esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 4 Experiments and Discussion
### Comparison Feature Algorithms  
As reviewed in [34], there are many feature selection methods. We conducted comparison experiments between our MVFS and several typical feature selection algorithms, namely Fisher score (F-Score), mutual information (MI), joint mutual information (JMI), joint mutual information maximization (JMIM), Relieff, Hilbert-Schmidt independence criterion lasso (HSIC Lasso) [35] and recursive feature elimination (RFE). F-Score ranks features through variance similarity calculation, and the same rank can be obtained by analysis of variance (ANOVA). MI ranks features according to values of their mutual information with class labels. JMI and JMIM are developed from MI [36]. RFE ranks and discards features after training a certain kind of classifier. Starting from all features, the elimination process continues until the feature number or output error is settled to a minimum.  
The above algorithms are developed for single-view learning and can be used in BSV, FCFS and FSFC directly. Except for RFE, all of them are filter feature selection approaches, as is HMVFS. Besides, the comparison algorithms designed for multiview learning are kernel canonical correlation analysis (KCCA) [24] and discriminant correlation analysis (DCA) [37]. These feature extraction approaches map multiview data into a common feature space so their results are attached to the comparison in FCFS. As for the proposed algorithm, there are two hyperparameters in HMVFS. In the experiments, these hyperparameters \(\alpha\) and \(\beta\) were tuned ranging in \([10^{-2},10^{-1},\,1,\,10,\,10^{2},\,10^{3}]\) through grid search on the training sets. Moreover, experiments without any feature algorithm were conducted using BSV features and all features, tabbed as RAW_BSV and RAW.
## 3 Samples and Methods
### Analytical Methods
#### 3.2.6 Chemical Form Analysis of Gold in Soil  
The determination of chemical forms of gold in soil samples refers to the analytical specifications of \"Ecological Geochemistry Assessment Sample Analysis Technical Requirements (Trial)\" (DD2005-03) [49]. The [PERSON] (1979) modified sequential extraction method [50] (seven-step procedure) is employed for the chemical forms of gold in the soil. This technique divides elements into seven forms: extraction with water for the water-soluble form; 1.0 mol/L magnesium chloride extraction for the ion-exchangeable form; 1.0 mol/L sodium acetate solution for the carbonate-bound form; 0.1 mol/L sodium pyrophosphate extraction for the weakly organic (humic acid) bound form; 0.25 mol/L hydroxylamine hydrochloride--0.25 mol/L hydrochloric acid mixed solution extraction for the iron-manganese bound form; 30% hydrogen peroxide extraction for the strongly organic bound form; and hydrofluoric acid extraction for the residual form. After quantitatively collecting samples and performing extractions with the solvents above, the gold content in each form is analyzed using the ICP-MS produced by Thermo Fisher Scientific, Waltham, MA, USA, for a full spectrum direct reading analysis.
## 3 Dimensional Analysis
### Application of Numerical Analysis
#### 3.3.2 Validation of Superelevation in the Numerical Model  
In this study, superelevation occurring in the curved section was simulated using the three-dimensional model, and the theoretical equations based on Equations (7) and (9) were compared with the results. Figure 6 shows the water surface elevation distribution along the curved section of the channel under overtopping conditions. The figure highlights the superelevation effect, where the outer bank exhibits a higher water level due to the centrifugal force generated in the curved flow. This pattern is consistent with theoretical expectations and previous experimental observations, supporting the accuracy of the numerical simulation in reproducing curved flow dynamics. Notably, the gradient across the cross section visually confirms the presence of secondary flow effects, which contribute to the asymmetric water surface profile.  
