esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 5 Discussion  
### Sound Localization First Round: Horizontal Plane  
In line with previous studies, the horizontal plane results analysis demonstrated a notable response in frontal and lateral localization accuracy [35]. The presence of visual features in the designed virtual environment, particularly those positioned at the front, may have also contributed to this positive outcome. Although the test was conducted using the same Ambisonics system within the same laboratory as [PERSON]'s [15], due to different methodology strategies in both experiments, as explained below, it was challenging to conduct a direct statistical comparison. However, in frontal positions, the test from this study showed a slightly better performance than that observed in [PERSON]'s study. Another important result was the non-significant difference at the rear position of 180\({}^{\circ}\) according to the V-test. This statistical result implies a pronounced front-back confusion [36]. This perceptual issue was also observed by [PERSON], who also found a significant front-back confusion tendency at other sound source positions, both in the front and rear. These results are consistent with the spatial resolution limitations of the algorithm, which are determined by the Ambisonics order and loudspeaker density mentioned in other similar studies [37].  
Table 1 illustrates that most responses are concentrated in the expected positions or in their immediate vicinity. This clustering suggests that participants in most of the cases  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Comparison** & **Chi-Squared Statistic** & \(p\)**-Value** \\ \hline
0\({}^{\circ}\) elevation 0\({}^{\circ}\) vs. 35\({}^{\circ}\) & 1.9283 & 0.1649 \\
90\({}^{\circ}\) elevation 0\({}^{\circ}\) vs. 35\({}^{\circ}\) & 13.7125 & 0.0002 \\
180\({}^{\circ}\) elevation 0\({}^{\circ}\) vs. 35\({}^{\circ}\) & 0.8570 & 0.3546 \\
270\({}^{\circ}\) elevation 0\({}^{\circ}\) vs. 35\({}^{\circ}\) & 8.5941 & 0.0034 \\ \hline \hline \end{tabular}
\end{table}
Table 2: MWW test results for the second round.  
Figure 7: Violin plot of sound localization perception in the second round. The distribution of localization responses is shown as a function of the azimuth. It presents the cases with both elevations of 0\({}^{\circ}\) and 35\({}^{\circ}\). The violin plot illustrates the spread of responses per angle, with the shape representing a sideways histogram.  
localized the sound sources accurately, even if not always pointing at the exact expected location. Since angular differences were statistically significant at most positions in the V-test, a consistent performance over the horizontal plane can be assumed. The effective use of 2 nd-order Ambisonics confirmed the benefits of HOA, justifying the hybrid solution developed by combining UE and Max, overcoming UE's 1 st-order limitations, as will be discussed below.
Remote Sens._**2014**, \(6\), 9951-10001; doi:10.3390/rs6109951  
_remote sensing_  
ISSN 2072-4292  
www.mdpi.com/journal/remotesensing  
_Article_  
**Now You See It... Now You Don't: Understanding Airborne Mapping LiDAR Collection and Data Product Generation for Archaeological Research in Mesoamerica**  
**[PERSON] \({}^{1,2,\star}\), [PERSON] \({}^{1,2,\dagger}\), [PERSON] \({}^{1,2,\dagger}\) and [PERSON] \({}^{1,2,\dagger}\)**  
\({}^{1}\) Department of Civil and Environmental Engineering, University of Houston, Houston, TX 77204, USA; E-Mails: [EMAIL_ADDRESS] (W.E.C.); [EMAIL_ADDRESS] (R.L.S.); [EMAIL_ADDRESS] (C.L.G.)  
\({}^{2}\) NSF National Center for Airborne Laser Mapping (NCALM), Houston, TX 77204, USA  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-832-842-8884.  
External Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 29 July 2014; in revised form: 21 September 2014 / Accepted: 8 October 2014 / Published: 20 October 2014_  
**Abstract:** In this paper we provide a description of airborne mapping LiDAR, also known as airborne laser scanning (ALS), technology and its workflow from mission planning to final data product generation, with a specific emphasis on archaeological research. ALS observations are highly customizable, and can be tailored to meet specific research needs. Thus it is important for an archaeologist to fully understand the options available during planning, collection and data product generation before commissioning an ALS survey, to ensure the intended research questions can be answered with the resultant data products. Also this knowledge is of great use for the researcher trying to understand the quality and limitations of existing datasets collected for other purposes. Throughout the paper we use examples from archeological ALS projects to illustrate the key concepts of importance for the archaeology researcher.  
