esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Changes in Land Use and Land Cover in the Miranda River Watershed  
Over the analyzed period, the areas of Native Vegetation decreased from 1,695,580.20 hectares to 1,558,072.61 hectares, representing a decrease of approximately 8.11% over 14 years (Figure 3). This trend of reduction in native vegetation has been observed over time, as noted by [PERSON] [23] when analyzing changes within the Miranda River Watershed regarding deforestation rates between 1973 and 2006. According to the author, in 2006, areas with natural coverage occupied 35.8% of the basin area, while in 1973, this percentage represented 72%.  
According to [PERSON] [24], these changes in natural coverage in the Pantanal basin can be explained by the issuance of State Decree N\({}^{\circ}\). 14,273/2015, which regulated ecologically sustainable exploitation and alternative land use in the Restricted Use Areas of the Pantanal floodplain in Mato Grosso do Sul. The author explains that the issuance of the decree may have stimulated the conversion of native vegetation for anthropogenic use. Additionally, population growth, followed by increases in economic indicators, may be factors that led to this reduction in natural vegetation. The areas under agriculture increased from 373,430.33 hectares to 620,952.67 hectares between 2005 and 2018, representing an increase of 66.28%. There is also a reduction in pasture areas, from 1,950,383.39 hectares to 1,760,624.88 hectares, a decrease of 9.73% over the period. The reduction in pastures and the increase in agricultural areas is a trend observed in the national scenario as surveyed by [PERSON] et al. [25]. The authors explain that the intensification of Brazilian livestock production, coupled with the ease of converting pastures to agricultural crops, explains the reduction in the total pasture area. According to [PERSON] [26], many degraded pasture areas have been converted to agricultural cultivation. There is also an increase in urbanized  
Figure 3: Land use and land cover data in the Miranda River watershed ranging from 2005 to 2018 (IA: Influence Area).  
areas and non-vegetated areas in the basin. Variability in wetland areas and water bodies was also observed, common in the Pantanal region, with periods of drought and rainfall causing fluctuations throughout the year, associated with the mechanisms of droughts and floods.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
###### Abstract  
Future wireless networks are expected to be dense and employ a higher frequency spectrum such as millimeter wave (mmwave) to support higher data rates. In a dense urban environment, the presence of obstructions causes the transmissions between the user equipment and base stations to transit from line-of-sight (LOS) to non-LOS (NLOS). This transit hence emphasizes the significance of NLOS links for reliable mmwave communication. The work presented in this paper investigates the downlink performance of a mmwave cellular system by modeling the NLOS channel using stretched exponential path loss model (SEPLM) and employing a 3 GPP distance-dependent LOS probability function. This path loss model has the inherent ability to define short ranges as well as obstructions in the environment as a function of its parameter resulting in a more realistic performance analysis. The path loss model is first validated for NLOS link using a data set from an open-source mmwave channel simulator. Then, a mathematical model incorporating LOS and NLOS transmissions is developed to study the impact of path loss on signal-to-interference-plus-noise (SINR) coverage probability and area spectral efficiency (ASE). The proposed framework can provide coverage performance indication over various blockage environments. Our results demonstrate that SINR coverage probability decreases exponentially with increasing base station density. Moreover, ASE initially increases with increasing BS density and is maximized for a particular density value, after which it converges to zero for higher densities. The results are also benchmarked with the existing path loss model of mmwave cellular system with different exponents for LOS and NLOS paths. It was observed that as the base station density increases, the SINR degrades more rapidly when using SEPLM as compared to the existing mmwave path loss model.  
