esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### From Dikes to Crater Alignments  
Many ocean islands are affected by pronounced volcano-tectonic interactions such as dike intrusions, faulting and rifting, gravitational spreading, flank instability, and sector collapses [1, 2, 3, 4]. Rifting and flank motion may episodically accelerate [5, 6] and create the necessary space for new intrusions along dikes and rift zones [7, 8]. Normal faults, central grabens, magmatic fissures, and aligned cones are common morphological features of rift volcanoes [9]. In addition, the direction of rift zones can be subject to changes and may be adjusted if the surrounding stress field conditions change [10; 11].  
Dike intrusions have a tabular shape, where their width usually ranges from a few tens of centimeters to several meters. In contrast, the propagation length ranges from hundreds of meters to kilometers at ocean islands [12]. The direction of dike intrusion is determined by pre-existing pathways (weak zones, faults, or lithological barriers) and/or by the principal compressive stress axes, which cause dikes to intrude perpendicular to the minimum compressive stress (\(\sigma\)3) and toward the maximum compressive stress (\(\sigma\)1) [12]. Likewise, a volcanic edifice load or changes associated with pressurized reservoirs at depth can deflect the ascent direction of dike propagation laterally [12]. Dike propagations cause faulting and earthquakes [13]. However, earthquakes produced by dike propagation reflect the differential stress and not the extension of the dike [12].  
Dikes are the feeders of fissure eruptions and aligned eruptive vents. At Kilauea, a prominent crater alignment followed the seismically inferred dike intrusion in 2011 [14]. A similar example can be found in the eruption of Holuharoun in Iceland in 2014. This eruption was characterized by the movement of magma from the Bardarbunga subglacial caldera to the Holuharoun eruption site, approximately 45 kilometers away. The eruption occurred at fissures and focused at vents aligned above a feeder dike [15]. The feeder dike was inferred to be oblique to the rifting direction, leading to complex faults, step-overs, and en echelon segments of fissures and eruption sites [14; 16]. Likewise, dikes at the Mount Etna NE rift zone are expressed at the surface by elongated, breached, and aligned scoria cones along the fault. This fault developed in relation to an unstable eastern volcano flank, which slides eastward toward the sea [17]. On the Canary Islands, dike intrusions and rift zones are common, associated with the governing stress field, and provide favorable conditions for magma plumbing systems [7; 18]. Rift zones are characterized by crater alignments and parallel groups of dikes beneath, which are particularly common on Tenerife Island [10], El Hierro Island [11], and La Palma Island [19]. In addition, the 2021 eruption on La Palma was associated with crater alignments into a crater row, which may imply the presence of an underlying feeder dike, as our study suggests.
## 3 Training Signal Optimization
### Complexity Comparison  
To validate the efficiency of the proposed designs, in this section, we compare the computational complexity of the proposed schemes with that of the iterative algorithm in [20]. In each iteration of the conventional algorithm, the arithmetic complexity of \(\mathcal{O}\big{(}(M_{t}^{6.5}+L^{6.5})\text{log}(1/\psi)\big{)}\) is required for computing \(\tilde{\mathbf{T}}\) and \(\tilde{\mathbf{Q}}\) for a fixed \(\mathbf{P}\)[33], where \(\psi\) is the solution accuracy for the interior point method, and the arithmetic complexity of \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}\big{)}\) is needed to compute \(\mathbf{P}\) for fixed \(\tilde{\mathbf{T}}\) and \(\tilde{\mathbf{Q}}\)[34]. Thus, the conventional algorithm in [20] requires the complexity \(\mathcal{O}\big{(}\{M_{t}^{3}+L^{3}+(M_{t}^{6.5}+L^{6.5})\text{log}(1/\psi) \}\big{)}\) per iteration. Let \(N_{\text{iter}}\) denote the total number of iterations required for the algorithm in [20]. Then, it requires the total computational complexity of \(\mathcal{O}\big{(}\{M_{t}^{3}+L^{3}+(M_{t}^{6.5}+L^{6.5})N_{\text{iter}}\text {log}(1/\psi)\}\big{)}\). Next, we consider the complexity of the proposed training schemes. To implement the training structure in (15), the arithmetic operation of \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}\big{)}\) is required for computing the EVDs of \(\tilde{\mathbf{T}}\) and \(\tilde{\mathbf{Q}}\)[34]. Also, we require the computational complexity of \(\mathcal{O}\big{(}(M_{t}^{3.5}+L^{3.5})\text{log}(1/\psi)\big{)}\) to compute the optimal power allocation numerically [33]. Therefore, the overall complexity for the optimal training scheme is \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}+(M_{t}^{3.5}+L^{3.5})\text{log}(1/\psi) \big{)}\). On the other hand, since the complexity of computing the suboptimal power allocation in (24) is insignificant compared with the complexity \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}\big{)}\)[34], the overall complexity for the suboptimal training scheme is \(\mathcal{O}\big{(}M_{t}^{3}+L^{3}\big{)}\). In Table 1, we summarize the analytical complexity results and processing time measured on the 12 th Gen Intel(R) Core(TM) i9-12900K CPU when \(M_{t}=M_{r}=L=4\).
