esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Eigenvalue Fusion and other Fusion Methods for Detecting General Wideband Signals
### Eigenvalue Fusion for General Wideband Signals  
We now propose a new eigenvalue fusion scheme, where each CR is responsible for computing its own sample covariance matrix, estimating its eigenvalues and transmitting these eigenvalues to the FC. The aim is to detect the presence of a primary signal to the level of subcarriers in OFDM systems, or to the level of predefined channels or sub-bands in general wideband signals. The adaptation of the eigenvalue combining is discussed in the next section.  
Again, let \(N\) be the number of snapshots of the signal received by each CR in the frequency domain, and let \(J\) be the desirable number of eigenvalues per frequency band or subcarrier of a general wideband signal, which is the order of the sample covariance matrices to be subsequently computed. Assuming that \(N/J\) is an integer number, a matrix \(\mathbf{A}_{k}^{(i)}\) with samples from the \(k\)-th sub-band or subcarrier can be formed by arranging the \(N\) samples taken by the \(i\)-th CR in \(J\) rows and \(N/J\) columns, preferably with \(N/J\gg J\) (which implies \(N\gg J\)) for more accuracy of the subsequent sample covariance matrix computation. Matrix \(\mathbf{A}_{k}^{(i)}\) is then  
\[\mathbf{A}_{k}^{(i)}=\begin{bmatrix}Y_{k}^{\,(i)}(1)&\ldots&Y_{k}^{\,(i)}(N/J \,)\\ \vdots&\ddots&\vdots\\ Y_{k}^{\,(i)}(n+1-N/J\,)&\cdots&Y_{k}^{\,(i)}(N)\end{bmatrix} \tag{12}\]  
The sample covariance matrices in this case are computed in each CR and are given by  
\[\mathbf{R}_{k}^{(i)}=\frac{J}{N}\mathbf{A}_{k}^{(i)}\mathbf{A}_{k}^{(i)^{ \dagger}} \tag{13}\]  
The eigenvalues of these covariance matrices are then computed and sent to the FC. The next part of the sensing process is to combine the eigenvalues received by the FC for the computation of the test statistic. For a quantity \(m\) of cooperating CRs, the total number of eigenvalues received by the FC is \(mKJ\). Through some modifications in Equations (3)-(6), we propose the following test statistics for the \(k\)-th sub-band in the eigenvalue fusion scheme for general wideband signals:  
\[T_{\text{GLRT},k}=\frac{\frac{\frac{1}{m}\sum\limits_{i=1}^{m}\lambda_{1,k,i}} {\frac{1}{JKm}\sum\limits_{j=1}^{J}\sum\limits_{z=1}^{K}\sum\limits_{i=1}^{m} \lambda_{j,z,i}}}{\begin{array}{c}\\ \\ \\ \\  
scheme, each CR forms one covariance matrix based solely on its samples and computes its eigenvalues. The eigenvalues from different CRs are then sent to the FC, where they are combined to form the test statistic. Notice that this approach aims at reducing the volume of data sent in the reporting channel (from the CRs to the FC) when compared with the sample fusion scheme.  
It is worth mentioning that the modified test statistics Equations (14)-(17), as well as other ones defined later in this paper, were determined empirically. This means that it is not guaranteed that they are the optimal versions of the corresponding hypothesis tests derived from a likelihood ratio test. Then, all the modified test statistics from this point on can be cast as GLRT-like, MMED-like, MED-like and ED-like tests.