Figure 7 displays a graph comparing the theoretical results with the simulation outcomes. The simulation results from FLOW-3D fell within the superelevation coefficient range of 0.5 to 1.0 as presented by the ADWR [30]. Additionally, when compared with the equations proposed by the KWRA [31], the average error rate of the simulation results was approximately 3%. This error rate was calculated based on the difference between the simulation results and the theoretical superelevation values obtained using a coefficient (C) of 0.75, which corresponds to the trapezoidal cross-section shown in Figure 4. The superelevation coefficient (C) varies depending on the cross-sectional shape and typically ranges between 0.5 and 1.0. The selection of C = 0.75 ensures consistency between the theoretical estimation and the model configuration. This indicates that the three-dimensional FLOW-3D model can appropriately reproduce flow conditions in river curves.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Location** & **Boundary Condition** & **Location** & **Boundary Condition** \\ \hline X Min & Wall & X Max & Outflow \\ Y Min & Flowrate & Y Max & Wall \\ Z Min & Wall & Z Max & Pressure (fraction = 0) \\ \hline \end{tabular}
\end{table}
Table 1: Boundary condition of the model.  
Figure 6: Distribution of water surface elevation in river curve.
# BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
## 2 Setup and Collection Procedure
Each collection campaign was performed three times, each time using a different smartphone and by a different individual. The smartphones were a BQ Aquaris X5 plus, a Samsung Galaxy S6 (SM-G920F) and a Samsung Galaxy A5 2017 (SM-A520F), hereinafter BQ, S6 and A5, respectively. Figure 1: Examples of the collection application operation: (**a**) Moving through the collection points, (**b**) Samples collection. In the Geotec zone (Figure 3a) there are only four tall furnitures and the beacons were attached to the ceiling tiles (Figure 3b). As a result, every collection point had line-of-sight situations with more than three beacons at the same time, if human-body blockage is not considered. The collection was performed following one campaign (with the subject facing the up or down directions) and one smartphone of the those used for the Library zone. The campaign was performed three times, each time with the beacons configured for a different transmission power. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Geotec** & **Library** \\ \hline number of beacons & 22 & 24 \\ number of samples & 2652 & 2100 \\ number of campaigns & 1 & 2 \\ smartphones & A5 & A5,BQ\_S6 \\ involved subjects & 1 & 3 \\ consecutive collection samples & 13 & 6 \\ transmission powers (dBm) & \(-\)20,\(-\)12,\(-\)4 & \(-\)20 \\ area (m\({}^{2}\)) & 151.07 & 176.02 \\ area covered by tall obstacles (m\({}^{2}\)) & 8.70 & 68.73 \\ area covered by samples (m\({}^{2}\)) & 137.51 & 121.65 \\ area covered by beacons (m\({}^{2}\)) & 138.08 & 172.9 \\ MDMS (m) & 2.34 & 1.79 \\ MDMB (m) & 3.07 & 3.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: General description of each collection zone. Figure 2: 5 th floor Library zone. In (**a**), green diamonds and the magenta asterisk represent the position of beacons and the local coordinates origin, respectively. The orange circles and blue squares represent the collection locations where the subject faced the left–right directions and those where they faced the up-down directions, respectively. The pale blue rectangles represent bookshelves. In (**b**), a shot that shows how beacons were deployed inside the top of bookshelves.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 4 Wireless Technologies for Industrial Applications
### Long Range Multi-hop Networks
#### 4.2.1 WirelessHART  
Along with the PHY, MAC, network and transport layer, the WirelessHART protocol stack also includes the application layer. The application layer is HART, which is compatible with existing wired HART solutions.  
Both WirelessHART and ISA100.11a combine TDMA and frequency hopping at the MAC layer. To access the channel, WirelessHART uses a two-dimensional matrix consisting of time slots and 15 available channels. Time slots are grouped in superframes which periodically repeat throughout the entire network lifetime. Variable-length superframes are also supported and at least one superframe must be enabled. Superframes can be added or removed during the operation of the network and they are managed by the network/system manager. Time slot duration is fixed to 10 ms in WirelessHART. This scheme allows multiple devices to transmit data at the same time using different channels. However, a single device can only make use of a single channel per time slot. WirelessHART's (single) time slotted channel hopping mechanism also identifies noisy channels and blacklists them,thereby greatly improving the network reliability. In addition, this technology can achieve up to 99.999% reliability by supporting mesh topologies [43]. Security in WirelessHART communication is provided by the 128-bit Advanced Encryption Standard (AES) authentication.  
