V1i-SVM, BQUATE1-U1-SVM, BQUATE1-Z1-SVM, SVM, PCA-SVM, and LDA-SVM on Indian Pines. In order to visually see the classification results, we paint the different ground features with different colors. Figure 14 shows the classification maps. As a whole, we can see that the proposed algorithms perform better for dimensionality reduction of HSI than PCA and LDA. Figure 13: The OA, AA, Kappa values of BQUATE2-S1i-SVM, BQUATE2-V1i-SVM, BQUATE1-U1-SVM, BQUATE1-Z1-SVM, SVM, PCA-SVM, and LDA-SVM on Indian Pines.
## 3 Results and Discussion
### Effect of VPD Regulation on Tomato Crops in Coconut Fiber above PVC Trays
#### 3.3.2 Effect of VPD Regulation on Yield  
In this experiment, the yields and harvested fruits were improved, as shown in Figure 9. VPD regulation showed approximately 89% improvement in the total yield and 88% improvement in the total commercial yield. At 96, 139, 145, 152, 159, and 166 days after transplanting, the total yield of T1 DC improved by more than 100%. The number  
Figure 8: Growth parameters of tomato crops grown in coconut fiber above PVC trays: (**a**) evolution of height; (**b**) evolution of leaves developed; (**c**) evolution of fruit setting; (**d**) evolution of phenological growth stages.  
of total fruits was also improved by 49% on average. The measurement results had a normal distribution, and the \(p\)-values of the \(t\)-tests for these three measurement results were less than 0.05, so the results are statistically significant. As for the number of total commercial fruits, the treatment without VPD regulation showed a better evolution until 89 days after transplanting, but the results were reversed from 103 days after transplanting. The treatment with VPD regulation improved the number of commercial fruits by 45% on average. However, the result was not statistically significant because the \(p\)-value of the \(t\)-test was greater than 0.05. H1 for the total yield, commercial yield, and total fruits is true, but false for commercial fruits.
## 4 Background
### Pearson's Coefficient  
Pearson's coefficient [Equation (1)] is used to identify the spatial correlation of the same variable between two sensor nodes [14]. But, it can also be used to identify the correlation between two variables of the same sensor node:  
\[r_{X_{1}X_{2}}=\frac{\sum\bigl{(}x_{1_{i}}-\bar{X}_{1}\bigr{)}*(x_{2_{i}}-\bar {X}_{2})}{\sqrt{\sum(x_{1_{i}}-\bar{X}_{1})^{2}*\sum(x_{2_{i}}-\bar{X}_{2})^{2 }}} \tag{1}\]  
where \(r_{X_{1}X_{2}}\) represents the relationship between two one-dimensional vectors \(X_{1}\) and \(X_{2}\), to be compared in terms of their correlation. They contain samples window of two variables, \(X_{1}=x_{1_{1}},...,x_{1_{i}}\) and \(X_{2}=x_{2_{1}},...,x_{2_{i}}\), where \(i=1,...,n\) and \(n\) is the number of samples. \(\overline{X_{1}}\) and \(\overline{X_{2}}\) represent the average of samples of each variable vector.  
The coefficient \(r\) measures the degree of linear relationship between two one-dimensional vectors and its results can range from \(-1\) to \(1\) (real numbers, e.g., \(0.9\) is highly correlated and \(-0.9\) is also highly correlated and \(0\) is little correlated). There is a perfect linear relationship (two vectors are increasing or decreasing their values) when the correlation value is \(1\). On the other hand, there is a perfect inverse linear relationship (one vector increases its values while the other decreases its values) when correlation value is \(-1\). There is no linear relationship between two vectors if the correlation value is \(0\) (zero).  
Therefore, when coefficient (r) is close to the highest or lowest value (\(1\) or \(-1\)), then the correlation between two vectors is high. Thus, we can calculate the spatial and temporal correlation of the readings of just one variable between two neighbor sensor nodes [14]. The problem is that we cannot calculate the multivariate spatial correlation by using this method, which is necessary for our solution. However, the next section shows how Euclidian distance is used to identify the multivariate spatial correlation in our solution.  