**Keywords:** mapping LiDAR; airborne laser scanning; archaeology; point clouds; digital elevation models; visualization; prospection; Mesoamerica
## 2 Materials and Methods  
### Sampling and Storage  
Xialei Manganese Mine (24\({}^{\circ}\)54\({}^{\prime}\) N, 106\({}^{\circ}\)40\({}^{\prime}\) E), located in Daxin County, Guangxi Zhuang Autonomous Region, is the largest open-pit manganese mine in China covering an area of 20.46 km\({}^{2}\). The region has a subtropical monsoon climate with an average annual temperature of 21.3 \({}^{\circ}\)C, and an average annual rainfall of 1267.5-1479.2 mm. The experimental soil samples included stream sediments (DX), low-disturbance campus soil (XY) and Luban Lake sediment (LB). DX was collected from a stream in the manganese mining area of Xialei Town, Daxin County, Guangxi, which was basically free of disturbance from agricultural and domestic sewage sources in the upper stream. Due to the long-term high manganese loading of manganese mining streams, the sediments contain high levels of manganese-oxidizing bacteria as well as manganese oxides. To initially investigate the specificity of Mn(II) sorption by stream sediments in the manganese mining area, low-disturbance campus soil (XY) and Lake Luban sediment (LB) within Guangxi University, China, were selected as control groups. XY and LB were both obtained from Guangxi University, China. Soil samples were collected from 0-30 cm of sediment, 80-100 cm of the surface, and around the sampling sites. The samples were thoroughly mixed, then stored in polyethylene boxes sealed at 4 \({}^{\circ}\)C.  
Pretreatment of the samples before the experiment required the removal of gravels, plant and animal remains, and trash from the collected sediment and soil samples. Subsequently, the samples were placed in a cool and ventilated place to dry naturally. The soil samples were ground to a fine powder, sieved (100 mesh), and sealed for storage.
## 3 Gauss-Maxwell Beams
### Arbitrarily Polarized Gauss-Maxwell Beams
As it can readily be seen, if the polarization state of the input beam is linear, regardless of its vibration direction (\(\phi=0\) or \(\pi\)), and has not received any extra kick (\(\delta=0\)), then the coupling disappears and we recover the same situation that we had in the previous section for the horizontally polarized input Gaussian beam. Otherwise, if the polarization state is not either horizontal or vertical, the second term in the polarization factor provides an extra spin component that makes the rays to get out the plane where they are initially contained, namely the \(XZ\)-plane or the \(YZ\)-plane. Accordingly, we have a clear description of the energy flux without appealing to a standard picture of rotating electric field vectors, where the electromagnetic energy streamlines already display such a rotation following the spin imprinted by the polarization vector. For instance, in the case of circularly right-handed polarized light (\(\theta=\pi/2\) and \(\phi=-\pi/2\), with \(\delta=0\)), described by the (non-normalized) polarization vector  
\[|P\rangle=|H\rangle-i|V\rangle, \tag{84}\]  
which is the case for which the polarization factor is maximal, we have  
\[\frac{d\Delta x}{dz} = \frac{z\Delta x}{4k^{2}\sigma_{0}^{2}\sigma_{z}^{2}}+\frac{z_{R} \Delta y}{4k^{2}\sigma_{0}^{2}\sigma_{z}^{2}}, \tag{85}\] \[\frac{d\Delta y}{dz} = \frac{z\Delta y}{4k^{2}\sigma_{0}^{2}\sigma_{z}^{2}}-\frac{z_{R} \Delta x}{4k^{2}\sigma_{0}^{2}\sigma_{z}^{2}}, \tag{86}\]  
which can be recast as  
\[\frac{d\Delta\mathbf{r}_{\perp}}{dz}=\mathbb{R}_{\varphi_{z}}\Delta\mathbf{r} _{\perp}, \tag{87}\]  
with \(\mathbb{R}_{\varphi_{z}}\) being the rotation matrix  
\[\mathbb{R}_{\varphi_{z}}=\left( \begin{array}{cc}\sin\varphi_{z}&\cos\varphi _{z}\\ -\cos\varphi_{z}&\sin\varphi_{z}\end{array} \right)=\left( \begin{array}{cc} \cos(\varphi_{z}-\pi/2)&-\sin(\varphi_{z}-\pi/2)\\ \sin(\varphi_{z}-\pi/2)&\cos(\varphi_{z}-\pi/2),\end{array} \right) \tag{88}\]that is, the position vector \(\Delta r_{\perp}\) is acted by a rotation \(\varphi_{z}-\pi/2\).