coverage probability; area spectral efficiency; stretched exponential path loss model; millimeter wave +
Footnote †: journal: _electronics_
## 3 Results
### Berry Skin Phenylpropanoids  
The irrigation regime affect both anthocyanin and flavonoid derivatives and enzymatic activity. In detail, malvidin, total anthocyanin content and methoxylated to nonmethoxylated anthocyanin ratio were +43%, +35%, and +16% higher in WS than in WW grapes. Among flavonoids in berry skin, kaempfer derivatives were significantly higher (+93%) in WS compared to WW vines. The activity of F3\({}^{\prime}\)5\({}^{\prime}\)OH, 3\({}^{\prime}\)-OMT, and 5\({}^{\prime}\)-OMT were similarly enhanced (+24%, +23%, and +19%, respectively) by water stress regime irrespective of _A. nodosum_ treatments (Supplementary Table S2). The anthocyanins detected in berry skin extracts were delphinidin-3-O-glucose, cyanidin -3-O-glucose, petunidin-3-O-glucose, peonidin-3-O-glucose, and malvidin-3-O-glucose (Supplementary Figure S1A). At t0, there were no significant differences in the anthocyanins content between _A. nodosum_ treated and untreated plants in irrigation regimes (Figure 3). On the contrary, at t1 and t2, both in WW and WS vines, delphinidin-3-O-, petunidin-3-O-, cyanidin-3-O-, and peonidin-3-O-glucose contents were significantly more abundant in SWE than CTRL berry skins (Figure 3A,B,D,E). Moreover, at t1, berry skin contents of malvidin and total anthocyanin were higher in SWE than in CTRL plants (Figure 3F), irrespective of the irrigation regime. The opposite trend was observed at t2, when malvidin and total anthocyanin did not significantly differ in treated and untreated berry skins, in both water regimes. In WW vines, the ratio of methoxylated to nonmethoxylated anthocyanins and the percentage of methoxylated anthocyanins/tot were both higher in CTRL than in SWE at t1 and the amplitude of these ratios was larger at t2 (Figure 4A,B). In WS vines, the methoxylated to nonmethoxylated anthocyanins ratio was higher in CTRL respect to SWE both at t1 and t2 (Figure 4A), whereas the percentage of methoxylated anthocyanins was unaffected by _A. nodosum_ treatments in WS vines (Figure 4B). At both sampling times and irrigation regimes, SWE berry skins presented a relevant increment in hydroxycinnamic acids (Figure 5A) and total phenolics (Figure 5D) content compared to CTRL counterparts. The content of quercetin derivatives was more than two-fold higher in treated respect to untreated berry skins in both WW and WS vines at t2. This difference was found also at t1, but only in WS vines (Figure 5B). The opposite was observed for kaempfer derivatives (Figure 5C), that did not show significant changes at every sampling time and were marginally affected by the _A. nodosum_ treatments only in WS vines, with higher values in SWE than in CTRL at t2. The enzymatic activities of F3\({}^{\prime}\)5\({}^{\prime}\)OH, 3\({}^{\prime}\)-OMT, and 5\({}^{\prime}\)-OMT are reported in Table 3.  
Both in WW and WS vines, _A. nodosum_ treatments did not affect F3\({}^{\prime}\)5\({}^{\prime}\)OH enzymatic activity at t1, while decreasing its activity at t2, leading to lower values in SWE than in CTRL vines.
_Algorithms_**2013**, \(6\), 546-564; doi:10.3390/a6030546  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**Quantitative Trait Loci Mapping Problem: An Extinction-Based Multi-Objective Evolutionary Algorithm Approach**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\) and [PERSON] \({}^{1,4,5}\)**  
\({}^{1}\) Department of Computer Science, Utah State University, Logan, UT 84341, USA;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Animal Science, Shahid Bahonar University of Kerman, Kerman 76169-14111, Iran; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Institute for Systems Biology, Seattle, WA 98109, USA  
\({}^{5}\) Synthetic Biomanufacturing Institute, Logan, UT 84322, USA  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 28 May 2013; in revised form: 9 August 2013 / Accepted: 26 August 2013 / Published: 2 September 2013_  
**Abstract:** The Quantitative Trait Loci (QTL) mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA) method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS) regression QTL models and considers the conflicting objectives of model simplicity _versus_ model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.  
