esa-sceva/satcom-chunk-collection huggingface dataset:
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 2 Related Work
### Trace Ratio Problems  
As presented in the above subsection, within the context of graph embedding, the dimensionality reduction methods can be viewed as trying to obtain the transformation matrix \(\mathbf{W}\) that makes \(Tr(\mathbf{W}^{T}\mathbf{S}_{p}\mathbf{W})\) maximum and \(Tr(\mathbf{W}^{T}\mathbf{S}_{i}\mathbf{W})\) minimum. This is often formulated as a trace ratio optimization problem, that is \(\max_{\mathbf{W}}Tr(\mathbf{W}^{T}\mathbf{S}_{p}\mathbf{W})/Tr(\mathbf{W}^{T} \mathbf{S}_{i}\mathbf{W})\)[33]. Generally, there are two kinds of solutions for this problem: (1) Simplifying the problem into a ratio trace problem: \(\max_{\mathbf{W}}Tr[(\mathbf{W}^{T}S_{i}\mathbf{W})^{-1}(\mathbf{W}^{T}S_{p} \mathbf{W})]\), then using generalized eigenvalue decomposition (GED) to obtain the transformation matrix \(\mathbf{W}\); (2) Directly optimizing the objective function through an iterative procedure, with each step presented as a trace difference problem: \(Tr[(\mathbf{W}^{T}(\mathbf{S}_{p}-\lambda^{n}\mathbf{S}_{i})\mathbf{W})]\). However, for the first solution, the optimization of ratio trace formulation may deviate from the original objective, which results in a closed-form but inexact solution and may subsequently lead to uncertainty in subsequent classification or clustering problems. For the second solution, [PERSON] et al. [33] propose an efficient iterative procedure by solving the trace difference problem in each iterative step, named iterative procedure (ITR). It is proven that ITR could converge to the optimal solution and solve the trace ratio problem. With the orthogonal assumption on the projection matrix, objective function of the ITR optimization can be defined as  
\[\mathbf{W}=\underset{\mathbf{W}^{T}\mathbf{W}=\mathbf{I}}{\operatorname{ argmax}}\frac{\operatorname{tr}(\mathbf{W}^{T}\mathbf{S}_{p}\mathbf{W})}{ \operatorname{tr}(\mathbf{W}^{T}\mathbf{S}_{i}\mathbf{W})}. \tag{2}\]  
In [34], [PERSON] et al. address the graph-based feature selection framework using the iterative process of the trace ratio problem. In [35], [PERSON] et al. analyze the iterative procedures for the trace ratio problem and prove necessary and sufficient conditions of the existence of the optimal solution of trace ratio problems, which are that there is a sequence \(\{\lambda^{*}_{1},\lambda^{*}_{2},\dots,\lambda^{*}_{n}\}\) that converges to \(\lambda^{*}\) as \(n\rightarrow+\infty\), where \(\lambda^{*}\) is the optimal value of Equation (2). Based on these previous works, we also formulate RFA as a trace ratio problem and theoretically prove the convergence of its optimization algorithm.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 5 Performance Results and Evaluation  
The system was tested in a controlled environment, simulating various fire scenarios to evaluate its performance. Key performance metrics for response time, network reliability, and scalability were analyzed.