simultaneously controlled values of the required boiler heat output and optimal conditions for the combustion process from both the efficiency and emissions point of view [4; 5; 6]. Therefore, the operation of biomass-fired boilers has to be properly controlled using a classical cascade [7] and also advanced control algorithms and methods [8; 9] with the possibility of on-line system monitoring [10].  
In medium-scale biomass-fired boilers, the amount of combustion air is in standard circumstances controlled by means of a Lambda probe that measures oxygen (O\({}_{2}\)) concentration in a flue gas. In order to increase combustion efficiency and simultaneously to increase the amount of heat produced and to decrease harmful emissions, new control algorithms were designed based on information not only about O\({}_{2}\) concentration in the flue gas but also about the trend of carbon monoxide (CO) emissions. These algorithms were implemented into the control systems of the biomass-fired boiler with the aim of reaching complete combustion with minimum excess of combustion air. It was proven that a low-cost wide band Lambda probe and simple CO sensor can be used for cost-effective biomass combustion control in medium-scale and, after some simplification, also in small-scale biomass-fired boilers [11; 12; 13]. However, due to strong interference in one type of boiler, the task of how to define a trend function of measured variables (especially CO emissions and O\({}_{2}\) concentration in flue gas) had to be solved. Another problem that arose was how to correctly use the noisy measured data for biomass combustion control because it was difficult to apply in a process control system [14]. The measured values were influenced by various transfer errors, disturbances and external interferences and for that reason the measured data had to be appropriately smoothed and filtered.  
There are many types of digital filters that can be used for signal filtration [15; 16]. One of the interesting examples of digital filters is the infinite impulse response (IIR) filter. This filter consists of feedforward and feedback and it is also called the auto regressive moving average (ARMA) filter. A special group of digital filters is a group of finite impulse response (FIR) filters, also called moving average (MA) filters [17; 18; 19]. A filter that exhibits the properties of superposition, homogeneity, and shift invariance is called a linear time invariant (LTI) filter [20]. Its advantage is that one-dimensional LTI systems can be described by linear differential equations with constant coefficients.  
Some of filters mentioned above are not convenient for biomass combustion signal filtering, because stochastic elements have an influence on the output of the filter (recursive IIR filters). For example, MA filters with exponential forgetting are not useful due to the algorithm of weight-counting [21]. The others are useful only with a mathematical model of the system (e.g., Kalman filter) [22].