WirelessHART is the oldest and the most experimentally evaluated wireless solution for IIoT [44; 45; 46; 47]. A study has demonstrated control over WirelessHART with the update rate of 20 ms [45]. However, this and similar studies evaluate the simplest variant of a WirelessHART network comprising of a sensor, an actuator, an AP, a gateway (GW) and a network manager. Such a low update rate is achievable only in star topologies and with very short superframes that disable scaling the network up. Even though not more than 5 hops are advised in industrial networks, scaling WirelessHART is possible (1) using multiple APs and (2) using several WirelessHART gateways connected to a HART-over-IP backbone. This is very much needed in dense industrial environments as only 8 devices with 500 ms reporting period can be supported by a single AP [48]. In practice, a single WirelessHART AP can support 25, 50, 80 or 100 devices at 1 s, 2 s, 4 s and 8 s reporting period respectively. Both bidirectional (cyclic) traffic and an increment in hops doubles the latency or halves the number of nodes. Hence, WirelessHART is eligible for use in classes 2-6 of industrial systems, although it is limited either by latency or by scale.
## 2 Improved RRT Algorithm
### Collision Detection  
In the path planning of the improved RRT method, the geometric dimensions of vehicles and obstacles are considered. In this paper, the geometric dimensions of vehicles and obstacles were abstracted into rectangles. The separation axis theorem was used to transform the collision detection problem between vehicles and obstacles into the intersection test problem of different rectangles [25]. The principle of the separation axis is to project the convex polygon onto a vector separation axis to see if the projections of the two polygons overlap. If they do not overlap, the projections of the objects on the separation axis do not overlap each other, and there is no collision between these two objects [26].  
The schematic diagram of the separating axis theorem is shown in Figure 6: the two rectangles are in the global coordinate \(xoy\), and rectangles \(A\) and \(B\) represent vehicles and obstacles, respectively. The unit vectors of rectangle \(A\) in the local coordinates with \(A_{o}\) as the origin are \(A_{x}\) and \(A_{y}\). The unit vectors of rectangle \(B\) in the local coordinates with \(B_{o}\) as the origin are \(B_{x}\) and \(B_{y}\). The length of rectangle \(A\) is \(L_{a}\), the width is \(W_{a}\), the length of rectangle \(B\) is \(L_{b}\), the width is \(W_{b}\), \(T\) is the distance between the center point of rectangle \(A\) and the center point of rectangle \(B\). \(\mu\) is the heading angle of the vehicle in the global coordinate system. \(\varphi_{1}\) is the angle between the center line of the two rectangles and the \(X\) axis of the global coordinate system.  
Figure 5: Extended schematic.  
There are four separation axes \(L\), which are the \(x\) and \(y\) axes of the local coordinates of the \(A\) and \(B\) rectangles. To detect whether the two rectangles collide, you only need to determine whether the projections of the two rectangles on all the separation axes meet all the following conditions.  
Condition 1: the projection axis is the \(x\) axis of the \(A\) rectangular local coordinate system, which is:  
\[T|cos\varphi_{1}|>\frac{L_{a}}{2}|cos\mu|+\frac{W_{a}}{2}|sin\mu| \tag{8}\]  
Condition 2: the projection axis is the \(y\) axis of the \(A\) rectangular local coordinate system, which is:  
\[T|sin\varphi_{1}|>\frac{L_{a}}{2}|sin\mu|+\frac{W_{a}}{2}|cos\mu| \tag{9}\]  
Condition 3: the projection axis is the \(x\) axis of the \(B\) rectangular local coordinate system, which is:  
\[T|cos(\varphi_{1}-\mu)|>\frac{L_{b}}{2}|cos\mu|+\frac{W_{b}}{2}|sin\mu| \tag{10}\]  
Condition 4: the projection axis is the \(y\) axis of the \(B\) rectangular local coordinate system, which is:  
\[T|sin(\varphi_{1}-\mu)|>\frac{L_{b}}{2}|sin\mu|+\frac{W_{b}}{2}|cos\mu| \tag{11}\]  
If the above four determination conditions are met at the same time, it can be determined that the vehicle and the obstacle will not collide.