## 2 Methodologies  
### Maximum Correntropy Criterion  
The similarity metric for signals that are nonlinearly projected into a feature space is called correntropy. Correntropy, in its simplest form, extends the autocorrelation function to nonlinear spaces. The two random variables X and Y with their joint distribution function can be expressed as  
\[V_{\sigma}(\textbf{X},\textbf{Y})=E[\kappa_{\sigma}(X,Y)]=\int\kappa_{\sigma} (x,y)dF_{X,Y}(x,y) \tag{1}\]  
where \(\kappa_{\sigma}(x,y)\), a positive definite kernel function that accomplishes the Mercer theory, is the joint distribution of \(F_{X,Y}(x,y)\), \(f_{XY}(x,y)\) is the joint probability density function, and \(E[\cdot]\) denotes the expectation operator. The kernel function uses the Gaussian kernel unless otherwise specified; that is,  
\[k_{\sigma}(x,y)=G_{\sigma}(e_{i})=\frac{1}{\sqrt{2\pi\sigma}}\exp\left(-\frac{ e_{i}^{2}}{2\sigma^{2}}\right) \tag{2}\]  
where \(e_{i}=x_{i}-y_{i}\), \(\sigma>0\) is an expression for kernel bandwidth. We could obtain very little data in most real situations, and the combined distribution for \(F_{X,Y}(x,y)\) is typically unavailable. The joint distribution is typically unknown because there is only a limited amount of data available in most real-world scenarios. In these situations, the correntropy can be calculated as follows using a sample mean estimator:  
\[\hat{V}_{\sigma}(X,Y)=\frac{1}{N}\sum_{i=1}^{N}G_{\sigma}(e_{i})=\frac{1}{N} \sum_{i=1}^{N}\kappa_{\sigma}(x_{i}-y_{i}) \tag{3}\]  
where \(\{x_{i}-y_{i}\}_{i=1}^{N}\) is the \(N\) number of data drawn from the joint distribution function. The sample mean square can be used to estimate the correntropy and solve this problem. Using the Taylor series expansion method for Gaussian kernel, we obtain  
\[V_{\sigma}(X,Y)=\sum_{n=0}^{\infty}\frac{(-1)^{n}}{2^{n}\sigma^{2n}n!}E\Big{[} (X-Y)^{2n}\Big{]} \tag{4}\]  
Therefore, it could be observed that the correntropy of the two random variables is the weighted sum of their moments in even order. A parameter that weights the second-order and higher-order moments is the kernel bandwidth. The second-order moment will dominate the correntropy when it is relatively large (the higher dynamic range of data), and the maximum correntropy criterion will then roughly align with the least mean square error criterion.
## 4 Omnidirectional Images
### Evaluation of Saliency for Omnidirectional Images  
Methods for saliency estimation in omnidirectional images are evaluated with a variety of measures, most of which are common to visual saliency in traditional images, such as the Pearson correlation coefficient CC ([29; 33; 36; 61; 62; 63; 64]), and the area under the ROC curve AUC ([34; 36; 61; 62; 63; 64]).  
The Salient360! benchmark[41] introduced, among other criteria, an evaluation based on the Kullback-Leibler divergence (KL). Although not specifically designed for omnidirectional images, this has been widely adopted as an evaluation measure[29; 61; 62; 63; 64] thanks to Salient360! being the de-facto reference for saliency in omnidirectional images.  
Regarding domain-specific evaluation, the same benchmark also introduced the evaluation of scanpaths based on the comparison metric by [PERSON] et al. [83] properly adjusted to incorporate orthodromic distances in 360\({}^{\circ}\) instead of Euclidean distances. The original metric is based on a comparison between each fixation from the prediction with all the fixations from the provided ground truth. Such comparison is applied on the basis of multiple elements, namely the spatial proximity of starting points, the difference in direction and magnitude of the saccades, and the temporal proximity of saccade midpoints.  
Based on the dataset/method matrix in Table 2, the Salient360! dataset is the best candidate benchmark to compare the largest subset of selected methods. Results are presented in Table 4 according to four different metrics reported in the corresponding publications. The VGG-based model by [PERSON] et al. [35] has been excluded as it does not report performance on metrics comparable with other methods. [PERSON] et al. [62] generates in absolute terms the best results across all considered measures, while the second-best is the model by [PERSON] et al. [64], according to three measures out of four. Both solutions are based on hand-crafted algorithms with a specific focus on emulating the color perception in human vision. Omnidirectional images, therefore, would appear to represent a domain where manually-defined criteria still outperform machine learning, possibly due to the stronger positional bias, and to image distortions that are uncommon in large datasets used for neural network pre-training [84].
