esa-sceva/satcom-chunk-collection huggingface dataset:
# Testbed for Experimental Characterization of Indoor Visible Light Communication Channels
## 3 Experimental Testbed  
Most of experimental demonstrations of proposed modulation and multiple access methods consider laboratory experiments under very controlled conditions. Misalignment or light reflection effects are out of the analysis, therefore leading to best-case studies. On the other hand, real-time systems are aimed to evaluate the possibilities of VLC at real distances but avoiding considering worst-case scenarios, such as bad alignment between emitting lamps and receiver [17; 27; 28]. In this work, we propose the design of a testbed which allows us to evaluate these effects usually overlooked from an experimental point of view. The testbed is a mockup where several lamps with different arrangements can be located and where the position and orientation of the receiver can be controlled. The reduction to a small scale implies that the VLC channel dispersion observed in a real room scenario no longer stands. However, as we will see in Section 4, these effects are little significant at practical data rates for VLC systems and they can be neglected. In addition, the emission power of WLEDs can be adjusted to obtain a similar scenario to that of a real room considering illumination and received power levels, so the testbed results can be extrapolated to real indoor VLC environments.
# Nematoidal Properties of Wild Strains of _Pleurotus ostreatus_
###### Abstract  
Plant pathogenic nematodes play an important role in crop production and are difficult to control. One of them is _Heterodera schachtii_--a cyst nematode, pathogenic to sugar beet. Nature suggests a natural way to resolve this problem by using nematode-trapping fungi, one of which is _Pleurotus ostreatus_. It is one of the most widespread mushrooms in the world. It is a valuable and widely cultivated edible mushroom with nematocidal properties. The mycelium of this mushroom produces toxin droplets that paralyze nematodes, after which the nematodes become infected with the hyphae filament, resulting in their death. This feature can be put to practical use as a natural nematocidal agent. In this paper, we have described studies on the variability of the nematocidal properties in the progeny of three wild strains of _P. ostreatus_ (Po1, Po2, Po4), obtained by crossing dikaryons with monokaryons (Buller phenomenon). The toxicity of mycelium was tested against the model organism _Caenorhabditis elegans_ and against sugar beet pest--_H. schachtii_. The results of the study allowed the selection of the progeny heterokaryotic mycelia with the best parameters useful for in vitro control of both nematodes. They differed in the activity against C. elegans and H. schachtii, and in the toxic knobs production. The results indicated that the progeny of the Po1 strain presented a good ability to produce hyphal knobs in the presence of C. elegans, and progenies of the Po4 strain presented good quality of growth in preferred temperatures (10-25 \({}^{\circ}\)C). Three strains Po1 5 dix27, Po2 20 dix21, and Po4 2 dix1, as well as a maternal strain Po4 controlled H. schachtii by entwining cysts better than other strains. These strains were moderately effective against C. elegans. Strains Po2 15 dix17, Po4 1 dix18, and Po4 1 dix30 may be considered good controlling isolates against both tested organisms. The results of the research also clearly suggest that the killing properties of _P. ostreatus_ mycelia should not be assessed only by their ability to produce toxic hyphal knobs. Their nematocidal properties also depend on other mechanisms developed by mycelia, which is observed as the lethality of nematodes. The results of this research will lead to a natural way to protect plants against nematodes. The research also proved the nematocidal properties of the wild strains to reduce the _H. schachtii_ population in the soil.  
nematocidal properties; Buller phenomenon; crossing; nematode; _Pleurotus ostreatus_; hyphal knobs
## 4 Implementation and Application Scenarios
### Application Scenario for Automated Context-Aware Service  
The diagram representing the application scenario for automated context-aware service according to the following sequence is demonstrated in Figure 18.  
1. Agricultural data are acquired by the WSA deployed in a maize field of the \"Kopaida\" farm (as in Table 5) and transmitted to the gateway.
2. The gateway forwards the raw data to the context-aware middleware cloud into the Microsoft Azure platform.  
Figure 17: Indicative Service Rule in XML format.  
Figure 18: Sequence Diagram for Automated Context-aware Service.  
