esa-sceva/satcom-chunk-collection huggingface dataset:
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 2 IPL Channel Statistical Description
### MGF of the Instantaneous SNR  
**Theorem 1**.: _The moment-generating function \(\mathcal{M}_{\gamma}(s)\) of the IPL channel and its high-SNR asymptotics are given by_  
\[\left\{\begin{aligned} \mathcal{M}_  
\[\mathcal{M}_{\gamma}(s) =\frac{(s\tilde{\gamma})^{-\beta}}{\Gamma(\alpha)}\Omega_{\alpha, \beta}\mathrm{H}^{1,2}_{2,1}\bigg{(}\frac{\Omega_{\alpha,\beta}}{(s\tilde{ \gamma})^{\beta}}\bigg{|}\begin{pmatrix}0,1),(-\beta,\beta)\\ (\alpha-1,1)\end{pmatrix} \tag{14a}\] \[=\frac{1}{\Gamma(\alpha)}\mathrm{H}^{1,2}_{2,1}\bigg{(}\frac{ \Omega_{\alpha,\beta}}{(s\tilde{\gamma})^{\beta}}\bigg{|}\begin{pmatrix}1,1 ),(0,\beta)\\ (\alpha,1)\end{pmatrix}. \tag{14b}\]  
To obtain a high-SNR asymptotic expression for (14b), one can resort to [53] (see expression (3) and (4) in [53]). In this case, one obtains  
\[\mathrm{H}^{1,2}_{2,1}\bigg{(}\frac{\Omega_{\alpha,\beta}}{(s\tilde{\gamma})^ {\beta}}\bigg{|}\begin{pmatrix}1,1),(0,\beta)\\ (\alpha,1)\end{pmatrix}\bigg{)}\bigg{|}_{\tilde{\gamma}\to\infty}\simeq\bigg{(} \frac{\Omega_{\alpha,\beta}}{(s\tilde{\gamma})^{\beta}}\bigg{)}^{\omega}h_{j}, \tag{15}\]  
where \(\omega\) and \(h_{j}\) are defined in [53] (see expression (3) and (4), respectively). For the IPL model, \(\omega=\alpha\) and \(h_{j}=\Gamma(\alpha)\Gamma(\alpha\beta+1)\), which finally helps to derive the high-SNR asymptotics for the MGF of the IPL model as  
\[\mathcal{M}_{\gamma}(s)\bigg{|}_{\tilde{\gamma}\to\infty}\simeq\frac{\Gamma( \alpha\beta+1)\Omega_{\alpha,\beta}^{\alpha}}{(s\tilde{\gamma})^{\alpha\beta}}. \tag{16}\]  
It must be noted that the univariate Fox H-functions are implemented in most modern computer algebra systems (e.g., Wolfram Mathematica, MATLAB, Maple, etc.). Thus, (14b) can be easily computed for arbitrary channel parameters, although the computational time can vary, and for some values of \(\alpha\) and \(\beta\), evaluations can be time-consuming. To speed up the computations, the integral representation of the Fox H-function can be implemented, and efficient numerical integration strategies can be employed. In this case, the MGF (14b) can be rewritten in the following form:  
\[\mathcal{M}\gamma(s)=\frac{1}{\Gamma(\alpha)}\frac{1}{2\pi i}\int\mathcal{L} \Gamma(-\sigma)\Gamma(1-\beta\sigma)\Gamma(\alpha+\sigma)\bigg{(}\frac{\Omega _{\alpha,\beta}}{(s\tilde{\gamma})^{\beta}}\bigg{)}^{-\sigma}\mathrm{d}\sigma, \tag{17}\]  
where \(\mathcal{L}\) is the contour chosen in such a way as to separate the poles of the gamma function with opposite signs for the integration variable. For instance, it can be chosen as \(\mathcal{L}=\left(-\frac{\alpha}{2}-i\infty;-\frac{\alpha}{2}+i\infty\right)\).
## 2 Materials and Methods
### Image Data Collection and Processing  
#### 2.2.1 UAV Platform and Imagery  
UAV flights covering the within-field areas of interest for each farm site (Figure 1) were carried out during three LAI ground measurement dates in each season. The UAV survey consisted of flying a hexa-copter (DJI Matrice 600) at a height of 100 m above ground level and a constant speed of 3.1 m s\({}^{-1}\). The platform was equipped with a real-time kinematic GNSS and was flown autonomously using a pre-planned flight mission, created using the DJI Ground Station Pro software, for each of the within-field areas, in order to ensure consistent and comparable multi-date coverage.  