# Interaural Coherence Estimation for Speech Processing in Reverberant Environment
###### Abstract  
Interaural coherence is used to quantify the effects of reverberation on speech, and previous studies applied the conventional method using all previous time data in the form of an infinite impulse response filter to estimate interaural coherence. To consider a characteristic of speech that continuously changes over time, this paper proposes a new method of estimating interaural coherence using time data within a finite length of speech, which is called the quasi-steady interval. The length of the quasi-steady interval is determined with various frequency bands, reverberation times, and short-time Fourier transform (STFT) variables through numerical experiment, and it decreased as reverberation time decreased and the frequency increased. In this interval, a diffuse speech, which is an infinite sum of reflected speeches of different propagating paths, is uncorrelated between two microphones apart from each other; thus, the coherence is close to zero. However, a direct speech measured at the two microphones has steady amplitude and phase difference in this internal; thus, the coherence is close to one. Moreover, the new method is the form of a finite impulse response filter that has a linear phase delay or zero phase delay with respect to speech to frequency; thus, the same or zero time delay for each frequency is applied to the power spectral density. Therefore, the coherence estimation of the new method is closer to the ideal value than the conventional one, and the coherence is accurately estimated at the time-frequency bins of direct speech, which is time-varying according to speech variation.  
reverberation; interaural coherence; speech processing; power spectral density estimation Article
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 4 Experiments and Results  
### Data Set  
For the experimental evaluation of our approach we used an urban image dataset which consists of a total of 79,465 photos collected from the center of the city of Athens, Greece. All these photos are geo-tagged, dated between January 2004-December 2015 and collected from Flickr using its public API. ([[https://www.flickr.com/services/api/](https://www.flickr.com/services/api/)]([https://www.flickr.com/services/api/](https://www.flickr.com/services/api/))) More specifically, we queried Flickr for a region covering what is in general considered to be the center of the Athens, (i.e., where the city's main touristic attractions are located) and retrieved all geo-tagged photos. This rectangular area is equal to 7.7 km\({}^{2}\). Its Northern-Western and Southern-Eastern points have coordinates (37.9836, 23.7153) and (37.9643, 23.7541), respectively. This is illustrated in Figure 3 within a larger map of Athens. Furthermore, we used OpenStreetMap to recover tourist attractions for the broader region of Attica, Greece. In particular, the densities of the collected photos are illustrated in Figure 3a, while those of POIs are depicted in Figure 3b. Upon careful observation of both, one may easily agree that the selected area is both the most \"photographed\", (density of photos is 10,887.8 photos/km\({}^{2}\) compared to 124.7 photos/km\({}^{2}\) for the broader area) while it also contains the highest density of POIs (46.2 POIs/km\({}^{2}\) compared to 2.2 POIs/km\({}^{2}\) for the broader area). Notably, it contains approx. 61% of photos, and approx. 35% of all POIs of possible tourist interest. However, approx. 96% of the most important POIs of Athens are included. We identify their importance by measuring the number of images in close proximity (taken within a radius of 100 m) to a particular POI.  
The collected photos have been captured by 5038 users of various nationalities, thus they contain tags of different languages. Although the majority of these tags is in English, we used the Google Translate API, ([[https://cloud.google.com/translate/](https://cloud.google.com/translate/)]([https://cloud.google.com/translate/](https://cloud.google.com/translate/))) in order to translate non-English tags (leaving English ones unchanged). This way, we tried to exploit tags which would otherwise act as \"noise\", although noisy tags still remained. Additionally, we also created a manual stoplist, whose goal was to remove non-relevant (to our goals) tags. For example, many cameras and smartphones automatically add metadata information related to their brand and model. Also, camera settings at the time of taking the photo are also added as part of the metadata. Users often add tags such as _Greece_ or _Hellas_ or even _Athens_. Such tags are both popular and spread to the whole area. However, they do not provide any useful information, thus may disturb the whole process.
