esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis and Discussion
### Asymptotic Performance Analysis for Identical Local Detection Performance Indices  
In this subsection, the asymptotic performance analysis with respect to a large number of sensors is carried out by using the CLT approximation when the local detection performance indices are equal to each other. The fusion statistic \(\Lambda_{\text{BER}}^{\prime}\) is equivalent to the statistic \(N_{1}^{\prime}\) in terms of the system detection performance. \(N_{1}^{\prime}\) follows the binomial distribution. For the CLT approximation performance evaluation, the system false alarm probability and detection probability can be written as:  
\[P_{\text{FA}}=P(\Lambda_{\text{BER}}^{\prime}\geq T_{\text{C}}\mid H_{0})=Q \left(\frac{T_{\text{C}}-NP_{0}}{\sqrt{NP_{0}(1-P_{0})}}\right) \tag{31}\]  
and:  
\[P_{\text{D}}=P(\Lambda_{\text{BER}}^{\prime}\geq T_{\text{C}}\mid H_{1})=Q \left(\frac{T_{\text{C}}-NP_{1}}{\sqrt{NP_{1}(1-P_{1})}}\right) \tag{32}\]  
where \(P_{1}\) and \(P_{0}\) are given in Equations (21) and (22). \(T_{\text{C}}\) is the fusion threshold under this case. \(Q(\cdot)\) is the complementary distribution function of the standard Gaussian. Clearly, \(NP_{0}\) and \(NP_{0}(1-P_{0})\) are the mean and variance under \(H_{0}\) hypothesis. Similarly, \(NP_{1}\) and \(NP_{1}(1-P_{1})\) are the mean and variance under \(H_{1}\) hypothesis. The CLT approximation does have a tractable performance evaluation. From Equations (31) and (32), we have:  
\[P_{\text{D}}=Q\left(\frac{Q^{-1}\left(P_{\text{FA}}\right)\sqrt{P_{0}(1-P_{0} )}-\sqrt{N}(P_{1}-P_{0})}{\sqrt{P_{1}(1-P_{1})}}\right)\]  
According to the N-P criterion, it is required to maximize the system detection probability at the FC while the system false alarm probability is below a given value \(\alpha\). When:  
\[P_{1}-P_{0}=(1-2P_{e})(P_{d}-P_{fa})>0\]_i.e._, \(P_{1}>P_{0}\), under identical system alarm probability \(\alpha=P_{\text{FA}}\), the system detection probability \(P_{\text{D}}\) can be enhanced when the number of sensor \(N\) increases due to the monotonic decreasing characteristic of function \(Q(\cdot)\).
# WUE and CO2 Estimations by Eddy Covariance and Remote Sensing in Different Tropical Biomes
## 2 Materials and Methods
### Description of Study Area
#### 2.2.3. Pantanal Site  
The study was conducted at the Brazilian Northern Pantanal Wetland (BR-Npw) flux tower (Figure 1) located approximately 35 km SE of Pocone, Mato Grosso, Brazil (16\({}^{\circ}\)29\({}^{\circ}\)53.71\({}^{\circ}\)S: 56\({}^{\circ}\)24\({}^{\prime}\)45.91\({}^{\prime}\)W; 120 m altitude). The site is part of a research station managed by the Federal University of Mato Grosso (UFMT) within a national reserve managed by the Brazilian Social Service of Commerce (SESC Pantanal) [28,29]. According to Koppen classification, the regional climate is Aw, which is defined as a hot and wet climate with rainfall in the summer and dry in the winter [30]. The accumulated precipitation varies from 800 to 1500 mm/year [31,32]. The air temperature ranges between 29 and 32 \({}^{\circ}\)C (maximum) and between 17 and 20 \({}^{\circ}\)C (minimum) [33,34]. The soil type in the region is classified as Dystric Gleysol [35] with an average concentration of 429 g/kg of sand, 254 g/kg of silt, and 317 g/kg of clay, mean soil organic matter (SOM) (0-0.10 m depth) of 17 g/kg and a soil pH of 4.7 [36]. The vegetation of the site is typical of \"scrub\" forests in  
Figure 1: Location of the study sites with the native extension of each biome.  