# Boosting of Denoising Effect with Fusion Strategy
## 4 Experiments
### Visual Comparison with Other State-of-the-Art Algorithms
Figure 7: Comparison of denoising results of the competing methods for image _Monarch_ with noise level \(\sigma=50\). (**a**) Original image. (**b**) Noisy image. (**c**) NCSR, PSNR = 25.69 dB. (**d**) WNNM, PSNR = 26.22 dB. (**e**) PGPD, PSNR = 25.97 dB. (**f**) BM3D, PSNR = 25.64 dB. (**g**) DnCNN, PSNR = 26.65 dB. (**h**) TWSC, PSNR = 26.16 dB. (**i**) FFDNet, PSNR = 26.65 dB. (**j**) Proposed, PSNR = **26.77** dB. original image to the greatest extent and suppresses almost all the noise, even at a high noise level. One of the reasons for this is that our weight map can incorporate the well-denoised pixels into the final result.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 3 SatSINS
### Module 1: Landfast Ice Thickness Estimation and Large-Scale Sea Ice Analysis  
#### 3.1.1 Analytical Ice Model for Fast Ice Thickness  
The land-fast ice thickness was calculated prior to ice navigation. This was usually finished before November 20 when RV _Xuelong_ departed from Australia after resupply. Since land-fast ice growth is a predominantly thermodynamic process, its thickness can be estimated using the continuity of heat flux from the underlying water to the atmosphere through the ice and snow layers. Assuming linear temperature profiles through the ice thickness and snow-free ice surface, the ice growth rate for a given thickness \(H\) is given by [PERSON]'s law [(20)]. In this study, a modified version considering ice-atmosphere coupling [(21)] was used. The density of sea ice, the latent heat of freezing of sea ice, the thermal conductivity of sea ice, and the heat exchange coefficient were set to 910 Kg/m\({}^{3}\), 333.4 KJ/Kg, 2.2 W/(m\({}^{\circ}\)C), and 0.73 W/(m\({}^{\circ}\)C); respectively. The daily air temperature was obtained from AWS at Zhongshan Station. In agreement with observations of the freeze-up near Zhongshan Station [(14)], the calculation is initiated on 1 March with zero thickness. It should be noted that the above-mentioned model is based on the assumption of snow-free surface and zero ocean heat flux. Both assumptions  
Figure 3: Flow chart of the SatSINS system with its three modules. SatSINS: satellite-based sea ice navigation system; MODIS: moderate-resolution imaging spectroradiometer; BNU: Beijing Normal University; MSLP: mean sea level pressure; SSMIS: Special Sensor Microwave Imager/Sounder.  
lead to conservative estimates of ice thickness (i.e., over prediction), which is favorable for the current application. Snow cover insulates the ice surface and thereby results in slower ice growth rate and overestimation of the thickness.