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 4 Discussion
### Hardware Failure of Information System and Its Causes
#### 4.1.2 Port Failure and Its Causes  
For the effective application and layout of information system communication technology, the computer port can be said to be an important part. If there is a problem with this computer port, it is easy to cause the communication to fail, and the transmission of information data presents a more obvious threat [25]. Combined with the problem of port failure in this computer network communication technology, the main problem is port pollution, which can easily result in a more obvious transmission problem. This kind of port pollution problem is mainly due to the use of different ports for mixed use, which leads to the information system not being able to communicate normally, and finally forms a large interference. In addition, in the specific layout of the port, the application of the corresponding crystal head is unreasonable, which will bring more obvious problems and a greater threat.
# A Survey on the Recent Advances of Deep Community Detection
## 3 Deep Community Detection Strategies
### AE-Based Community Detection Strategies
The graphs are generally represented by adjacency matrices, which store the node connection information but not the proximity information for non-directly connected nodes. The dominant solution to this accuracy problem is the auto-encoder (AE). 3. **Sparsity of network samples:** Network sampling techniques like random walk sampling can be used to to derive vertex sequences as training datasets. However, the sampled data represent only a small portion of all the vertex sequences. Alternatively, these structures can be encoded in a continuous code space. The idea is to adaptively find the optimal representation by adding a sparse constraint to the auto-encoder for this purpose (sparse auto-encoders). An example is the work of [PERSON] et al. [45], which employs a similarity matrix to reveal the indirect connections between nodes and a sparse auto-encoder to lower the dimensions and extract the complex network structures. 4. **Community Overlapping:** The majority of the strategies described do not deal with overlapped communities. This can be a subject of future concern. An example of auto-encoder based community detection scheme is [27]. 5. **Complexity:** The high dimensionality of the feature space of the input data normally increases the number of trainable parameters and makes the models rather complex. Most of the schemes achieve linear time complexity by employing division policies over the network and/or data space to efficiently handle the large data amounts (for examples, see [26; 41]).
# Predicting Road Crash Severity Using Classifier Models and Crash Hotspots
## 4 Machine Learning Models  
Crash severity models involve a non-linear relationship between contributing factors, for which statistical models often become insufficient to explain the inner and intrinsic correlations at the same time; the statistical model usually has some predefined model assumptions, which may lead to an invalid model if violated [71]. Another issue with using a statistical model is determining which explanatory variables to include in the model using large-scale multivariate datasets. Machine learning (ML) models are increasingly used to overcome the limitations of statistical models for estimating the non-linear correlations between crash contributing factors and injury severity [60]. ML models have advantages over statistical models when dealing with outliers and noisy data. The proceeding subsections provide a few details related to the development algorithm of ML models used in this study, namely, random forest (R.F.), X.G. boosting and logistic regression.
## 3 Background Information  
### Reference Frames  
Two reference frames are used in our study: an inertial reference frame \(\{I\}\) and a body\(-\)fixed frame \(\{B\}\)[42]. The origin of \(\{I\}\) is an appropriate position with three axes pointing north, east, and down, respectively. The body\(-\)fixed frame \(\{B\}\) is fixed to a UUV's body and acts as the moving frame. The origin of \(\{B\}\) is fixed at the UUV's center.  
Let \(\phi,\theta,\psi\) present the euler roll, euler pitch, and euler yaw, respectively. The counter-clockwise (CC) rotation of \(\psi\) centered at the \(z\)-axis in \(\{B\}\) is as follows:  
\[\mathbf{R}(\psi)=\left(\begin{array}{cc}\cos(\psi)&-\sin(\psi)&0\\ \sin(\psi)&\cos(\psi)&0\\ 0&0&1\end{array}\right). \tag{1}\]  
The CC rotation of \(\theta\) centered at the \(y\)-axis in \(\{B\}\) is as follows:  
\[\mathbf{R}(\theta)=\left(\begin{array}{cc}\cos(\theta)&0&\sin(\theta)\\ 0&1&0\\ -\sin(\theta)&0&\cos(\theta)\end{array}\right). \tag{2}\]  
The CC rotation of \(\phi\) centered at the \(x\)-axis in \(\{B\}\) is as follows:\[\mathbf{R}(\phi)=\left(\begin{array}{ccc}1&0&0\\ 0&\cos(\phi)&-\sin(\phi)\\ 0&\sin(\phi)&\cos(\phi)\end{array}\right). \tag{3}\]  
The combined rotation matrix is built by multiplying the yaw, pitch, and roll rotation matrices in this order to obtain the following:  
\[\mathbf{R}(\psi,\theta,\phi)=\mathbf{R}(\psi)\mathbf{R}(\theta)\mathbf{R}( \phi). \tag{4}\]  
Figure 1 depicts three euler angles (\(\phi\), \(\theta\), and \(\psi\)). In this figure, there is a spherical UUV, and we plot a cross-shaped propeller at the back of the UUV. Furthermore, this figure depicts the inertial reference frame \(\{l\}\) and the body\(-\)fixed frame \(\{B\}\).