## 1 Introduction  
Compressive sensing (CS) and sparse signal representation have received widespread attention and increasing interest over the past few years [1,2], which are motivated by the sparse nature of the real world data and the advantages of the CS theory. The applications of CS in numerous areas have been widely investigated in the literature, such as magnetic resonance imaging (MRI) [3], synthetic aperture radar (SAR) imaging [4], inverse synthetic aperture radar (ISAR) imaging [5], passive radar imaging [6], direction-of-arrival (DOA) estimation [7], communication channel estimation [8], seismic signal processing [9], spectral estimation [10], image processing [11], speech enhancement [12], _etc._ Generally speaking, those disciplines explore the following linear signal model [13]:  
\[\mathbf{y}=\mathbf{H}\mathbf{w}+\mathbf{n} \tag{1}\]  
where \(\mathbf{y}\in\mathtt{c}^{M\times 1}\) is a \(M\times 1\) complex vector, \(\mathbf{H}\in\mathtt{c}^{M\times N}\) is a \(M\times N\) complex measurement matrix, \(\mathbf{w}\in\mathtt{c}^{N\times 1}\) represents the \(N\times 1\) unknown complex signals of interest, and \(\mathbf{n}\in\mathtt{c}^{M\times 1}\) denotes a \(M\times 1\) complex noise vector. Suppose \(\mathbf{w}\) is sparse or compressible in a known dictionary \(\mathbf{\Psi}\in\mathtt{c}^{N\times N}\), _i.e._, \(\mathbf{w}=\mathbf{\Psi}\mathbf{x}\), where \(\mathbf{x}\in\mathtt{c}^{N\times 1}\) is a \(K\)-sparse vector, namely it can be approximated by its \(K\) largest coefficients or its coefficients satisfy a power decay law with \(K\) strongest coefficients. Therefore, linear measurements are obtained in CS as:  
\[\mathbf{y}=\mathbf{A}\mathbf{x}+\mathbf{n} \tag{2}\]  
where \(\mathbf{A}=\mathbf{H}\mathbf{\Psi}\in\mathtt{c}^{M\times N}\) represents the sensing matrix. Although the above equation is usually ill-posed, the CS theory has shown that if \(\mathbf{A}\) satisfies some certain conditions, we can construct \(\mathbf{x}\) and \(\mathbf{w}\) stably from highly undersampled measurements \(\mathbf{y}\)[14].
## 2 Materials and Methods
### Vegetation Index Computation  
Normalized Difference Vegetation Index or NDVI is one of the important indices, commonly used for the green vegetation. It is a relation between the near-infrared (NIR) band and the red (RED) band. It can be expressed through the following expression (1):  
\[\text{NDVI}=\frac{\rho_{\text{NIR}}-\rho_{\text{RED}}}{\rho_{\text{NIR}}+\rho_{ \text{RED}}} \tag{1}\]  
where NDVI represents the Normalized Difference Vegetation Index, \(\rho_{\text{NIR}}\) represents NIR band and \(\rho_{\text{RED}}\) represents the RED band.  
The presence of chlorophyll on the leaf and its relation to NDVI is directly related to each other. Hence different phases of the crop's growth cycle, such as the sowing period, growing period, and harvesting period, can be traced from the close observation of the NDVI reflectance signatures [38; 39]. Band information of Sentinel-2 images is given in Table 1. In the present study, Band 8 (Visible and Near Infrared (VNIR)) and Band 4 (RED) were taken into consideration for the generation of NDVI.  
Figure 2: Workflow.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Band**} & **Pixel Size** & **Central Wavelength** & **Description** \\  & **(m)** & **(nm)** & **Description** \\ \hline B1 & 60 & 443 & Aerosols \\ \hline B2 & 10 & 490 & Blue \\ \hline B3 & 10 & 560 & Green \\ \hline B4 & 10 & 665 & Red \\ \hline B5 & 20 & 705 & Red Edge 1 \\ \hline B6 & 20 & 740 & Red Edge 2 \\ \hline B7 & 20 & 783 & Red Edge 3 \\ \hline B8 & 10 & 842 & NIR \\ \hline B8a & 20 & 865 & Red Edge 4 \\ \hline B9 & 60 & 940 & Water Vapor \\ \hline B11 & 20 & 1610 & Short Wave Infrared (SWIR) \\ \hline B12 & 20 & 2190 & Short Wave Infrared (SWIR) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Band information of the Sentinel-2 satellite images [37].  
Figure 3: Algorithm execution.
# Influence of the Sound Source's Position on the Stage on the Speech Perception in the Auditorium
## 3 Results
### Influence of Sound Source Location on the Quality of Speech Perception  
During a performance, the actors move around the stage, and so the sound source moves, which can result in a change in the perception of speech quality. When assessing the effect of sound source location on speech perception, three S positions were considered: the central, S01, and the extreme left, S03, and right, S02 (Figures 1 and 2). The results obtained for the three sound source positions on stage and the two types of decoration are presented in Table 6 and Figures 7 and 8.  
Figure 6: Results of the subjective quality assessment (MOS) for female and male speech in the function of listener position. The results for Décor #1 are marked with a solid line, and those for #2 are marked with a dashed line.  