# Reduced-Complexity Tracking Algorithms on Chip for Real-Time Location Estimation
## 5 The Proposed VLSI Architecture and Implementation
### Computation of Kalman Gain and \(\alpha\)-\(\beta\) Module  
As shown in Figure 6, this module is used to calculate the weight value, which is calculated from the state noise distribution (\(\mathbf{\overset{\kappa}{P}}_{k}\)) and measurement noise (covariance \(\mathbf{R}\)). This module involves the inverse matrix, which represents a high degree of complexity in hardware. Therefore, the inverse matrix encounters huge difficulties in this step from software to hardware, and it also has to overcome this cumbersome calculation. After the calculation of KG, the threshold will be judged by DKF or PKF. If it meets the raft value, and the \(\alpha\)-\(\beta\) filtering algorithm will be started.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 2 Spherical Sector Harmonics
According to (2) and (4), the definition of SSHs is similar to the definition of SH but contains the shifted associated Legendre polynomials \(\mathcal{P}_{n}^{m}(q_{1}\text{cos}\theta+q_{2})\) and scaled exponential functions \(e^{imu\phi}\) rather than the associated Legendre polynomials \(\mathcal{P}_{n}^{m}(\text{cos}\theta)\) and exponential functions \(e^{jm\phi}\). SH has been successfully implemented in the spatial audio and acoustics field, but the following limitations of SSHs can hinder their further application. Firstly, to preserve the orthogonality of the associated Legendre polynomials and exponential functions in the original SH, SSHs replace these functions with shifted associated Legendre polynomials, \(\mathcal{P}n^{m}(q1\text{cos}\theta+q_{2})\), and scaled exponential functions, \(e^{jmu\phi}\). This replacement effectively maps the sector region in SSHs back to the entire sphere in SH. However, this mapping process violates the Helmholtz equation in both the elevational and azimuthal directions, as demonstrated in Section 3. Secondly, due to this violation of the Helmholtz equation, \(R_{n}(kr)\) no longer accurately represents the variation in and propagation of the sound field in the radial direction. As a result, \(R_{n}(kr)\) cannot be used to extend the sound field from one measured sector to another sector with the same angular range of \(\theta\) and \(\phi\) but a different radius. Additionally, the SSH coefficients, \(\Gamma_{n}^{m}(r,k)\), become radius-dependent, making radial extrapolation with SSHs challenging and impractical for direct application. Further details are provided in Section 4. Third, the mapping process of SSHs can cause severe distortion around the edge of the sector region when using (3) to reconstruct a sound field. For a sector region \(\mathbb{T}\) (\(\theta\in[\theta_{1},\theta_{2}],\phi\in[\phi_{1},\theta_{2}]\)), the shifted associated Legendre polynomial maps two latitudinal edges of the sector region (\(\theta=\theta_{1},\phi\in[\phi_{1},\phi_{2})\) or \(\theta=\theta_{2},\phi\in[\phi_{1},\phi_{2})\)) to two poles of the sphere (\(\theta=0,\phi=0\) or \(\theta=\pi,\phi=0\)), and the scaled exponential function maps two longitudinal edges of the sector region (\(\theta\in[\theta_{1},\theta_{2}]\), \(\phi=\phi_{1}\) or \(\theta\in[\theta_{1},\theta_{2}]\), \(\phi=\phi_{2}\)) to one longitude line of the sector region (\(\theta\in[\theta_{1},\theta_{2}]\), \(\phi=0\)). Due to the mapping process, the information of the sound field over the edge of \(\mathbb{T}\) is lost, and the reconstructed sound field has severe distortion around the edge. The distortion problem is further investigated in Section 5.
# Improved Likelihood Probability in MIMO Systems Using One-Bit ADCs
## 4 Proposed Method
### Proposed Method  
Because the optimized weights cannot be derived easily from the expectation form in (15), the expectation form is replaced using the realized samples. When \(n\) realized samples of likelihood probability are available, the optimization problem can be expressed as:  
\[(\alpha,\beta) =\operatorname*{argmin}_{\alpha,\beta\in[0,1]}\mathbb{E}\{\|p_{r, k}-\hat{p}_{r,k}(\alpha,\beta)\|^{2}\},\] \[=\operatorname*{argmin}_{\alpha,\beta\in[0,1]}\sum_{m\leq n}\|p_ {r,k}[m]-\alpha p_{r,k}^{pi}[m]-\beta p_{r,k}^{da}[m]\|^{2}, \tag{16}\]  
where \(p_{r,k}^{i}[m]\) is the \(m\)-th realized sample of the likelihood probability obtained at the receiver. The optimization problem in (16) is suboptimal because the weights are obtained from a limited number of realized samples whose values are incorrect. However, as \(n\) increases, the optimization problem is approximated as that in (15) because the values of the realized samples converge.  