## 2 Materials and Methods
### Derivation of the Initial Conditions
#### 2.3.1 Initial Condition on Velocity  
Mathematically, an initial velocity imposed at the contact point is equivalent to a force impulsion. It is assumed that the system starts at the quasi-static equilibrium position, that is, \(U_{0}=U_{st}\to u_{0}=0\), which yields, in modal coordinates, \(\mathbf{Q}_{0}=\mathbf{Q}_{st}\to\mathbf{q}_{0}=\mathbf{0}\). Now, to find \(\dot{\mathbf{q}}_{0}\), it is assumed that the system is initially at rest, such that \(\dot{u}(0^{-})=0\), and is then disturbed by an impulsion in force, with an amplitude of \(F_{0}\), that sets the vibrations to \(\dot{u}(0^{+})=\dot{u}_{0}\). By modal projection, the following is implied:  
\[\begin{cases}\dot{\mathbf{q}}(0^{-})=\mathbf{0}\\ \dot{\mathbf{q}}(0^{+})=\dot{\mathbf{q}}_{0}\end{cases} \tag{18}\]  
Now, the left-hand side of Equation (6) is integrated around \(0^{-}\) and \(0^{+}\) and the aforementioned conditions are applied:  
\[\int_{0^{-}}^{0^{+}}(\mu\ddot{\mathbf{q}}(t)+\dot{\mathbf{c}}\dot{\mathbf{q}}( t)+\gamma\mathbf{q}(t))dt=\int_{0^{-}}^{0^{+}}\mathbf{\Phi}^{T}F_{0}\delta(0)dt \tag{19}\]  
It is deduced that  
\[\dot{\mathbf{q}}_{0}=\mu^{-1}\mathbf{\Phi}^{T}F_{0} \tag{20}\]  
This expression is premultiplied by \(\mathbf{\Phi}\) to obtain \(\dot{u}_{0}\) (cf. Equation (9)), from which \(F_{0}\) is deduced, as follows:  
\[F_{0}=\frac{\dot{u}_{0}}{\mathbf{\Phi}\mu^{-1}\mathbf{\Phi}^{T}} \tag{21}\]  
Via substitution in Equation (23), it is deduced:  
\[\dot{\mathbf{q}}_{0}=\dot{u}_{0}\frac{\mu^{-1}\mathbf{\Phi}^{T}}{\mathbf{\Phi }\mu^{-1}\mathbf{\Phi}^{T}} \tag{22}\]  
Initial velocity \(\dot{u}_{0}\) is chosen as a disturbance \(\varepsilon\) of the imposed sliding velocity \(\Delta V_{y}=\alpha V_{x}\) between the wheel and the rail, such that \(\dot{u}_{0}=\varepsilon\alpha V_{x}\).
###### Abstract  
Linking architectural models to structural analyses can be demanding and time-consuming, especially when the architectural models cannot be accurately analysed using readily available one- or two-dimensional finite elements. This paper presents a tool for finite element analysis using solid elements developed as a plugin for Grasshopper 3D\({}^{\otimes}\) that enables designers to include analyses of complex objects within the same software as the design exploration. A benchmark using the tool on a cantilever beam is compared with both ANSYS\({}^{\otimes}\) and the theoretical solution, before the versatility of the tool is demonstrated by analyzing the metal part in timber gridshell nodes. The results were satisfying and the tool can prove especially useful for early phase design and collaboration between disciplines.  
FEA; Grasshopper 3D; Solid FEM; volumetric analysis; gridshell; reticulated shell; gridshell node; connector +
Footnote †: journal: _Sci._  
engineers, focusing on gridshells, resulting in both built prototypes and new design tools. In 2016 a temporary pavilion called Printshell was realised for a local festival in Trondheim. It had a footprint of 6 \(\times\) 6 m and a height of 3 m. It was built from members of 48 \(\times\) 48 mm of C24 spruce with standardised cuts at the ends and unique lengths. The ends of members were produced in the workshop with saw tables and were connected to 51 unique nodes produced with 3D-printing, hence the name Printshell. The nodes should distribute the forces between the members. As the nodes would be highly visible in the structure, it was essential to be able to visualise the geometries as precise as possible. Manufacture and analysis of the nodes was the key issue and focus of the project.  
A parametric model was used to define the geometries for the nodes. The design workflow also included a connection between global and local modelling of the gridshell. The global model evaluated the overall structural performance, representing the gridshell members (beams) as the lines and nodes as the points. The global model enables the designer to preview the volumetric representation of the model. The local model included detailed node geometries. The global model and local geometry generation were done in a parametric environment using Rhinoceros(r) and Grasshopper 3D(r) [1], with the global structural analysis made in Karamba3D(r) [2]. The local analysis on behavior of the nodes was made in ANSYS(r) [3]. The design of relatively small elements cannot be done on the global model with readily available one- and two-dimensional finite elements. Therefore a Finite element analysis (FEA) with volumetric finite elements was deemed necessary for the analysis. However, the volumetric analysis was a computationally heavy and slow procedure, demanding changes between software and geometry conversion. The most crucial nodes were exported to ANSYS from Rhinoceros and analysed individually, with little options for automation. Figure 1 shows one of the prindshell nodes, manufactured with 3D-printing, and an example of a node analysis in ANSYS.