In addition, we can build a table which determines how much one variable is related to another. The correlation table for variables from real data trace is shown in the next section. Coefficient \(r\) is used to identify what variable is more correlated to another. This highly correlated variable was used to calculate \(\beta\) and \(\alpha\) coefficients of the multiple linear regression and also for data recovery in the sink to which the data was not sent.
# Voltage Optimization in PV-Rich Distribution Networks--A Review
## 4 Voltage Optimization in PV-Rich Distribution Networks--Formulation and Solution Methods
### Computational Intelligence Methods  
In recent years, computational intelligence methods have been increasingly used to solve voltage optimization problems. One of the main advantages of computational intelligence methods is that they do not require a mathematical formulation of the optimization problem. For the sake of unification and formality, the authors keep the formulation of the OPF problem in Table 5 even though it is not required for computational intelligence methods. The most commonly used methods are GA [37; 57; 62; 83] and PSO [64; 82; 85; 88]. Other computational intelligence methods that appear in the literature are the sine-cosine algorithm (SCA) [66], feasibility pump (FP) method [67], sparrow search algorithm (SSA) [74], pattern search algorithm [80], and grey wolf optimization (GWO) [81]. In some papers, hybrid methods are applied. In [79], a GA is applied to solve the day-ahead scheduling optimization problem in the first stage, while the pattern search algorithm (PSA) is used to solve the real-time optimization problem in the second stage. The branch-bound algorithm is combined with the IPM in [86] to solve the discrete problem. The combination of modified PSO and direct load flow (DLF) is used in [75]. DLF is used for power flow analysis and according to obtained data and PSO is used to evaluate network performance. In [77], the authors used both computational intelligence methods and analytical methods. In [55], five multi-objective evolution algorithms (MOEAs), named promising-region-based evolutionary many-objective algorithm (PREA), strength Pareto evolutionary algorithm 2 (SPEA 2), nondominated sorting genetic algorithm II (NSGA-II), nondominated sorting genetic algorithm III (NSGA-III), and two-phase framework (ToP), are used to determine the reactive power capacity of PVs and EVs. The results obtained by MOEAs are used to train a deep deconvolution neural network (DDNN) to solve the problem of voltage deviation and loss minimization. For inverter coordination, the authors in [113] use deep deterministic policy gradient (DDPG).
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 2 Theoretical Foundation
### Bilingual Evaluation Understudy (BLEU)  
As stated by [PERSON] et al. [12], BLEU is an automatic metric for evaluating the quality of translation in comparison to a reference. The metric is language-independent and fast.  
The reference is the correct translation proposed by a human. BLEU scores range from 0 to 1, where a score of 0 indicates no match with the reference, and 1 indicates an exact match. In the evaluation, two approaches are proposed, as outlined in Equations (1) and (2):  
* Modified Precision. The BLEU metric is implemented at the phrase level to obtain precision using \(n\)-grams of the candidate translation and the reference translation. However, precision has some drawbacks, hence the proposal of a modified precision \(P_{n}\).
* Brevity Penalty. In the evaluation of the candidate translation compared to the reference translation, there can be variations in length. This impacts the precision obtained. The candidate translation may have more \(n\)-grams that are not present in the reference translation, resulting in lower precision. Conversely, the candidate translation may be shorter while the reference translation is longer. To address this, the brevity penalty \(BP\) is proposed in Equation (2).  