For mathematical tractability, matrix \(\mathcal{A}_{n}\in\mathbb{R}^{n\times 2}\) and vector \(\mathcal{B}_{n}\in\mathbb{R}^{n\times 1}\) are defined from the realized samples as follows:  
\[\mathbf{A}_{n} =\left[[p_{r,k}^{pi}[1],\ldots,p_{r,k}^{pi}[n]]^{T},[p_{r,k}^{da} [1],\ldots,p_{r,k}^{da}[n]]^{T}\right],\] \[\mathbf{b}_{n} =[p_{r,k}[1],\ldots,p_{r,k}[n]]^{T}, \tag{17}\]  
where \(\mathbb{R}\) is a real number.  
Using (17), the optimization problem can be expressed in the matrix form as follows:  
\[(\alpha,\beta)=\operatorname*{argmin}_{\alpha,\beta\in[0,1]}\hskip-10.0 pt\left\| \mathbf{b}_{n}-\mathbf{A}_{n}\right[\left.\begin{array}{c}\alpha\\ \beta\end{array}\right\|\hskip-10.0 pt\left\|^{2}. \tag{18}\]  
This optimization problem is well-known least-squares optimization [36]. From the least-squares solution, the optimal value \((\alpha^{\star},\beta^{\star})\) of (18) can be derived as:  
\[(\alpha^{\star},\beta^{\star})=(\mathbf{A}_{n}^{T}\mathbf{A}_{n})^{-1}\mathbf{ A}_{n}^{T}\mathbf{b}_{n}. \tag{19}\]  
The optimal weights of \((\alpha^{\star},\beta^{\star})\) converge to the optimal weights in (15) as \(n\) increases.
## 4 Performance Evaluation in Edge Scenarios
### Performance in Network Programming  
In the context of IoT, the performance of network programming directly influences the speed of data exchange, thereby impacting application performance. This study simulated a scenario where a confidential server based on SGX was deployed remotely, and by adjusting the client load, server throughput and latency were observed to assess the impact of SGX on network programming performance. Lighttpd [68], a lightweight HTTP server, was enabled on the server side, while ApacheBench [69] was used on the client side to adjust the load. The simulation test was conducted by continuously downloading files from the server. The Lighttpd server version used was 1.4.40, and the ApacheBench version was 2.3. To simplify the experiment, both the server and the client ran on the same test machine, with the download address set to the local network 127.0.0.1 and port 8004. The test focused on two main aspects: first, testing multi-threaded performance by examining changes in system throughput as concurrency increases; and second, testing single-threaded performance by analyzing how latency changes as the size of the downloaded file increases. Since SGX SDK does not support system calls and cannot directly access ports, the experiment was conducted using SGX implementations based on LibOS, namely Gramine and Occlum.
## 7 Results and Discussion
### Results
#### 7.2.4 Average End-to-End Delay  
Our proposed scheme has the best CHs selection, which does not need a communication overhead and decreases transmission of redundant information to cluster heads (CHs). The average end-to-end delay of our proposed schemes is minimal compared to FBCFP, TEEN and LEACH as shown in Figure 11a,b with different mobility speeds of nodes. It is because of the early identification of the next hopping node and the remaining energy of nodes for better route selection. While, in the other schemes, the cluster head (CH) selects the same route for data transmission in each round, leading to more congestion and delay. The proposed schemes CRP-GR and CRP-G decrease the average end-to-end delay by 9% and 5%, respectivley, as compared to FBCFP, 18% and 11% as compared to TEEN, and 25% and 17% as compared to LEACH.