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline \multirow{2}{*}{**Listener**} & \multicolumn{4}{c}{**\#1**} & \multicolumn{4}{c}{**\#2**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} \\ \cline{2-9}  & **MOS** & \(\sigma\) & **MOS** & \(\sigma\) & **MOS** & \(\sigma\) & **MOS** & \(\sigma\) \\ \hline
1 & 4.44 & 0.56 & 4.24 & 0.69 & 4.00 & 0.68 & 3.73 & 0.84 \\
2 & 4.49 & 0.53 &  
Figure 8: Results of the subjective quality assessment (MOS) for different sound source locations S as a function of listener position (Decor #2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Listener** \\ **Position** \\ \end{tabular} } & \multicolumn{4}{c}{**\#1**} & \multicolumn{4}{c}{**\#2**} \\ \cline{2-13}  & \multicolumn{2}{c}{**S01**} & \multicolumn{2}{c}{**S02**} & \multicolumn{2}{c}{**S03**} &  
Analysing the results obtained, it is possible to see large variations in the speech quality rating depending on the location of the listener in the audience. This is particularly visible in the case of Decor #2. For this decoration, the MOS value averaged over all points for the source centrally located on stage S01 is 3.88, while the maximum is 4.38 and the minimum is 3.55. In comparison, the MOS value for the same position of S01 but for Decor #1 is 4.17, while the maximum is 4.43 and the minimum is 4.02. Changing the position of the source from S01 to S02 or S03 resulted in a deterioration of speech perception quality at points opposite the sound source; the quality rating at these points reached MOS values < 4.0.
# Phase Noise Cancellation in Coherent Communication Systems Using a Radio Frequency Pilot Tone
## 4 Differential Carrier Phase Recovery  
Differential phase detection (DPD) can also be employed for recovering the carrier phase in coherent communication systems. In the DPD scheme, signal data are encoded in and extracted from the phase differences between consecutive transmitted signal symbols [21]. For instance, the phase information of the \(k\)-th symbol can be extracted according to the phase difference between the \(k\)-th and the \((k+1)\)-th symbols. Therefore, the CPR error is determined by the phase fluctuation between the \(k\)-th and the \((k+1)\)-th symbols within a symbol period [19]. It is noted that DPD does not require additional computations such as the \(n\)-power, the averaging, and the phase unwrapping, compared to other CPR methods [20, 24, 25, 26]. Therefore, it can be easily implemented in DSP hardware for a real-time operation. The block diagram of DPD CPR is provided in Figure 4.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
## 2 Methodology  
Coverage, specifically regional coverage, has been identified as a critical necessity since the beginning of WSN research [11]. Coverage indicates if at least one sensor has detected each place inside the Region of Interest (ROI). In terms of coverage, each sensor must enclose certain sub-regions, and by aggregating the whole secure sub-regions, a  
Figure 1: WSN coverage hole detection and categorization hierarchical diagram.  
protected region in the WSN may be achieved [11; 12]. A coverage hole might be built for a variety of reasons. In addition to the random arrangement of nodes, faulty network architecture [12], node replacement [13], a lack of friendliness in the surroundings, issues with ROI [14], and power draining [15] all contribute to coverage hole formation. There are two categories of nodes in a WSN as a result of the arbitrary organization. The outer node participates in the border arrangement and the interior nodes do not [16]. Outside nodes have an active role in security and research activities. While the arrangement of nodes inside each other may be enhanced, however, Figure 2a shows possibly fewer coverage hole areas without any sensors protecting them, and Figure 2b indicates the network boundary. Circle indicates the sensing range of the sensor. The arrow indicates the coverage hole, due to node failure.  
The network-boundary recognizing or connecting ROI is difficult, whereas the organization limits hubs around the ROI of the whole organization. The hole boundary is mostly made up of nodes, but it may also consist of functions. Malfunctioning hubs may even contribute from the interior nodes to the nodes that define the boundaries of another hole. As a result, detecting the hole boundary is important for the following reasons:  
1. The nonfunctional nodes can be accurately identified by the hole boundary;
2. By identifying hole borders, effective routing can stop data from being lost in the network;
3. Detecting the hole boundary precisely determines the hole region;
4. Geographic multicasting is enhanced by the introduction of a hole restriction;
5. The number of additional nodes required for hole correction may be determined with proper hole boundary detection.  
Coverage hole detection algorithms can discover more about the physical placement of sensor nodes, their communication ranges, and any gaps or obstructions that can impair coverage by utilizing computational geometry. Accuracy is improved by combining geometric approaches with energy-conscious algorithms and communication protocols.