# A High-Robust Sensor Activity Control Algorithm for Wireless Sensor Networks
## 5 Evaluations and Results
### Self-Organization Ability Analysis with High Death Ratio  
The actual network environment is often unpredictable. The sensor may be damaged or out of power, causing the total number of sensors to change. Therefore, we created a network environment with a high death ratio. We verified the SACA, Gur Game, and ACK algorithms in terms of self-organization and reconstruction capabilities. As in Section 5.2,  
Figure 10: (**a**) shows the average convergence time and success ratio with \(\sigma\) is 0.001; (**b**) \(\sigma\) is 0.0001 in the SACA.  
Figure 9: (**a**) shows the average convergence time; (**b**) the success ratio in the ACK algorithm.  
in an environment that does not consider the death or damage of the sensor, when the activation parameter is small, fewer sensors change from the sleep state to the active state, which makes the system more stable. However, in a network environment with a high death ratio, the activation parameter is increased to reach the target value as soon as possible. To create a network environment with a high death ratio, we generated the life span of the sensor from an exponential distribution with an average value of 70. In addition, in this experiment, the activation parameter of SACA was set to 0.01.  
Figure 11(a) shows the execution results of the SACA and ACK algorithms. The SACA algorithm took longer than the ACK algorithm to maintain the target value. The execution results of ACK and Gur Game are shown in Figure 11(b), which barely stayed at the target value. The activation parameter affects the probability of the sensor transitioning from the sleep state to the active state. In other words, if the activation parameter is higher, the sensor has a higher probability of converting its state into an active state. In an environment that does not consider the death or damage of the sensor, when the activation parameter is small, fewer sensors will transition from the sleep state to the active state.  
Therefore, in a network environment with small changes, we recommend setting smaller activation parameters to achieve a quick convergence to the target value. However,  
Figure 12: (a) SACA and ACK versus (b) ACK and Gur Game in highly death environments.  
Figure 11: Comparison of the average convergence time of the ACK algorithm and SACA.  
in a network environment with a high death ratio, owing to the large changes in the network environment, a larger activation parameter should be used to increase the speed of convergence to the target value. The results show that SACA has stronger self-organization and reconstruction capabilities than Gur Game.
## 3 Performance Assessment
### Simulation
#### 3.1.2 Simulation Results
Figure 6 compares the performance of adaptive equalization for post-process between the proposed method and the TR acoustic communication using SC modulation. In Figure 6, \(N_{{}_{t}}\) denotes the total number of taps in all adaptive equalizers. The number of adaptive equalizers equals the number of subcarriers in the proposed method while the number of adaptive equalizers equals one in the TR acoustic communication using SC modulation. The taps of each adaptive equalizer are adjusted using RLS algorithm and the number of the taps is determined by the ISI span in each composite channel corresponding to each subcarrier. Several observations can be made from Figure 6. Firstly, the performance is further improved using adaptive equalization as post-process after the TR process. Secondly, when \(E_{a}\big{/}N_{{}_{0}}\geq 10\) and the total number of taps \(N_{{}_{t}}\) are equal, the performance of adaptive equalization in the proposed method individually for \(M=8\), \(M=16\)
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 5 Single Scanner-Based DoA Estimation
### Heading Measurements Using Magnetometer  
To estimate the direction of arrival of a signal, it is important to know the orientation of the scanner device and its position in the three-dimensional space. In this research, the scanner device is a smartphone that also has an in-built magnetometer (as a part of the inertial measurement unit or IMU) used for navigation applications. A magnetometer provides data on the strength and direction of a magnetic field. Magnetometer data are dependent on the orientation of the phone with respect to the horizontal plane, and an accelerometer can be used to calculate the pitch and roll angles of the device to determine the orientation when moved away from the parallel horizontal plane. For this project, we are only using two-dimensional readings of the heading angle from true north and ignoring the tilt information. This information can yield the angle of orientation of the phone, and with location services enabled, the GPS coordinates and declination angle provide the phone's orientation from true north. Both iOS and Android provide convenient API to acquire heading measurements from the built-in IMU in real-time [27; 28; 29].