## 3 Results and Discussion
### Principal Component Analysis (PCA) of Biological Activities of GV Extracts  
Principal component analysis (PCA) was performed to analyze the patterns of TPC, TFC, and antioxidant activity in the GV extracts. The principal component1 (PC1) and the principal component2 (PC2) explain 88.3 and 10.8% of the total variance, respectively (Figure 3a). PC1 was mainly related to TPC and ABTS activity and to the horizontal axis variance of the GV extracts. APX activity, GVW, and GVM were distributed in the positive direction of the PC2 axis, and FRAP activity and GVE were distributed in the negative direction of the PC2 axis. As shown in Figure 3b, the arrow exists in the opposite direction between GVW and TPC, TFC, and antioxidant activity, suggesting low antioxidant activity. GVM and GVE showed that the arrows existed in equal directions, indicating that GVM and GVE had high levels of TPC, TFC, and antioxidant activity.  
Furthermore, the variable importance in the projection (VIP) score was analyzed to determine the variables affecting the separation of GV extract groups. The VIP scores for  
Figure 3: **(a)** Score plot and **(b)** biplot of PCA of various solvents extracted from _Gracilaria verrucosa_ for antioxidant determinants. GVE: _G. verrucosa_ ethanol extract; GVM: _G. verrucosa_ methanol extract; GVW: _G. verrucosa_ water extract; TPC: total polyphenol content; TFC: total flavonoid content.  
the ABTS, DPPH, and FRAP activities were 1.64, 1.40, and 1.32, respectively (Figure 4a). Among the biological activities, ABTS, DPPH, and FRAP activities were confirmed as factors that distinguished the GV extracts using different extraction solvents. The extract of the red seaweed _G. changii_, which contains polyphenol compounds and has excellent radical-scavenging activity, showed a strong positive correlation with TPC, TTC, and DPPH activity [62]. Similarly, GV extracts exhibited significantly high positive correlations (r = 0.760-0.982, \(p\) = 0-0.018) with TPC and SOD, CAT, and ABTS activity (Figure 4b). These results revealed that the high antioxidant activity of the GV extracts could be attributed to their total polyphenol content.
## 5 Result and Discussion  
### Heat Rate and Exposure Time  
In this research, three cases of heating rate were considered to analyse the effect of temperature exposure time on modal parameters. In the first case, the beam temperature was allowed to increase in a slow and steady rate of 2 \({}^{\circ}\)C/min. This enabled the beam to be exposed to the temperature for a longer duration in order to reach the test temperatures. In the second case, the temperature was increased at the rate of 5 \({}^{\circ}\)C/min. This heating rate was considered as a moderate rate to reach the required temperature for the experiments. In the third case, the beam temperature was allowed to increase rapidly at the rate of 8 \({}^{\circ}\)C/min. This controlled rate of heating the beam was achieved by using a PID temperature controller. Therefore, the temperature along the beam was controlled to meet the experimental requirements. Figure 7a-d presents the time required for all the three cases for the temperature of 50, 100, 150, and 200 \({}^{\circ}\)C, respectively. The graphs show that the same temperature was achieved at different times. This means the increase in temperature at the rate of 2 \({}^{\circ}\)C/min takes more time to heat the beam for all isothermal conditions. The heating at the rate of 2 \({}^{\circ}\)C/min (i.e., slow heat rate) may adversely affect the material properties. It takes more time to reach the required temperature compared to medium and fast heating rates. Therefore, during a slow heating rate, the heat accumulates on the beam for a longer duration and hence has a tendency to influence the material properties more such as reducing the stiffness.