## 3 Distributed Target Tracking Systems
### Estimation Refinement with ANN
#### 3.3.4 Estimation Fusion  
The distributed scheme is executed in two steps: (1) Group Estimation: local decisions are performed; (2) Estimation Fusion: a fusion rule is applied to combine the posterior density of the estimation from each member of the cooperative group in the leader sensor. Since the weight reflects the significance attached to the estimate, the next issue is to determine the weigh \(\omega_{i}\) for each estimate and try to weight out faulty estimates. One strategy for choosing \(\omega_{i}\) is to use the utility measure. Since the utility of a sensor measurement is a function of the geometric location of the target, here we consider the Mahalanobis measure [28]. Hence, with respect to a neighboring system estimate characterized by the mean \(\mu_{m\ell}\) and covariance \(\Sigma\), the utility function for sensor \(m\) is defined as the geometric measure  
\[\mathcal{U}_{m\ell}=(\mu_{m0}-\mu_{m\ell})^{T}\Sigma^{-1}(\mu_{m0}-\mu_{m\ell}) \tag{5}\]  
where \(\mu_{m0}\) is the local estimated target position of sensor \(m\) and \(\ell\) refers to a neighboring system estimate. In order to arrive at a consensus, the utility measure \(\mathcal{U}_{m\ell}\) can be shown to be \(\mathcal{U}_{m\ell}\leq 1\)[29]. Given the utility measure, two estimates can be allowed to be compared in a common framework and measure how much they differ \(|\mu_{m0}-\mu_{m\ell}|\). Accordingly, the weights for the CI method in (3) are given by  
\[\omega_{\ell}=\frac{\frac{1}{\mathcal{U}_{m\ell}}}{\sum_{k\in U_{s}}\frac{1}{ \mathcal{U}_{mk}}} \tag{6}\]  
where \(U_{s}\) is the index set of the neighboring estimates that pass the utility test. Otherwise, \(\omega_{\ell}\) is set to be zero. Notice that in this work \(m\) may refer to a tasking leader and \(\ell\) may refer to a sub-cluster member.
# Assessment of Impacts of Climate Change on Hydropower-Dominated Power System--The Case of Ethiopia
## 3 Result and Discussion
### Electric Export  
The country aspires to become a cornerstone of the Eastern Africa Power Pool's (EAPP) regional power market, owing to its vast clean energy potential that can be produced at a low cost. Ethiopia already exporting electrical power to Sudan and Djibouti through its high-voltage (HV) connections. Ambiguous infrastructure developments in eastern Africa and beyond are creating the groundwork for a genuinely regional, integrated electricity market. The construction of a high-voltage direct current (HVDC) transmission line between Ethiopia and Kenya, capable of carrying up to 1000 MW has already begun. The Ethiopia-Kenya link is part of a larger, continent-wide plan to connect the EAPP via Kenya, Tanzania, and Zambia [2].  
In the New Policy Scenario, the electricity exports increase from 0.83 TWh in 2015 to 18 TWh in 2065. However, in a Drought Scenario, the electricity exports 4.8 TWh at the end of modeling period, which is not increased significantly compared to New Policy. As can be seen in Figure 5, electricity export is negatively affected by the severe drought. Eventually, it will affect the country's ambitious plans to become the export hub of the EAPP.
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 2 Related Work
### Guided Crowd Evacuation
#### 2.2.1 Single Human Leader  
The study presented in [9] reviewed various types of guidance approach using signage, human leaders, mobile devices, robots, and wireless sensor networks, and found those involving humans to be the most efficient. Another study [10] used the social force model to study evacuation in a subway station with two exits, and concluded that using a leader accelerates the evacuation process.
# Active Control Loop of the BOROWIEC SLR Space Debris Tracking System
## 2 Satellite Laser Ranging Process
### Laser Beam Propagation
#### 2.3.2 Coordination of Beam Emission  
The classic SLR system consists of a receiving telescope, mostly in the \(Az/El\) configuration, with a photon detector and a coaxially mounted laser beam emitter with a divergence corrector (Figure 1). Depending on the type of the laser, it is possible to mount the laser source directly on the telescope housing or, in the case of the scientific lasers with a picosecond pulse width, there is a need to propagate the laser beam from an air-conditioned room, using the Coude path with intermediary mirrors. Each solution ensures the alignment of the telescope axis with the laser beam emitter. Unfortunately, the imperfection of the ephemerides described in Section 2.2 and the impact of laser beam divergence on the effectiveness of the measurement described in Section 2.3.1 requires the correction of the telescope's movement in order to frame the object. Ephemeris uncertainty is mainly related to the time shift along the object's trajectory (along-track) and is determined by the TimeBias parameter. In the case of cooperative satellites, this parameter is determined by other SLR stations during previous measurements. In this way, it is possible to make an appropriate time correction before starting the tracking process. A large number of uncooperative objects makes it impossible to determine the value of this parameter. In such a case, a movable prismatic head in the XY plane system can be used behind the laser source output. This solution enables manual or automatic angular offset of the laser beam in relation to the telescope's axes. Laser measurement only determines the distance to the object in the time domain; therefore, the angular offset of the laser beam does not cause disturbances to the results.