In Equation (1), the geometric mean of the modified precision of \(n\)-grams \(p_{n}\) is obtained using \(n\)-grams of length \(N\) and the positive weights are incremented by one \(w_{n}\). Here, \(w_{n}=1/N\).  
\[BLEU=BP\cdot exp(\sum_{n=1}^{N}w_{n}\log P_{n}) \tag{1}\]  
Equation (2) is shown below, where \(c\) is the length of the proposed translation, and \(r\) is the length of the reference translation.  
\[BP=\left\{\begin{array}{cc}1&\text{if $\circledright$r}\\ e^{(1-r/c)}&\text{if $c\leq r$}\end{array}\right. \tag{2}\]
# A Review of Deep Learning Applications for Railway Safety
## 4 Railway Infra Safety
### Catenary
[44] studied an image-based deep learning model to check damage in the current-carrying ring of a catenary. RetinaNet [27] was used to detect and classify defects for fault diagnosis. RetinaNet was trained based on the focal loss that mitigates the imbalance between classes of training data, instead of cross-entropy loss. Additionally, RetinaNet contains a spatial attention map (SAM) and a channel weight map (CWM) to harness the spatial characteristics of each feature map and consider patterns in the channel. Performance tests were conducted with catenary images taken at various locations, and the proposed model achieved the best performance in diagnosis accuracy.
## 3 Fn Application and Involvement in Biomaterials and Medical Device Engineering
### Fn in Volume
#### 3.3.2 Fn Covalent Cross-Linking  
Fn has been incorporated using different chemical cross-linking strategies in hydrogels made with natural compounds. First, for example, fibrin hydrogels containing Fn covalently cross-linked by factor XIII and thrombin have been reported [117]. Associated with vitronectin, fibrin-Fn hydrogels have been used as a platform to deliver autologous mesenchymal cells in an emphysema sheep model. The authors demonstrated the capacity of this system to promote tissue regeneration after four weeks [112]. Moreover, [PERSON] et al. reported the benefit of using a fibrin-Fn hydrogel system to specifically bind heparin (Hep) and subsequently improve the presentation of bone morphogenic protein (BMP2) to cells. A fibrin/Fn/Hep-BMP2 hydrogel led to mineralization after 21 days of MC3T3 cell culture and the expression of osteogenic markers (ALP, RUNX2, OPN, OCN, and COL-I). In vivo tests in a rat calvaria critical-sized defect model revealed significantly improved bone regeneration compared to that achieved with a local administration of high-concentration BMP2 [113]. Complementary to the enzymatic natural cross-linking approach described above, other chemical strategies have been explored. For instance, the covalent immobilization of Fn inside hyaluronic acid hydrogels has been performed by a photopolymerization reaction. [PERSON] et al. investigated this method for functionalizing hyaluronic acid hydrogels and showed that encapsulated HUVECs presented increased viability and migrated into aligned structures organized in tubule-like networks [101]. In addition, [PERSON] et al. developed an HA hydrogel that contained Fn covalently cross-linked by thiophene addition mediated by photopolymerization. MSCs seeded on the top of or encapsulated in the HA-Fn hydrogel presented well-defined actin fibers and high cell adhesion compared to in the HA hydrogel [99].
## 4 2.5D Map Representation and Relocation
### Relocation with Proposed 2.5D Map  
In real applications, although the robot already has the environment map, it may usually lose its location. Such a situation happens when the robot starts on an unknown position, or was kidnapped by a human (blocking the sensors and carry it away) while working. Relocation is required, and its speed and the successful rate have a great influence on the practicability of a mobile robot system.  
Currently, for the grid map created by LiDAR-SLAM approaches, Montecalo and particle filter-based method is wildly applied to find the robot pose. However, because the scan data carries too little unique information of the environment, it may take a long time for the robot to find its location. On the other hand, image information is rich enough for fast place finding. In this paper, with the aid of image features and BoW, a fast relocation algorithm with our map is as follows:  
Figure 5: The expression of the proposed 2.5D map.  
Firstly, extract current image features, calculate the bag-of-features (visual words) with BoW\({}_{2}\). Secondly, List all the previous key-frames with poses in map which shares visual words with current image, and find \(n\) best match key-frames with ranking score through BoW searching; Thirdly, for \(n\) best match key-frames, let their poses as initial guess for particles; Finally, apply particle filter based method to find best robot pose, the error function could be formula (6) in Section 3.