Multispectral imagery was acquired throughout the UAV flights using a MAIA camera system (SAL Engineering/EOPTIS), which is composed of an array of nine 1.2-megapixel monochromatic sensors that collect data simultaneously using a global shutter. The nine sensors of the MAIA camera have band-pass filters that have the same central wavelength as that of the first nine bands (i.e., bands 1 to 8A) of the ESA Sentinel-2 Multispectral Instrument [45]. During the UAV flights, these sensors imaged the field areas from a nadir position that was maintained using a 3-axis stabilisation gimbal (DJI Ronin-MX). In order to ensure complete coverage of the flying area, successive imagery along the flight lines was acquired with a 20% to 30% overlap. At the above-ground flying altitude the MAIA camera system had a ground sampling distance of approximately 4.7 cm and field-of-view of \(60\times 45\) m\({}^{2}\).
# A Review of the Influence of Steel Furnace Slag Type on the Properties of Cementitious Composites
## 4 Discussion of SFS Variability  
Certainly, Tables 2-4 present a view of SFS that there can be significant variability in composite materials' behavior. First and foremost, this variability stems from different steelmaking processes (e.g., EAF, BOF, LMF, AOD), different steel chemistries, different slag cooling methods, different fluxing agents, and so forth. Therefore, each stockpile of SFS can have variable chemical composition, mineralogical composition, free CaO content, free MgO content, particle size distribution, particle shape and texture, porosity, and so forth. Table 5 shows an example dataset from the literature where the same concrete mix design was used with equivalent volumetric replacements of the coarse aggregate by EAF slag or BOF slag relative to a control. In this example, the compressive strength increased with EAF slag but decreased with BOF slag, while both SFS aggregates increased the fracture properties and free drying shrinkage [9]. In a subsequent study, these same aggregates were investigated by backscattered electron microscopy, and it was found that the BOF slag aggregates exhibited a larger, more porous interfacial transition zone (ITZ) than the EAF slag or dolomite aggregates, which exhibited similar ITZ characteristics [10]. By microscopic analysis, the BOF slag was also found to exhibit a different surface composition than the interior, while the EAF slag did not exhibit a different surface composition, which was seen to be one explanation why the BOF slag reduced compressive strength while EAF slag increased strength in Table 5. Table 6 shows a related dataset for mortars with equivalent volumes of dolomite, EAF slag, BOF slag, or LMF slag aggregates, where it can be seen again that EAF slag achieved greater mechanical properties than the control, whereas BOF and LMF slag aggregates experienced reductions in strength, possibly attributable to the surface composition. The different exterior and interior compositions of SFS particles have been shown for BOF and LMF slag [10; 114; 123; 251] but not EAF slag [10; 251]. For BOF slag, [PERSON] et al. reported an exterior particle surface composition containing calcite, calcium silicate hydrate, and calcium carboalumnate hydrate phases, which was argued to result in a reduced paste aggregate bond [123]. Similar arguments can be made for aged vs. unaged SFS aggregates, since the properties of SFS aggregates will change when they are aged, weathered, or carbonated, such as reduction in aggregate porosity [114; 169], decrease in pH [124; 252; 253; 121], decrease in chemically absorbed water [124], and alteration of the aggregate surface texture and composition [124; 169; 254; 255; 256; 257]. Conversely, increases in concrete strength when using SFS aggregates have been argued to be attributable to the rough particle surface texture and
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 5 TTN Server Configuration and Application Server Design
### Configuration of the Server Application  
The application server will be an HTTP web server in charge of sending requests to the database implemented in the TTN server. It will also modify the data to transform it into a readable format. The web server will host an interpreter that will be executed continuously as a service of the operating system of the machine. This program is written in Python and it is mainly based on three functions:  
* RequestHTTP(): This will be responsible for performing HTTP requests to the database, implemented on the TTN server. To do that, the system will use the Python module called \"requests\" which allows performing HTTP requests. The database will determine the time interval of the collected data by a parameter of the HTTP request, using the sequence of characters \"1h\". The response of the DB is kept in a variable that the JSON module will interpret as a vector of JSON objects. Finally, the last object, which correspond to the last measure performed by the node, will be kept.
* toParse(): This function is in charge of transforming the JSON data into a readable format to be interpreted by the program on the web (i.e., a format that can be drawn by the Google Maps API). It will save the JSON values for a determined node, being CO2 level, relative humidity value, temperature, wind speed, and the time stamp. We have a dictionary with the coordinates of each device (node) and another dictionary in a JSON format embedded in the argument of the \"eqfeed_callback\" function that is readable for the web application. It will save the values of the previous variables, identify the source node and, save the value of its coordinates in the dictionary. Finally, a file to save the data with *json extension will be created.