## 1 Introduction
We already demonstrated that the traditional classification algorithm, employing a least absolute shrinkage and selection operator, could be used to identify three spatial audio scenes differing in horizontal distribution of foreground and background audio content around a listener in binaurally rendered recordings of music [20]. In the subsequent study [21], we compared the performance of humans against that of the machine learning algorithms in the task of the classification of spatial scenes in binaural recordings of music. The three scenes were subject to classification: (1) music ensemble located in the front, (2) music ensemble located at the back, and (3) music ensemble distributed around a listener. According to the results, the machine learning algorithms substantially outperformed human listeners. More recently, we compared the traditional classification methods with the deep learning one in the task of the classification of front- and back-positioned music ensembles [22]. The major drawback of the above-mentioned studies is that they were restricted to two-dimensional (2D) scenes, with the audio sources distributed solely within the horizontal plane (elevation angle was equal to 0\({}^{\circ}\)). The aforementioned limitation was overcome in the present work. To the best of the authors' knowledge, this is the first study employing three-dimensional (3D) music ensembles (as opposed to 2D ones). Furthermore, it considers ensemble \"size\" as an experimental factor. Moreover, it attempts to identify the most important frequency bands, in terms of the spatial localization of the ensembles. There is an increasing body of research focusing on \"computational auditory scene analysis\" (CASA) [23] and \"acoustics scene classification\" (ASC) [24]. The purpose of CASA is to isolate individual audio objects within a complex scene, whereas the aim of ASC is to identify acoustical environments. Therefore, despite their nomenclature similarity, the research goals within the above-mentioned areas are different from the one posed in this study, which is focused on the localization of music ensembles. The work reportedin this paper constitutes part of the ongoing research project aiming to develop machine learning methods for the holistic characterization of spatial audio scenes of reproduced sound, referred to as spatial audio scene characterization (SASC) [25]. The paper is organized as follows. Section 2 gives an overview of the state-of-the-art models for binaural localization. A methodology outline is presented in Section 3. The audio repository employed in this work is described in Section 4. The topology of the CNN used in this study along with its development procedure is outlined in Section 5. The obtained results are presented in Section 6. Finally, the discussion of the achieved results and the conclusions are provided in Sections 7 and 8, respectively.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 3 Orchestration in Intermittently Available Platforms
### Transport-Layer Protocols for Control Communications
However, we also need to avoid harming its throughput and performance as much as possible for control communications, which is the main problem that TCP can introduce in these environments. One possible alternative to solve it could be using protocols that rely on UDP as its main transport-layer protocol, moving its reliability mechanisms to the application layer since this information should safely arrive to the aircraft. In this fashion we can find CoAP, which tries to apply the HTTP REST model to constrained environments but reducing its overhead size as well as using UDP as its transport-layer protocol. Similarly, another attractive protocol that uses UDP is QUIC [36], which tries to mimic an implementation of HTTP (version 2 [37]) and Transport-Layer Security (TLS) [38]. QUIC would allow keeping the REST model intact used for the exchange of information between the VIM and the aerial nodes. In any case, we want to highlight that QUIC is still a connection-oriented transport-layer protocol, which implements a congestion control mechanism based on that of TCP. Therefore, it may be sensitive to the same limitations that can be observed for TCP.
# Characterizing Situations of Dock Overload in Bicycle Sharing Stations
## 3 Methodology
### Data Collection, Modeling and Enrichment
For example, hourly or daily time slots can be selected as reference time periods to monitor dock overload situations during the day. **Data enrichment with spatial information. ** To detect dock overload situations restricted to a given area, we enrich occupancy level data with spatial information. Since all the stations in the system are geo-referenced, the geographical coordinates of all the stations in the system is collected. This information is used in our approach to compute the pairwise distances between stations.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 1 Introduction
Reference [20] gives a survey on pilot contamination and identifies possible sources of it. The authors of [21] provided an overview on some myths in massive MIMO systems and explained why they are not true. In this paper we provide a comprehensive overview on the effect of channel and hardware impairments in massive MIMO systems. First we introduce the basic concepts of massive MIMO systems and explain how they give the ability to the users to use the same physical layer resources (frequency band and/or time slots), simultaneously. After studying advantages and challenges of massive MIMO systems, we will give a brief review of the candidate waveforms for massive MIMO systems and introduce the two strong candidate waveforms: Orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain processing (SC-FDP). Next, we will specifically focus on the issue of hardware impairments and channel time variation in massive MIMO systems considering the two main waveform candidates. Since in massive MIMO systems we have large number of antennas at the BS, we have to use low-cost RF chains to have a cost effective system. This results in hardware impairments and degrades the performance of the system. Also, the performance of massive MIMO systems is very dependent on channel knowledge at the BS side. Due to Doppler effects, the channel varies in time (especially for high speed environments) and, therefore, estimating the channel and following its time variations is difficult. Therefore, paying attention to these two issues in massive MIMO is very important. Table 1 summarizes the challenges of massive MIMO systems studied in this paper and previous surveys. The rest of the paper is organized as follows. In Section 2 the basic concepts of massive MIMO are explained. Section 3 explains the advantages and challenges of massive MIMO systems. Section 4 introduces the candidate waveforms for massive MIMO systems. Sections 5 and 6 address the hardware impairment and channel variation issues, respectively. Finally, Section 7 concludes the paper.