3. Into the context-aware middleware cloud the raw data are classified as high-level contextual data (as in Table 6) and it appears that a set of them involves unusual values (No 8 of Table 6). The context management module after performing context correlation with the provided services identifies the most suitable service rule which corresponds to imminent plant disease conditions (high air temperature, soil moisture use, and soil pH range).
4. The contextual data are transmitted to the cloud server running in the Azure platform and stored in a data source as context history.
5. The service rule is converted to XML code and sent to the cloud server running in the Azure platform as a SOAP response.
6. The cloud server receives the service rule and sends the relevant action command to alert the farmer via a pop-up mobile application running in the cloud platform (in the case of the simulated model a flag is set in the device's class object).
7. The pop-up mobile application involves a conversational user interface for communication with the farmer in a question-answer session. The form of the question for the current instance is indicatively \"Imminent disease conditions identified. Request real-time agronomist support?\". The result of the conversation is sent to the context-aware middleware cloud to receive more specific context as \"Consultancy request is positive\".
8. The context management module of the middleware runs a service-mapping operation with the new context (consultancy request) and identifies new service actions based on the context value. (For positive consultancy request it selects the service \"Consultancy\" as in Table 3).
9. The record of the conversation is also sent to the data source of the cloud server to be stored as context history for possible future usage.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Restrictive Boltzmann Machines for Speech Synthesis  
In recent years, restricted Boltzmann machines (RBMs) [40] have been widely used for modeling speech signals, such as speech recognition, spectrogram coding and acoustic-articulatory inversion mapping [40]. In these applications, RBM is often used for pre-training of deep auto-encoders (DAEs) [41; 42] or DNNs. In the field of speech synthesis, RBM is usually regarded as a density model for generating the spectral envelope of acoustic parameters. It is adopted to better describe the distribution of high-dimensional spectral envelopes to alleviate the over-smooth problem in HMM-based speech synthesis [40]. After training the HMMs, a state alignment is performed for the acoustic features and the state boundaries are used to collect the spectral envelopes obtained from each state. The parameters of the RBM are estimated using the maximum likelihood estimation (MLE) criterion. Finally, RBM-HMMs are constructed to model the spectral envelopes. In the synthesis phase, the optimal spectral envelope sequence is estimated based on the input sentence and the trained RBM-HMMs. Although the subjective evaluation result of this method is better than that of traditional HMM-GMM systems, and the predicted spectral envelope is closer to the original one, this method still cannot solve the fragmentation problem of training data encountered in the traditional HMM-based method.
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
###### Abstract  
This paper considers a wireless energy harvesting two-way relay (TWR) network where the relay has energy-harvesting abilities and the effects of practical hardware impairments are taken into consideration. In particular, power splitting (PS) receiver is adopted at relay to harvests the power it needs for relaying the information between the source nodes from the signals transmitted by the source nodes, and hardware impairments is assumed suffered by each node. We analyze the effect of hardware impairments on both decode-and-forward (DF) relaying and amplify-and-forward (AF) relaying networks. By utilizing the obtained new expressions of signal-to-noise-plus-distortion ratios, the exact analytical expressions of the achievable sum rate and ergodic capacities for both DF and AF relaying protocols are derived. Additionally, the optimal power splitting (OPS) ratio that maximizes the instantaneous achievable sum rate is formulated and solved for both protocols. The performances of DF and AF protocols are evaluated via numerical results, which also show the effects of various network parameters on the system performance and on the OPS ratio design.  