# A Room Monitoring System Using Deep Learning and Perspective Correction Techniques
## 2 Related Work
### Room Monitoring System  
Smart sustainable cities are becoming popular worldwide, and therefore, their realization has attracted considerable research interest. Some studies have focused on monitoring the environment of a room, including the indoor temperature, relative humidity, and indoor air quality [PERSON] et al. (2019); [PERSON] et al. (2020); [PERSON] et al. (2020). Studies have also investigated monitoring people in a room. For example, [PERSON] et al. [PERSON] et al. (2019) introduced an indoor human localization system based on a capacitive sensor. They set up this system in a 3 m\(\times\)3 m room and then compared the localization performance of most machine learning classification algorithms in the Weka collection. [PERSON] et al. [PERSON] et al. (2019) proposed a room monitoring system that uses a webcam to detect people's movements. This system has additional features such as alarms, alerts, message warnings, and email reports. [PERSON] et al. [PERSON] et al. (2019) introduced a cloud-based people counter using an embedded system based on Raspberry Pi that delivered the obtained data to ThingSpeak and an IoT platform. They used a Pi camera to monitor people's movement and then processed the video footage to count people. [PERSON] et al. [PERSON] et al. (2019) proposed a scale-driven CNN model for people counting and localization in crowd scenes. They created scale-aware head region proposals by using perspective information to address scale variations.  
In schools, computer vision techniques have been widely used for classroom monitoring systems. For example, [PERSON] et al. [PERSON] et al. (2019) presented a system based on computer vision to monitor student behavior in the classroom. First, the system recorded entire students' behavior. Then, data processing was implemented by using several computer vision techniques, such as face detection, facial landmark detection, face embedding, face classification, and gaze estimation. Finally, the results were stored in the database. Furthermore, they built a web application for assisting the teachers with monitoring student behavior. [PERSON] et al. [PERSON] et al. (2020) proposed a classroom concentration model to support teachers and experts in teaching analysis. They built the CNN models for head detection and head pose estimation. Then a concentration model was implemented to analyze the concentration situation of students. [PERSON] and [PERSON] and [PERSON] (2019) presented a monitoring system for checking student attendance. The system captured the images from the surveillance cameras of the classrooms. Next, the face recognition method was used to detect student attendance. These results would be sent to the teachers.
## 4 Results and Discussion
### Ndvi  
Three examples of the NDVI computed from the red/NIR bands of the Landsat OLI-TIRS images have heterogeneous landscapes and vegetation coverage, as seen in Figure 10.  
In the NDVI maps, healthy vegetated areas are bright, water is light to dark brown, and soils and bare ground areas are beige and bluish to slate grey, as seen in Figure 10. Denoting those shades and hues can be understood from the inspection of the relevant land cover types in previous maps. The NDVI is used for the assessment of various types of vegetation along the Congo flow, where the environmental effects from the hydrology on plant growth are more distinct. Moreover, the significance of the NDVI for environmental monitoring is that it well corresponds to crop biomass accumulation, as well as to botanical parameters, for example, leaf chlorophyll levels, LAI, and the active radiation absorbed by a crop canopy through the photosynthesis process.  
Figure 10 demonstrates the three examples of the NDVI maps derived from the three dates of Landsat imagery in the Bumba, Basoko and Kisangani regions, respectively. Here, values from \(-1.0\) to \(0.1\) signify water areas (Congo river and tributaries); values from \(0.1\) to \(0.2\) represent river banks; values from \(0.2\) to \(0.3\) belong to the class of the non-vegetated areas; pixels with values from \(0.3\) to \(0.4\) represent seasonally flooded and inundated areas, wetlands and swamps; values from \(0.4\) to \(0.5\) are savannah with sparse trees; pixels of class \(0.5\) to \(0.6\) are primarily irrigated agricultural fields with diverse types of crops; bright pixels of the class \(0.6\) to \(0.7\) are secondary forests; values from \(0.7\) to \(0.8\) are densely vegetated lands and mosaic forests; finally, the areas with NDVI values greater than \(0.8\) are dense humid tropical forests.