# Soil Texture Estimation Using Radar and Optical Data from Sentinel-1 and Sentinel-2
## 2 Materials and Methods
### Satellite Data
#### 2.3.2 Sentinel-2 Data  
Sentinel-2A (S-2A) is a spaceborne spectral imager, which was launched by the European Space Agency (ESA) in June 2015 as part of the \"Copernicus\" program. It is accompanied by Sentinel-2B (S-2B), launched in March 2017, which allows a repeat cycle of 5 days to be achieved using both satellites. This constellation takes images in 13 spectral bands, at visible and mid-infrared wavelengths and at various spatial resolutions (10, 20, 60 m), as shown in Table 2.  
Eight cloud-free S-2 images acquired between July and August 2017, were used for the present study. These optical images were obtained from the THEIA website at the French Land Data Center ([[https://www.theia-land.fr/](https://www.theia-land.fr/)]([https://www.theia-land.fr/](https://www.theia-land.fr/))), which provides data in the level-2A format, including radiometric and atmospheric corrections, based on the method developed by [PERSON] et al. (2013).  
In this study, bands 4 (B4), 8 (B8), 9 (B9), 11 (B11) and 12 (B12) are used to generate the soil and vegetation indices presented in Section 3.1.  
To retrieve bare soil areas and produce soil moisture maps, normalized difference vegetation index (NDVI) maps were derived from the optical images, using B4 and B8. The NDVI pixel values within each reference field were averaged, to characterize the vegetation conditions of that field. Consequently, an NDVI threshold value of 0.25 was used to retain only bare soils. The use of NDVI highlights the difference between crop and soil.
###### Abstract  
Scientific evidence shows that breathing or aerobic programs can improve the quality of life of asthma patients. The aim of this work was to find out the influences of breathing exercises and breathing exercises combined with aerobic exercise on changes in spirometry parameters in patients with bronchial asthma. Participants: The group consisted of 33 women with bronchial asthma--mild to moderate persistent levels of FEV1 reduction (80-50%)--with a mean age of 34.73 \(\pm\) 1.53 years. They were randomly assigned to experimental group 1 (EX1), experimental group 2 (EX2) or the control group (CG). Materials and methods: Changes in spirometry parameters were evaluated over a 16-week period in the three groups: CG (placebo), EX1 (breathing exercises) and EX 2 (combination of breathing exercises with an aerobic program). To evaluate the pre-training and post-training diagnostics, we used MIR Spirobank II. The influences of the experimental and control factors were assessed using the following dependent variables: forced vital capacity (FVC), forced expiratory volume in one second (FEV1), Tiffeneau-Pinelli index (FEV1/FVC ratio), peak expiratory flow (PEF) and forced mid-expiratory flow (FEF25-75%). We used the Wilcoxon t-test and the Kruskal-Wallis test to evaluate the differences in the measured parameters. To examine the effect of our protocols, we used effect size (ES). Results: In CG we observed improvements in: FVC--(5%; \(p<0.05\); ES = 0.437). FEV1--(7.33%; \(p<0.01\); ES = 0.585). FEV1/FVC ratio (5.27%; \(p<0.01\); ES = 0.570). PEF (11.22%; \(p<0.01\); ES = 0.448). FEF25-75% (7.02%; \(p<0.01\); ES = 0.628). In EX1 we observed improvements in: FVC (5.23%; \(p<0.01\); ES = 0.631), FEV1 (20.67%; \(p<0.01\); ES = 0.627), FEV1/FVC ratio (16.06%; \(p<0.01\); ES = 0.628), PEF (13.35%; \(p<0.01\); ES = 0.627) and FEF25-75% (13.75%; \(p<0.01\); ES = 0.607). In EX2 we observed improvements in: FVC (9.12%; \(p<0.01\); ES = 0.627), FEV1 (27.37%; \(p<0.01\); ES = 0.626), FEV1/FVC ratio (15.32%; \(p<0.01\); ES = 0.626), FEV1/FVC ratio (15.32%; \(p<0.01\); ES = 0.626), FEF3/6 (58.99%; \(p<0.01\); ES = 0.626). Significant differences compared to the control group were observed in EX1 for FEV1 (\(p<0.05\)) and FEV1/FVC ratio (\(p<0.01\)); and in EX2 for FEV1 (\(p<0.05\)), FEV1/FVC ratio (\(p<0.01\)), PEF (\(p<0.05\)) and FEF (\(p<0.05\)). A significant difference between EX1 and EX2 was observed in FEF (\(p<0.05\)). Conclusions: It appears to be that combination of breathing exercises with aerobic activities is a more beneficial option for patients with bronchial asthma.  