**Keywords:** QTL; quantitative trait loci mapping; multi-objective evolutionary algorithm; partial least squares; extinction-based EAs
# Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems
## 3 Proposed Scheme  
The proposed scheme consists of two mechanisms. One is to verify the integrity of an original video file by examining the codec software characteristic of the file. The other is to trace the transforming history of a non-original video file by searching the comparison result stored in the blockchain system. The former mechanism is explained in detail in Section 3.1, and the latter mechanism is explained in detail in Section 3.2.
# PSDFH: A Phase-Space-Based Depth from Hologram Extraction Method
## 3 Proposed Methodology
### Step 3: Regions of Interest First Estimate  
The goal of this step is to extract an area of the 2D scene, further called ROI (region of interest), the 2D points belonging to the scene. To do so, each point \(p=(x,y)\) belonging to the 2D scene is mapped into phase space using the mapping \(\Omega\) defined in Equation (9). The obtained phase space representation of the 2D candidate point is intersected with the binary mask estimated \(\hat{M}_{b}\) in Step 2. The relative intersection provides the candidate point score. More formally:  
\[score(p,\hat{M}_{b})=\frac{\left|\hat{\Omega}_{\hat{M}_{b}}(\mathcal{P})\cap \Omega(p)\right|}{|\Omega(p)|},\quad p=(x,z)\in\mathbb{R}^{2} \tag{15}\]  
where  
\[\hat{\Omega}_{\hat{M}_{b}}(\mathcal{P})=\{(x[n_{1}],\xi[m_{1}])\mid\mathbb{1}_ {\hat{M}_{b}[n_{1},m_{1}]=1}\}. \tag{16}\]  
The score for a candidate point \((x,z)\) defined in Equation (15) quantifies how much active frequencies that define this point in phase space are also active in the support \(\hat{M}_{b}\) of the input hologram phase space representation. For a candidate point to truly belong to the scene, all those frequencies should be active. Therefore, a thresholding step is performed in order to keep only the points \((x,z)\) with a score equal to 1, i.e., all points fulfill the condition of active frequencies, as follows:  
\[ROI(\hat{M}_{b})=\hat{\mathcal{P}}=\{(x,z)\mid\mathbb{1}_{score(p,\hat{M}_{b}) =1}\quad\forall p=(x,z)\in\mathbb{R}^{2}\}. \tag{17}\]  
A score less than 1 can also be encountered with a point that really belongs to the scene but with a degraded mask \(\hat{M}_{b}\).  
Figure 2 gives two mapping examples with the extracted ROI colored in blue. In the first case, the extracted ROI consists of perfect diamonds that encapsulate the scene points colored in red. In this specific case, the geometry of the scene can be estimated using the set of lines that connect the two extremities of each diamond along the \(X\)-axis. In the second case, the extracted ROI does not have a perfect diamond shape due to the proximity of the different scene points.  
The closer the points are to each other, the larger the extracted ROI and the bigger its depth extent. In contrast, the further apart the points are, the finer the ROI and the smaller the depth extent. Therefore, if the scene points are very close to each other and extend along the entire \(X\)-axis, then the extracted ROI will be very large with a high depth extent.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
###### Abstract  
Structural health monitoring problems are studied numerically with the time reversal method (TR). The dynamic output of the structure is applied, time reversed, as an external loading and its propagation within the deformable medium is followed backwards in time. Unknown loading sources or damages can be discovered by means of this method, focused by the reversed signal. The method is theoretically justified by the time-reversibility of the wave equation. Damage identification problems relevant to structural health monitoring for truss and frame structures are studied here. Beam structures are used for the demonstration of the concept, by means of numerical experiments. The influence of the signal-to-noise ratio (SNR) on the results was investigated, since this quantity influences the applicability of the method in real-life cases. The method is promising, in view of the increasing availability of distributed intelligent sensors and actuators.  