* main(): It is the main function of the program and will be in charge of calling both the \"RequestHTTP()\" and \"toParse()\" functions.
* eqfeed_callback(): it creates a file where the coordinates of the nodes that exceed the values imposed by the 30-30-30 rule will be saved. It gets the values of the JSON objects from the \"data.json\" file. It defines a marker that indicates fire if the value of the CO2 concentration exceeds the 700 ppm threshold and displays an alert by the screen. This function also defines the information windows that will be displayed by clicking on the markers and their format. Finally, the function is in charge of painting both the markers and the heat map, in case the defined thresholds have been exceeded.  
Figure 12 shows the workflow diagram of our system. The diagram shows the workflow of our phyton application, which is in charge of checking rules taking into account the received data. Additionally, it calls other functions to, for example, draw the map.
## 3 Methods
### Economic Analysis  
An economic analysis was realized to determine the primary costs associated with the proposed plant and estimate its levelized cost of electricity (LCOE). The analysis implemented was based on the method proposed by [PERSON] et al. [44] and includes the calculation of the total capital investment (TCI) and the total revenue requirement (TRR). As the costs were calculated for the entire economic life of the plant, it was necessary to establish the reference conditions of the economic, technological, and legal framework assumed in the proposed project:  
1. The economic study took place in 2016 with construction starting in 2017 and ending in 2019. Thus, the economic data were calculated in 2016 and were accordingly levelized depending on the year they became effective [44].
2. The capacity factor of the plant was 85% [46].
3. The inflation rate was i = 2.98% [47].
4. The nominal escalation rate (except coal) corresponded to inflation (2.98%).
5. A real escalation rate of 0.5% was assumed for coal [48]. The average growth rate was thus a nominal escalation rate of \(\mathrm{r}_{\mathrm{n}}^{\mathrm{f}}=3.49\%\).
6. The useful life of the hybrid power plant was assumed to be 25 years and its economic life 20 years (both calculated from the beginning of operation).
7. It was considered that the residual value of the plant at the end of the 25 years would be zero.
8. Financing was achieved as follows: - Debt: 65% of the capital at an interest rate of 10.0%. - Common equity: 25% of the capital at an interest rate of 15.0%. - Preferred stocks: 10% of the capital at an interest rate of 11.7%.
9. The electricity sale price used was 105.16 $/MWh (price for Spain in 2016) [49].
10. All taxes were considered constant throughout the life of the plant. 1. Income tax: \(\mathrm{t}_{\mathrm{s}}=25.0\%\)[50]. 2. Property tax (Almeria) in 2016: \(\mathrm{t}_{\mathrm{prop}}=0.46\%\)[51]. 3. Insurance taxes: \(\mathrm{t}_{\mathrm{ins}}=0.5\%\)[44].