## 3 Results
### Electrostatic Force Microscopy of CPDs, CPDs/PUI and C\({}_{60}\)/PU  
Electrostatic force microscopy (EFM) is a very useful method for detecting the charge of a single nanoparticle or nanoparticles dispersed in an insulating polymer matrix or on an insulating surface. This technique enables imaging of the distribution and uniformity of charged carbon-based nanoparticles inside polymers. EFM images can be quantitatively related to charge distributions on a sample surface through mathematical modeling of the tip-sample interaction [35]. Several authors studied the distribution of conductive carbon nanotubes and graphene in a polymer matrix and developed theoretical models that describe the architecture of a polymer composite [36; 37; 38; 39].  
All EFM data were collected from the surface of CPD nanoparticles deposited on freshly cleaved mica by the spin coating method and the surface of CPD/PU and C\({}_{60}\)/PU composite films. Figure 3a,d,g show the top view of AFM images (left column) of CPD nanoparticles and CPD/PU and C\({}_{60}\)/PU composite films, respectively. Figure 3b,e,h present the EFM phase images (right column) of CPD nanoparticles and CPD/PU and C\({}_{60}\)/PU composite films, respectively. Data in Figure 3b indicate the presence of negatively charged carriers--electrons in the core of CPDs (negative phase of electrical signal compared with phase on the surface of mica). The presence of electrons in the CPD core helped us to detect CPD nanoparticles inside the polymer matrix (black voids) in Figure 3e. Similar data with smaller intensities were obtained after EFM microscopy of the C\({}_{60}\)/PU composite film. This result indicates that CPD and C\({}_{60}\) clusters are located in the pores of polyurethane polymer. The intensity of the electrical phase signal was considerably stronger for CPDs/PU than for C\({}_{60}\)/PU, which corresponds to the expected charge distribution. Figure 3c,f,i show the charge distribution profile of CPD nanoparticles, the charge distribution profile of CPD nanoparticles encapsulated in the interior of CPDs/PU and the charge distribution profile of C\({}_{60}\) clusters in the PU polymer film, respectively. The CPDs have much more electrons (phase in the range of 1-4.5 degrees), see Figure 3e, than the cluster of C\({}_{60}\) molecules (range 0.3-0.8 degrees), as shown in Figure 3h, located in a nearly identical polyurethane pore.  
Figure 2: Top view AFM images of neat PU (**a**), CPDs/PU (**b**) and C\({}_{60}\)/PU (**c**) composite films. Scale bar is 400 nm.
## Material and Method
### Data Preprocessing
Since the map of _PROBA-V_ data is small, the adjacent four images need to be spliced by seamless mosaic algorithm before resampling. After resampling, the spatial resolution of all three data is 297.661 m (\(\approx\)0.3 km). To eliminate the abnormal values of the data in the study area, it is necessary to specify a (2, n) array of one or more data ranges for inputting mask data values, where n is the number of data ranges. Each pixel within the data range will not be masked. Then, a unique data value for pixels needs to be specified in the output raster that have a non-zero pixel state value. The next steps involve geometric correction; using the Image Registration workflow geometrically aligns images with different viewing geometry and/or different terrain distortions into the same coordinate system so that corresponding pixels represent the same objects. In order to eliminate the influence of atmosphere on image data, FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction based on the MODTRAN5 radiation transport (RT) model was used in this study. According to the latitude and terrain of the study area, atmospheric model and aerosol model were Sub-Arctic Summer (SAS) and Urban, respectively, and the aerosol retrieval method was 2-band (Kauth-Thomas Transformation). The last step of preprocessing is to mask the raster of SMAP, _PROBA-V_, and _MODIS_ according to the scope of the research area. The advantage is reducing the workload of data processing (masks are used to confine image processing operations to specific areas in the image. A mask has only two types of pixel values: 0 and non-zero. When the mask raster is applied to a source image, the non-zero pixels are processed, and the 0 pixels are ignored when you perform subsequent image processing operations).