the region, with a mean leaf area of 7.4 m\({}^{2}\) m\({}^{-2}\) and a mean height of 6 m [37], dominated by _Combretum lancolatum_, Phol (Combretaceae), a common species found on the riverbanks of the Pantanal region [38]. Vascular or semi-aquatic plants such as _Thalia geniculata_ and _Nymphaea_ sp. occur in more open areas [39]. These are dense forests that are referred to as \"hyperseasonal\" because they are subjected to both seasonal flooding and drought [40].  
Our data were collected from 1 January 2015 to 31 December 2016. Micrometeorological variables were measured 20 m aboveground, close to the eddy covariance sensors. Air temperature (_Ta_, \"C) and relative humidity (_RH_, %) were measured using a thermohygrometer (HMP45 AC, Vaisala Inc., Woburn, MA, USA). Precipitation (_Ppt_, mm) was measured 2 m above the ground using a micrometeorological station (WXT520, Vaisala Inc., Helsinki, Finland) installed in an open area to avoid interception by the tower or tree canopy. The flood stage was determined by measuring water levels (_WL_) above the ground at the study site. These inundation levels (\(\pm 1\%\)) were measured along with water temperature (\(\pm 0.3\)\({}^{\circ}\)C) using a CTD-10 (Decagon Devices Inc., Pullman, WA, USA, \(\pm\) 0.05% full scale at 20 \({}^{\circ}\)C) in 2015 and 2016. Due to instrument malfunction in 2014, the data for this year are not available. The start of each flood cycle began with the first reading of standing water at the site and ended when sensors indicated the absence of standing water. These flood cycles were then compared to the stage of the Cuiaba River collected by the RPPN-ESSC Pantanal park rangers (pers. comm.) approximately 1 km away.
# A Clutter Suppression Method Based on LSTM Network for Ground Penetrating Radar
## 3 Two Layers LSTM Network for Wavelet Prediction
### Network Training  
As shown in Figure 4a, three-layer infinite medium is set up to simulate air, cement, and limestone from top to bottom. The relative permittivity of cement and limestone are \(\varepsilon_{r2}=6\) and \(\varepsilon_{r3}=9\), respectively. The cement layer thickness is \(d_{1}=0.4\) m. The top air layer and the bottom limestone layer are both infinite. A PEC cube object with a 0.2 m edge length is partially buried between the cement layer and the limestone layer. The depth from the top of the cube to the ground is \(d=0.3\) m. The distance between the transmitting and receiving antennas is \(L=0.4\) m, and the height above the ground is \(h=0.4\) m. The two antennas are vertically located on both sides of the survey line and move synchronously along the survey line of the red arrow. The signal source is a ricker wave with center frequency of 200 MHz, time sampling interval of 0.0385 ns. The spatial sampling interval along the survey line is 0.04 m. A 3D-FDTD simulation tool [19] is used to generate the A-scan data. In total, 95 channels of A-Scan echoes, each with 780 samples, are accumulated along survey line to form the B-Scan image, as shown in Figure 4b. In order to eliminate the signal similarity of dense sampling along the survey line, we selected 30 channels of data at equal intervals as the data set. From these data, 18 channels of A-scan echoes are randomly selected for the training set, 6 channels are for the validation set, and another 6 channels are for the testing set.  
Figure 3: The structure of two-layer LSTM model network.  
The 18 channels of A-scan echoes of the training set are randomly connected end-to end to construct a longer sequence with 780 \(\times\) 18 = 14,040 samples as the input sequence. In each step of network training, the input length is 100, which means the first 100 samples of the input sequence are used to predict the next sample. The time window of the input data moves backward step by step. The network uses the mean square error (MSE) as the loss function.  