11. Operating and maintenance costs were assumed to be 20.0% of purchased equipment cost of the plant (PEC).
12. The price of coal was assumed to be 84.72 $/t [52].
13. It was assumed that 100 employees work in the plant, with an average hourly wage equal to $20.80.  
In order to calculate the total capital investment (TCI), the purchased equipment cost (PEC) is required [53; 54; 55]. The rest of the costs are obtained based on the PEC, according to the percentages proposed by [PERSON] et al. [44]. The TRR is the total revenue requirement, i.e., the total amount that the plant needs to earn within a given year through electricity sales to compensate for the expenditures of that same year [44]. Finally, the LCOE represents the levelized cost of power generation and provides the connection between the necessary investment and electricity generation (E) for the 25 years of useful life (UL) of the plant [44]:  
\[\mathrm{LCOE=}\frac{\sum_{N=1}^{UL}\frac{TRR}{(1+r)^{N}}}{\sum_{N=1}^{UL} \frac{E}{(1+r)^{N}}} \tag{6}\]
## 2 Materials and Methods
### Statistical Analysis
\[\text{FAR}=\frac{\text{N}_{\text{b}}}{\text{N}_{\text{a}}+\text{N}_{\text{b}}} \tag{5}\]  
where \(\text{N}_{\text{a}}\) is the number of correct answers, referring to the number of days in which the precipitation values estimated by the satellite and observed by rain gauges were greater than a predefined precipitation limit, 1 mm/day, in this study, following the criterion adopted by [44,45]; \(\text{N}_{\text{b}}\) is the number of days on which the satellite-based precipitation estimate was greater than 1 mm/day and the rainfall observed by the pluviometer was less than 1 mm/day; and \(\text{N}_{\text{c}}\) is the number of days on which the satellite-based precipitation estimate was less than 1 mm/day and the rainfall observed by the pluviometer was greater than 1 mm/day. The probability density function (PDF) was also used. Denoted by \(\text{f}_{\text{X}}(\text{x})\) the FDP describes the behavior, in polygon form, of the frequency distribution of a random variable. The probability of the random variable being less than a given value of interest, x, is calculated using the cumulative distribution function (CDF), represented by Equation (6):  
\[\text{F}_{\text{X}}(\text{x})=\text{P}(\text{X}\leq\text{x})=\int_{-\infty}^{ \text{x}}\text{f}_{\text{X}}(\text{x})\text{d}\text{x} \tag{6}\]  
The CDF of a continuous random variable is a non descending function, and the expressions are validated: \(\text{F}_{\text{X}}(-\infty)=0\) and \(\text{F}_{\text{X}}(+\infty)=1\)[46, 47, 48, 49]. Conversely, the corresponding FDP can be obtained by differentiating \(\text{F}_{\text{X}}(\text{x})\), The PDF, \(\text{f}_{\text{X}}(\text{x})\), is represented by Equation (7):  
\[\text{f}_{\text{X}}(\text{x})=\frac{\text{d}\text{F}_{\text{X}}(\text{x})}{ \text{d}\text{x}} \tag{7}\]  
The Student \(t\)-test was applied to verify a significant difference between the means (gauge and IMERG), adopting 5% statistical significance. In addition, a simple linear regression model was used to assess the similarity of monthly precipitation variations between the data estimated by IMERG and observed by gauges, Equation (8):  
\[\text{Y}=\beta_{0}+\beta_{1}\text{X} \tag{8}\]  
where Y represents the dataset observed by the rain gauges; \(\beta_{0}\) is the intercept; \(\beta_{1}\) is the slope; and X represents the dataset estimated by the satellite. The closer to zero and one the estimated values, respectively, of parameters \(\beta_{0}\) and \(\beta_{1}\) are, the better the relationship between the gauge and IMERG data is. The determination coefficient (\(\mathbb{R}^{2}\)) represents the goodness of fit of the regression model.
## 2 Results and Discussion
### Enhanced Vegetation Index (EVI) Time Series and Phenology Timing
This is likely due to a combination of increased vertical mixing, changes in background CO\({}_{2}\) mixing ratios, and the 50% reduction in residential emissions from January through March [39] due to the typical early year decrease in heating degree days (HDD): during 2011 in Boston, there were 1156 HDD in January, 961 in February, and 804 in March [47]. As a result, green-up occurred well after the timing of peak CO\({}_{2}\) mixing ratios in Boston. Conversely, CO\({}_{2}\) mixing ratios at all sites began to rise between late June and mid-August, proceeding brown-down by as many as 104 days in Boston's case. The late summer increase in CO\({}_{2}\) was likely not due to changes in residential emissions, but rather decreases in canopy photosynthetic efficiency (associated with foliar aging and decreasing insolation [42]), increasing ecosystem respiration, and changes to background CO\({}_{2}\) mixing ratios. Others have found that rates of gross primary production typically begin to decline in early July [48], consistent with the patterns observed at all sites. In contrast to the patterns in Boston, green-up and brown-down occurred closer to maxima and minima mixing ratios in Harvard Forest. Mixing ratios at Harvard Forest began to decline in mid-May, 25 days before green-up. Biogenic fluxes dominate this rural site, which likely reflects the influence of the coniferous trees in the canopy: conifers begin photosynthesizing as soon as daily mean temperatures are consistently above freezing [42]. The high values of EVI observed in Boston were influenced by urban and suburban lawns, which typically sequester less carbon than forests [49].