energy harvesting; two-way relay; hardware impairments; ergodic capacity; optimal power splitting +
Footnote †: journal: Sensors
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 2 Materials and Methods
### Feature Screening  
There are two main objectives for feature screening. One is to identify the essential features that have a substantial impact on the target variable and explore each essential feature's impact on the result. The other is to minimize the input dimensions, speed up the training process, and make the model interpretable and universal [41; 42]. The selected features should be both easily accessible and responsive to the creep rupture life. In this study, Pearson correlation screening and sequential feature selection methods are used for feature screening. The Pearson correlation coefficient is generally used to examine the strength of the linear relationship between any two features. In the feature screening process, the Pearson correlation analysis will provide a preliminary filter of features that exhibit strong linear correlation. The Pearson correlation coefficient is obtained according to the following formula:  
\[r=\frac{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}}{ \sqrt{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X})^{2}}}\sqrt{\sum\limits_{i=1}^{ n}{(Y_{i}-\overline{Y})^{2}}}} \tag{1}\]  
where \(n\) is the total number of samples in the dataset, \(X_{i}\) and \(Y_{i}\) are the values of features \(X\) and \(Y\) of the \(i\)th sample, and \(\overline{X}\) and \(\overline{Y}\) represent the average values of features \(X\) and \(Y\) of all samples, respectively. Any two input features that have an absolute value of r greater than 0.95 are considered to be linearly dependent, meaning that they provide comparable information and thus have comparable impacts on the output. The feature that has the lower \(r\) with the output is thus deemed to be less significant and is then removed. After correlation screening, the retained features are analyzed using the SHapley Additive exPlanation (SHAP) method [43], which is capable of reflecting the contribution of each feature to the prediction of the model. The global effect of the features is determined and the features are ordered from highest to lowest importance to the prediction. Subsequently, the sequential forward selection method is employed to choose the optimum set of features that can describe the data within a given machine learning model. This approach is a bottom-up search process in which features are sequentially added to an empty candidate set until the addition of additional features results in the highest model accuracy [44]. In this study, the input features are gradually added to the regression model following the order of feature importance provided by the SHAP approach. The critical number of features is determined when there is a negligible improvement of \(R^{2}\) or no decrease of RMSE. Based on the above selection process, the essential features that significantly affect
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Functional Validation
#### 4.3.5 Validation Results
To guarantee proper timing synchronization in the throughput figures, the clocks of the three multicast router VNFs were synchronized using _chrony_, an open-source implementation of the Network Time Protocol (NTP) [34]. End-to-end delays are not noticeable in the throughput measurements, given the low round-trip times that exist between sites (2.861 ms between sites \(A\) and \(B\), and 10.589 ms between sites \(A\) and \(C\)).
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 5 Compressive Sensing  
The celebrated Shannon/Nyquist sampling theorem states that in order to reconstruct a signal without losing information, the sample rate must be at least two times larger than the signal bandwidth. So far, most signal acquisition techniques rely on this principle. As mentioned in Section 4.1, transform coding plays a key role in data acquisition systems. However, this compression framework suffers from several drawbacks [38]. For instance, consider a discrete signal \(\mathbf{x}\in\mathbb{R}^{N}\) with entries \(x[n]\), \(n=0,1,\cdots,N-1\). An image or a higher-dimensional data structure can be also vectorized into a long one-dimensional vector. It is known that any signal in \(\mathbb{R}^{N}\) can be represented in terms of a basis of \(N\) vectors \(\{\boldsymbol{\psi}_{i}\}_{i=0}^{N-1}\). In this case,  
\[\mathbf{x}=\sum_{i=0}^{N-1}s_{i}\boldsymbol{\psi}_{i}=\boldsymbol{\
abla} \mathbf{s},\]  
where \(\mathbf{s}\) stands for the weighting coefficients and \(\boldsymbol{\
abla}\) is the basis matrix with columns \([\boldsymbol{\psi}_{0},\cdots,\boldsymbol{\psi}_{N-1}]\). The signal \(\mathbf{x}\) is called \(K\)-sparse if only \(K\) coefficients of \(\mathbf{s}\) are non-zero and the remaining \(N-K\) coefficients are roughly zero (or can be truncated to zero). Thus, in order to process the transform coding, the locations of the \(K\) non-zero coefficients must be coded, which results in an overhead. Moreover, the \(N-K\) zero coefficients still need to be computed even though they will be discarded.  
Compressive sensing addresses these issues by directly processing the compressed signal without acquiring the original \(N\)-sample signal provided that the signal is \(K\)-sparse [39]. This novel sampling method dramatically undersamples the signal at a greatly reduced rate. Next we will present the mathematical model behind this new technique.  