bronchial asthma; breathing exercises; aerobic activity; spirometry
# Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank
## 3 Novel Dynamic BAT and States
### Overall Procedure of the Proposed Method  
The overall pseudo-code for the proposed method, based on the New BAT in Section 3.5 along with the novel state concepts discussed in Sections 3.1-3.4, in estimating the number of the wildfire propagation areas is presented in Algorithm 4. ```
Input The map including all areas we are interested in estimating the wildfire propagation Output The total probabilities of the wildfire propagation areas. 1: Build the network model for the area map by letting the city/down/important area to be node and links to connect these areas that are neighbor to each other. 2: Generate and normalize the adjacency matrix based on each column. 3: Calculate the PageRank values for each node based on the degree of nodes as shown in Equation (4). 4: Find out all node subsets of each node as discussed in Section 3.1. 5: Calculate the state PageRank values (including the maximum-state PageRank values) and state probabilities based on Section 3.4. 6: Use the maximum-state PageRank values to decide which areas need to protect in the beginning, middle, and final stages of the wildfire propagation. 7: Use the proposed the New BAT provided in Section 3.5 to calculate Pr(\(i\), N\({}_{\text{area}}\)) for all nodes \(i\) and N\({}_{\text{area}}\) = 1, 2,..., \(|V|\). ```  
**Algorithm 4** The overall procedure of we proposed method. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(I\) & \(i\) & \(\mathbf{V(i)}\) & **Label 1** & **Label 2** & \(\mathbf{X(i)}\) & \(\mathbf{T^{\sharp}}\) & \(\mathbf{T_{I}=T_{I-1}\cup S^{\sharp}}\) & \(\mathbf{X}\) & **Probability \({}^{*}\)** \\ \hline
0 & 3 & \([7,6,5]\) & 1 & 001 & 1 & [5] & \([3,5]\) & \((1/3)\) & P\({}_{3,5}\) \\
1 & 5 & \([6,4,3,2,1,0]\) & 0 & 000000 & 0 & \([3,5]\) & \((1/3,0/5)\) & P\({}_{3,5|}\)P\({}_{3,5|}\)P\({}_{5,6}\) \\
2 & 5 & \([6,4,3,2,1,0]\) & 1 & 000001 & 1 & [5,7] & \([3,5,0,7]\) & \((1/3,1/5)\) & P\({}_{3,5|}\)P\({}_{5,10|}\) \\
3 & 0 & \([7,5]\) & 0 & 00 & 0 & \([3,5,0,7]\) & \((1/3,1/5,0/0)\) & P\({}_{3,5|}\)P\({}_{5,0|}\)P\({}_{0,0}\) \\
4 & 7 & \([6,4,3,2,1,0]\) & 0 & 000000 & 0 & \([3,5,0,7]\) & \((1/3,1/5,0/0,0/7)\) & P\({}_{3,5|}\)P\({}_{5,0|}\)P\({}_{0,0|}\)P\({}_{7,6}\) \\
5 & 7 & \([6,4,3,2,1,0]\) & 1 & 000001 & 1 & 0 & \([3,5,0,7]\) & \((1/3,1/5,0/0,1/7)\) & P\({}_{3,5|}\)P\({}_{5,0|}\)P\({}_{0,0|}\)P\({}_{7,1|}\) \\ \hline \hline \end{tabular}  
* decimal labels 2 binary state labels \({}^{*}\)\(p_{i,d}=\Pr(S_{i}(i))=\Pr(X(i))\), where node subset \(i\) is the \(j\)th state of node \(I\) and \(X(i)=j\).  
\end{table} 
Table 8: Proposed BAT procedure to obtain the first state vector with five coordinates.