## 2 System Model
### Receiver  
A straight-forward receiver model may assume an introduction of additive white Gaussian noise (AWGN) \(\xi(t)\) onto the pass-band signal \(z_{\text{C}}(t)\) on the ground before its demodulation with \(\omega_{\text{IF}}=2\pi f_{\text{IF}}\) and front-end filtering with a ideal brick-wall filter \(h_{\text{FE}}(t)=B\sin(\pi Bt)/(\pi Bt)\triangleq B\sin(Bt)\), where \(B\) denotes the two-sided front-end filtering bandwidth, resulting in the analog receive signal \(r(t)\). This receive signal is then digitized using a sampling rate of \(1/T_{s}\), resulting in the receive signal vector:  
\[\mathbf{r}= \begin{bmatrix}r(0)&r(T_{s})&r(2T_{s})&\cdots&r((N_{\text{s}}-1)T_{s}) \end{bmatrix} ^{\text{T}}\quad\in\mathbb{C}^{N_{\text{s}}\times 1} \tag{5}\]  
where \(N_{\text{s}}\) denotes the number of samples accumulated for a specific integration time and \((. )^{\text{T}}\) denotes the transpose operator. Applying the receive signal vector \(\mathbf{r}\) to a correlator bank  
\[\mathbf{C}= \begin{bmatrix}\mathbf{\varepsilon}^{(0)}&\mathbf{\varepsilon}^{(1)}&\cdots& \mathbf{\varepsilon}^{(n_{c})}&\cdots&\mathbf{\varepsilon}^{(N_{\text{c}}-1)}\end{bmatrix}  \quad\in\mathbb{C}^{N_{\text{s}}\times N_{\text{c}}}\,, \tag{6}\]  
where \(\mathbf{\varepsilon}^{(n_{c})}\) denotes the vectorized signal replica \(\mathbf{\varepsilon}(t-\tau_{n_{c}})\) with the shift \(\tau_{n_{c}}=\Delta((N_{c}-1)/2+n_{c})\) using the uniform correlator spacing \(\Delta\), yields then a noisy, sampled CCF \(\mathbf{\tilde{q}}=\mathbf{C}^{\text{H}}\mathbf{r}\in\mathbb{C}^{N_{\text{c}}\times 1}\) for a specific noise realization of \(\mathbf{\xi}(t)\). Figure 2 shows the generation of the correlator output \(\mathbf{\tilde{q}}\) for the straight-forward model. For simulation purposes, it is convenient to replace the straight-forward receiver model with an equivalent alternative, shown in Figure 3, which adds colored noise \(\mathbf{\tilde{n}}\) directly onto the noise-free version, a sampled CCF \(\mathbf{q}=\mathbf{C}^{\text{H}}\mathbf{s}\in\mathbb{C}^{N_{\text{c}}\times 1}\) resulting in a noisy CCF \(\mathbf{\tilde{q}}\) with identical statistical properties as in the straight-forward model. For the simulation, a vector for different noise power levels has to be computed based on the carrier-to-noise-density ratio (\(C/N_{0}\)) that is to be investigated. The advantage of this Carlo simulation can be achieved with less computational effort since \(N_{s}\gg N_{c}\). From a statistical viewpoint, the system models shown in Figures 2 and 3 are fully equivalent. This is established in the following theorem. **Theorem 1**. : _The addition of the colored noise in the post-correlation domain is statistically equivalent to the addition of noise in the pre-correlation domain when generating the zero mean colored noise vector \(\bar{\mathbf{n}}=\mathbf{D}^{\mathrm{H}}\mathbf{\eta}\) using a white noise vector \(\mathbf{\eta}\sim\mathcal{CN}(0,\sigma^{2}\mathbf{1})\) with \(\sigma^{2}=N_{0}B\) and a coloring matrix \(\mathbf{D}\).
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 4 Proposed Method
### Overview  
The proposed method aims to enable intermediate nodes to generate RREPs as responders in AODV-based secure routing protocols to improve communication efficiency. Intuitively, this objective can be achieved through a procedure in which the node proves to a third party that the specified node has communicated with the destination node in the past. To this end, relaying nodes working with the proposed method record the routing packet generated by the destination in the past along with the routing table. In addition, each relaying node signs the relaying packet using a digital signature, and this packet is used to prove the route validity.  
Furthermore, to prevent the falsification of packets and attacks by malicious nodes, our proposed method allows the source and latest intermediate nodes to add their digital signatures to the relay packet. This signature scheme uses an ID-based signature that does not require a public key certificate.  
In the original AODV, a relaying packet contains hop count information, and each relay node increases this value when relaying it. However, a malicious node can falsify the  
Figure 1: Network model.  
hop count, which may be a clue to a black hole attack. Therefore, the relaying packet in the proposed method does not include any updated (without digital signatures) hop-by-hop information. Instead of the hop count, the packet includes the location of the destination obtained via GPS. Each node calculates the distance to the destination by comparing its location and GPS information.  
The proposed method must consider (i) a route request (RREQ), (ii) an RREP phase when the destination generates the RREP (i.e., routing over end-to-end), and (iii) an RREP phase when an intermediate node generates the RREP. We describe each protocol in detail below.