Consider the following representation [38]:  
\[\mathbf{y}=\boldsymbol{\Phi}\mathbf{x}=\boldsymbol{\Phi}\boldsymbol{\
abla} \mathbf{s}=\boldsymbol{\Theta}\mathbf{s}, \tag{41}\]  
where \(\mathbf{x}\in\mathbb{R}^{N}\) stands for the original signal, \(\mathbf{y}\in\mathbb{R}^{M}\) (\(M\ll N\)) denotes the compressed signal, and \(\boldsymbol{\Phi}\in\mathbb{R}^{M\times N}\) represents the linear measurement process. Mathematically, the problem can be formulated as follows. First, find a matrix \(\boldsymbol{\Phi}\) such that \(\mathbf{y}=\boldsymbol{\Phi}\mathbf{x}\) preserves most of the information embedded in \(\mathbf{x}\). Second, find an algorithm to recover \(\mathbf{x}\) from only \(M\) samples of \(\mathbf{y}\).  
Figure 8: Haar Wavelet transform.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 3 Dedicated Short-Range Communication (DSRC)
### IEEE 802.11 bd DSRC  
The next generation DSRC technology, namely IEEE 802.11 bd DSRC, is introduced to overcome problems with interferences and NLOS operation faced by DSRC based on IEEE 802.11p. In the current version of IEEE 802.11 bd, Doppler shifts are counteracted by the introduction of midambles, analogous to preambles but located at the center of the frame, to provide reliable channel estimates with the fast varying channels experienced during vehicle motion [56; 57]. In addition, multiple retransmissions are allowed in IEEE 802.11 bd, in contrast to IEEE 802.11p, to enhance the reliability of the communication link. Furthermore, physical layer latency, also known as transmission latency within the air  
Figure 3: WAVE protocol stack showing management plane and data plane.  
interface, is smaller for IEEE 802.11 bd in comparison with IEEE 802.11p. This is explained by the authors in [58] who clarify that IEEE 802.11 bd allows higher order modulation and coding schemes including 256-QAM, which were not utilized in IEEE 802.11p. In addition, four more data carriers are available in 802.11 bd. Collectively, these features guarantee better EV charging coordination and management, particularly in dynamic and quasi-dynamic charging modes where EV energy updates need to be actively communicated to the infrastructure, and charging activation, coordination, and billing messages need to be exchanged with minimal interference to ensure privacy of the exchanged messages.
## 4 Performance Analysis
### Outage Probability  
The OP is an important performance metric of wireless communications systems operating over fading channels. It is defined as the probability that the instantaneous SNR at the receiver output, \(\gamma\), falls below a predefined outage threshold, \(\gamma_{\rm th}\). Based on this definition, the outage probability of a wireless communication system over fading channels can be expressed as  
\[P_{out}=\Pr(0<\gamma<\gamma_{th})=\int_{0}^{\gamma_{\rm th}}f_{\gamma}(\gamma) d\gamma. \tag{35}\]  
Therefore, the corresponding OP is readily deduced as follows  
\[P_{out}=F_{\gamma}(\gamma_{th}), \tag{36}\]  
where \(F_{\gamma}(\gamma_{th})\) can be obtained by using (8), (11), (21), (25), (31), and (34) replacing \(\gamma\) with \(\gamma_{th}\) over different fading conditions, respectively.
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 3 Proposed Association Utility
### Proposed Utility  
We define here a new utility that takes into account both security and power consumption. In particular, we define the secure energy efficiency (\(EE^{[SEC]}\)).  
\[EE^{[sec]}=\sum_{u,c}EE^{[sec]}_{u,c}=\frac{\sum_{u,c}\gamma_{u,c}\overline{T }_{u,c}^{\text{[sec]}}}{P_{tot}} \tag{8}\]  
where  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**BS Type** & \(P_{max}\) **[W]** & **\(P_{0}\) [W]** & \(P_{idle}\) **[W]** & \(\Omega_{p}\) \\ \hline Macro & 20.0 & 130.0 & 75.0 & 4.7 \\ Micro & 6.3 & 56.0 & 39 & 2.6 \\ Pico & 0.13 & 6.8 & 4.3 & 4.0 \\ Femto & 0.05 & 4.8 & 2.9 & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Power model parameters for different base stations (BSs).  
* \(\gamma_{u,c}\) is the element \((u,c)\) of the allocation matrix \(\mathbf{\Gamma}\), whose value is \[\gamma_{u,c}=\left\{\begin{array}{ll}1&\text{if user $u$ is associated with cell $c$}\\ 0&\text{otherwise}\end{array}\right.\] (9)
* \(EE^{[sec]}_{u,c}\) is the energy efficiency of the \(u\)-th user served by the \(c\)-th cell.