reciprocity; inverse problems; imaging; structural dynamics +
Footnote †: journal: Applied Mechanics and Computational Time Reversal  
1
# Development, Implementation and Evaluation of An Epidemic Communication System
## 4 Development of the Epidemic Communication System
### Device Implementation
This layer enables the accumulation of data, helps handle data corruption during transfers, and retains the data to be transmitted. However, wireless LANs do not have such accumulation functions. Therefore, in this study, the data were pseudo-accumulated in memory using Python, as shown in Figure 13. The data received from the socket function were first reconstructed into an image using Python, and then, saved in memory. Additionally, the data payload size that could be passed from the application and bundle layers to the session layer was 65,507 bytes, which was obtained by subtracting the UDP header (8 bytes) and IP header (20 bytes) from the maximum datagram length of the IP layer, which was 65,535 bytes. Sockets [41] were used to connect the transmitting and receiving stations. A socket function enables the transmission and reception of data between two endpoints, thereby allowing a network node to be treated as a node that is identified by a specific IP address and port number within the network. The receiving station enters a waiting state for data by binding to the IP address and port number, and the transmitting station can send and receive data by sending requests to the bound information. In particular, Figure 14 shows the socket function procedure of the session layer. After affiliating with the wireless LAN, a socket function is created for communication between the sending and receiving stations. The sending station reads the file to be sent and obtains the data size. The receiving station waits until it receives the data. If the flower image size is 500 KB, the sending station extracts data in chunks of the maximum datagram length of the IP layer, which is 65,507 bytes; calculates the number of transmissions; and notifies the receiving station of that value. The sending station then transmits data in chunks of 65,507 bytes. The receiving station saves the received data as a list-type variable. When saving the data in a list-type variable, the operation speed is faster than that when writing to a file, and errors in the continuously transmitted data are reduced. Finally, the transmitting station sends the remaining data, and the receiving station outputs the data saved in the variable to a file. Finally, the socket function is closed in preparation for the next connection, and the entire process concludes. It is assumed that image data transferred using these protocols will be received at the receiving station, after which the data will be transferred to a cloud server in the wired network. For example, the cloud server uses AI classification technology by analyzing image data to classify flowers that can be pollinated [13]. In this case, duplicate images can be discarded. In addition, drones with pollination capabilities have been developed to fly to the coordinates of flowers to perform pollination work [42]. Figure 13: Configured protocol stacks including the bundle layer and the socket function.
## 6 Profound Impact Potential on Decision-Making Processes
### Enhancing National Resilience with the GOES-R Series  
The Geostationary Operational Environmental Satellite-R (GOES-R) series, developed collaboratively by NOAA, NASA, and Lockheed Martin, represents a significant leap in geostationary weather monitoring and forecasting. Since the launch of GOES-R in 2016, the series has dramatically enhanced the precision, frequency, and scope of atmospheric observations, providing critical data to improve disaster preparedness, severe weather warnings, and long-term climate assessments [38].  
Notable technological advances include:  
1. Geostationary Lightning Mapper (GLM): Enables continuous real-time lightning detection and tracking.  
* Solar Ultraviolet Imager (SUVI): Facilitates monitoring of solar activity and space weather conditions.
* Compact Coronagraph (CCOR): Provides early detection of coronal mass ejections, helping protect satellites, power grids, and communication systems.  
The GOES-R series offers up to four times greater temporal and spatial resolution compared to previous generations, delivering near-instantaneous updates crucial for timely responses to severe weather events [39]. The final satellite in the series, GOES-U, was launched aboard a SpaceX Falcon Heavy rocket in June 2024 from NASA's Kennedy Space Center. GOES-U extends advanced weather and environmental monitoring capabilities across the eastern hemisphere, spanning from the west coast of Africa to New Zealand, solidifying the series' role in supporting national resilience and global environmental intelligence [40].