## 5 Security Analysis
### Uniformity Analysis  
The uniformity metric is an indicator for the balance of '0s and '1's in the PUF response bits. Ideally, both binary values should occur equiprobable in a PUF response [32]. For PUF instance \(n\), having an \(L\)-bit (\(L\in\mathbb{N}\)) response \(R_{n,l}\) at bit position \(l\), the uniformity is defined as:  
\[U_{n}=\frac{1}{L}\sum_{l=0}^{L-1}R_{n,l}\times 100\%. \tag{10}\]  
For our analysis, we used the reference responses of 30 fabricated PUF cores measured in our uniqueness evaluation. We computed the uniformity value for each PUF core and response with respect to Equation (10). The resulting mean uniformity is 45.60%, which is close to the ideal value of 50%. Figure 8 shows the box plot for all measured PUF responses. In general, a uniformity value lower than 50% means that the response is biased towards 0. The results for the uniformity are consistent with our analysis on the analog voltage level, where the mean value for the comparator input voltage difference is slightly negative, as shown in Figure 5a.  
Identification, which is one subgroup in the field of security, mainly relies on unique identifiers. In the ideal case, the entire binary address space for a given bit width can be used for distinction between single instances. In this context, the uniqueness metric shows how distinguishable the PUF responses are. In the context of cryptographic key derivation, the PUF response bits should be uniformly distributed. The greater the deviation from 50%, the easier to guess the response by an attacker. Our results show that the mean uniformity is around the ideal value. However, there are some PUF responses that are non-uniform, which makes the proposed PUF's raw responses a candidate for identification applications, rather than for deriving cryptographic keys.
## 7 Simulation Environment
### Energy Efficiency Calculation  
The energy efficiency is the remaining energy at the end of the network data transmission. Figure 10 compares the energy efficiency performance of the nodes for various protocols namely GA-LEACH, MW-LEACH, CSOGA, and MWCSGA. For this scenario, the simulation experiment is carried using 100 nodes. The energy efficiency performance of MWCSGA protocol is 79.32%. Whereas the earlier protocols such as GA-LEACH, MW-LEACH, and CSOGA protocols produces energy efficiency as 57.11%, 65.38%, and 71.34%, respectively. According to analyzes it proves that the performance of MWCSGA protocol performs better when compared with the others.
## 2 Notes on the Theory, Method and Data
### Data: Remote Sensing and Terrestrial
#### 2.4.3 Positions of Known Oil and Gas Deposits  
First, we take data/plots (and geodetic coordinates where available) showing locations (ground positions) of oil and gas deposits (discovered and planned for drilling or already in use) from the literature (see Figures 3 and 4). Then (Section 3), we correlate these places with the maps of the strike angles computed from EIGEN 6C4. We already started with such a work in Section 7.3.2 of [1].  
During approximately the last ten years, a series of large natural gas discoveries in the Eastern Mediterranean has changed how energy is used in the region (e.g., [20]). Israel now has the potential to export gas. In the region, the most significant discoveries were the Tamar field (Israel), Leviathan fields (Israel), Aphrodite (Cyprus), Zohar (Egypt), plus delta of the Nile (Nile Margin Reservoirs) or Suez (Egypt) [21] (their figure 1, plus various internet sources).  
Figure 3: The strike angles (for \(I<0.3\)) overlapping a map composed from various sources about the gas fields in the Eastern Mediterranean. According to [21] and various internet sources. Redrawn composition/summary from several public domains.
# Markov Chain Model-Based Optimal Cluster Heads Selection for Wireless Sensor Networks
## 5 Performance Evaluation  
We execute a series of simulations to analyze and evaluate the performance of MOCHs. We presume that all the sensor nodes deployed in the sensing field have the same size of data, the data of block size are 4000 bits. We take network size of 100 m \(\times\) 100 m in which 100 nodes are randomly distributed. The BS is placed outside the sensing area in any arbitrary position. We take different scenarios in which nodes with different intensity are deployed in the network. We also take different initial energies of the sensor nodes to check the strength and bounds of our designed framework. We used five different evaluation measures which are very famous in WSN literature and employed to check the performance of clustering protocols. The quality measures that we engaged in the evaluation are: the lifetime of the network, stable and unstable region in the lifetime of the network, throughput of the network, the number of CHs in the network, and the transmission time of the network. Simulation parameters used for our experiments are given in Table 1 and the performance measures used for simulations are described in the next subsection.