* \(P_{tot}\) is the total power consumption of the network considering both active and idle cells.  
Let us define \(\mathbf{\Phi}=[\Phi(1),\cdots\Phi(c),\cdots\Phi(\mathcal{N}_{\mathcal{C}})]\) the vector of length \(\mathcal{N}_{\mathcal{C}}\) (i.e., the number of cells in the area), whose element \(\Phi(c)\) is one if the \(c\)-th cell is active, zero otherwise. Consequently, \(P_{tot}\) can be written as  
\[P_{tot}=\sum_{c=1}^{\mathcal{N}_{\mathcal{C}}}\Phi(c)(P_{0}+\Omega_{p}P^{[ \text{TX}]})+(1-\Phi(c))P_{idle} \tag{10}\]  
Given a pattern of active cells, \(\mathbf{\Phi}\), and an association matrix, \(\mathbf{\Gamma}\), it is possible to calculate the SINR as well as the secure area, the average secure throughput and the proposed utility (8), provided by that association between UEs and cells. As an example, let us suppose to have one user and three cells as in Figure 2. Depending on which cell the user is associated with, different SINR, secure area, and secure EE values can be obtained. The user selects the cell that maximizes the selected utility. For example, in Figure 2, in the case (A) the UE selects the cell-2 if the SINR must be maximized, or the cell-3 if the secure area or the secure EE must be maximized. Differently, in case (B) the cell-1 is selected if the considered utility is the SINR or the EE, while the cell-2 for the achieving the maximization of the secure area.
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 5 Simulated Environment and Demonstration  
This section first introduces the simulated environment and then demonstrates the results of applying this algorithm to calculate the inner points of targeted areas. Next, we offer targets within three selected fields of hundreds to thousands of square kilometers calculated from more than one million targets.
## 4 Results and Analysis
### Solution of the Stochastic Planning Problem
#### 4.2.1 Scenario Identification (Fault Start Time)  
Since it is necessary to plan microgrids capable of responding to different fault scenarios it was necessary to identify these scenarios. To do this, [PERSON]'s hierarchical agglomeration method was used (Section 2.2), which was applied to the distribution of the possible power consumption of the 8 h fault. As an example, Figure 11 shows three fault scenarios, calculated from the annual distribution of an 8 h fault consumption for a residential household. Each value of the distribution corresponds to the total energy that would have been consumed during the next 8 hours given a fault occurring in hour h; these values are then sorted from highest to lowest; this corresponds to the \"Fault Energy Demand Duration Curve\". These scenarios try to adequately represent all possible fault scenarios (8753 scenarios). It is observed that, when applying [PERSON]'s method, considering the formation of three clusters, the method includes within the same group all the fault energy demands (bins) that have similar distances to the centroid \(m_{k}\) of its cluster \(k\).  
Furthermore, given the average value of the fault energy demand for each cluster, Table 7 presents the three calculated scenarios, with the fault start time and its probability of losing that amount of energy.  
Figure 11: Annual 8 h power fault demand duration curve, for a residential household, with scenarios arranged in descending order.  
According to the results of this example of a residential household, it can be affirmed that its worst scenario corresponds to scenario three, since it is the one with the highest average energy demand during eight continuous hours of fault (4.25 kWh). [PERSON]'s method was also applied to obtain the scenarios of various groups of 10, 20 and 100 residential households, with which hundreds of stochastic simulations are carried out to determine the type and size of the community microgrid, as presented below.
## Materials and Methods
### Module Description  
In this section, we describe in detail the workflow steps. For a successful run of the workflow, the following datasets must already be available to the system:  
1. High spatial resolution satellite images of the study area. In cases in which we have an image time series, one of the images is declared as the master image, onto which all other images are geometrically registered.
2. A DEM that covers the satellite image footprint and that is used in the orthorectification phase, if that phase is necessary.  
Figure 3: The processing chain of the STARS automated satellite image workflow.  
If these datasets exist, the sequential processing steps described below, controlled through a bash script, can be run successively. An alternative approach that makes use of the same scripts is provided with make, and we discuss this in Section 3.4.