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 2 Materials and Methods
### Technology Selection
#### 2.2.2 Communications  
The selection of the communication protocol is critical, as it determines the size, structure, and format of the messages to be transmitted. In this case, the system is in rural environments, typically located on the periphery of urban areas, where mobile network coverage and Wi-Fi access may be unreliable or unavailable. In terms of data volume, the messages will be of a limited size, transmitted at regular intervals in text format. The system must also be cost-effective and energy-efficient, especially in contexts where access to electricity is limited. In this context, LPWAN networks are the most appropriate solution, as they offer long-range communication, low data rates, low power consumption, and low deployment costs [29]. Furthermore, LPWANs are highly scalable, allowing the integration of a large number of devices without significant degradation in performance. Specifically, LoRaWAN has been selected as the most suitable communication protocol. It is an open communications protocol managed by the Lora Alliance. This choice is based on the proven effectiveness of LoRaWAN in providing connectivity in areas where other proprietary technologies have failed, especially in rural environments [30; 31].  
LoRaWAN operates in unlicensed frequency bands of the electromagnetic spectrum, with the specific frequency range varying by geographic region; in Europe, frequencies between 863 and 870 MHz are used. The capacity of this technology is mainly determined by the relative position of the gateway and of the associated LoRaWAN nodes. Under ideal line-of-sight (LoS) conditions, communication distances can reach up to 20 km. LoRaWAN is particularly well suited for applications that require the transmission of small amounts of data at low bit rates. The cost of deploying a LoRaWAN-based solution is largely defined by two elements: the gateway(s) and the end nodes. Notably, maintenance costs are very minimal as LoRaWAN nodes can operate for several years on battery power without requiring intervention.  
The LoRaWAN gateway is an essential component in the system as it establishes the LoRaWAN wireless network, enabling communication with sensor nodes and receiving data from them. A single gateway can cover an extensive rural area, depending on environmental conditions and antenna placement. For this development, the MikroTik LoRa Gateway wAP LR8 Kit and the MikroTik 6.5 dBi LoRa Omni Antenna were selected.
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 4 Rule Weights Interpretation  
This section aims to compare the weights obtained from the optimization process, to analyze them and give a proper interpretation of the three metrics (FND, HND, and LND) and of the three scenarios used. The first part of this section will show some examples of the differences among the weights comparing FND with HND, FND with LND, and finally  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **FND** & **HND** & **LND** \\ \hline Algorithm & FND & HND & LND \\ LEACH & 764.8 & 936.5 & 1020 \\ CHEF & 1402.2 & 1767.9 & 1827.2 \\ CRT2 FLACO & 510.9 & 1713.4 & 2183.4 \\ ICUU & 1502.7 & 1984.7 & 2153.1 \\ CFC3 PSO & 1847.2 & 2198.1 & 2344.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results with the BS placed in the center of the deployment area.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **FND** & **HND** & **LND** \\ \hline LEACH & 764.1 & 931.2 & 1019.4 \\ CHEF & 1403.6 & 1775.7 & 1816.7 \\ CRT2 FLACO & 749.5 & 1707.6 & 2199.1 \\ ICUU & 1485.3 & 1966.4 & 2145 \\ CFC3 PSO & 1800.6 & 2116.8 & 2359.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results for the BS placed at x = 75 m and y = 75 m.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Algorithm** & **FND** & **HND** & **LND** \\ \hline leach & 744 & 897.1 & 1007.5 \\ chef20 & 1403 & 1769.3 & 1811.4 \\ CRT2 FLACO & 639 & 1602.2 & 2040 \\ ICUU & 1366.3 & 1885.7 & 2097.4 \\ CFC3 PSO & 1808.3 & 2064.1 & 2223.9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results with the BS placed at x = 125 m and y = 50 m.  
HND with LND. The last part will present some of the rules that changed their weight depending on the location of the BS to obtain better metric results.