Figure 5 shows the loss during the training process. The loss of the one-layer network decreases slowly, and the final loss is much greater than the loss of the two-layer network. The loss of the three-layer network drops quickly, but it appears a slight over-fitting has resulted in poor prediction results. The two-layer network converges after the 180 steps, with the loss less than 0.001. Figure 6 compares the predicted wavelet. It can be seen that the red line predicted by the two-layer network is most similar to the ideal wavelet, which is the echo when there is no target buried in the ground. So, the two-layer LSTM network is superior in training time and performance and will be used for wavelet prediction in the following.  
Figure 4: The original simulation model and the corresponding B-scan image. (**a**) Schematic diagram of GPR measurement. (**b**) Time accumulation B-scan image.  
Figure 5: Loss function of network training process.
Microorganisms_**2015**, \(3\), 746-758; doi:10.3390/microorganisms3040746  
_microorganisms_ ISSN 2076-2607  
www.mdpi.com/journal/microorganisms  
_Article_  
Carbon-Starvation Induces Cross-Resistance to Thermal, Acid, and Oxidative Stress in _Serratia marcescens_  
**[PERSON] \({}^{\dagger}\), [PERSON] \({}^{\ddagger}\) and [PERSON] * Department of Biology, University of West Georgia, Carrollton, GA 30118, USA; E-Mails: [EMAIL_ADDRESS] (J.R.P.); [EMAIL_ADDRESS] (L.C.K.)**  
\({}^{\dagger}\) Present Address: Department of Biology, East Georgia State College, Swainsboro, GA 30401, USA  
\({}^{\ddagger}\) Present Address: Georgia Department of Agriculture, Atlanta, GA 30334, USA.  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-678-839-4033; Fax: +1-678-839-6548.  
Academic Editor: [PERSON]  
_Received: 14 August 2015 / Accepted: 21 October 2015 / Published: 26 October 2015_  
**Abstract:** The broad host-range pathogen _Serratia marcescens_ survives in diverse host and non-host environments, often enduring conditions in which the concentration of essential nutrients is growth-limiting. In such environments, carbon and energy source starvation (carbon-starvation) is one of the most common forms of stress encountered by _S. marcescens_. Related members of the family _Enterobacteriaceae_ are known to undergo substantial changes in gene expression and physiology in response to the specific stress of carbon-starvation, enabling non-spore-forming cells to survive periods of prolonged starvation and exposure to other forms of stress (_i.e._, starvation-induced cross-resistance). To determine if carbon-starvation also results in elevated levels of cross-resistance in _S. marcescens_, both log-phase and carbon-starved cultures, depleted of glucose before the onset of high cell-density stationary-phase, were grown in minimal media at either 30 \({}^{\circ}\)C or 37 \({}^{\circ}\)C and were then challenged for resistance to high temperature (50 \({}^{\circ}\)C), low pH (pH 2.8), and oxidative stress (15 mM H\({}_{2}\)O\({}_{2}\)). In general, carbon-starved cells exhibited a higher level of resistance to thermal stress, acid stress, and oxidative stress compared to log-phase cells. The extent of carbon-starvation-induced cross-resistance was dependent on incubation temperature and on the particular strain of _S. marcescens_. In addition, strain- and temperature-dependent variations in long-term starvation survival were also observed. The enhanced stress-resistance of starved _S. marcescens_ cells could be an important factor in their survival and persistence in many non-host environments and within certain host microenvironments where the availability of carbon sources is suboptimal for growth.  
**Keywords:** carbon-starvation; stress response; stress resistance; starvation survival; strain variation; opportunistic pathogen
# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
## 4 Conclusions  
This work proposes the use of the GPR ML technique for monitoring the performance of optical wireless communication systems. The model demonstrated high accuracy in predicting the system's impairment parameters, except when high ASE noise was introduced, where it struggled to predict low values of the \(C_{n}^{2}\) parameter. Comparisons with RF and SVM models showed that the proposed GPR model achieved equal or better performance in terms of R-squared and RMSE metrics in all channel conditions. The amount of improvement depends on the type of the involved impairments in the system and their severity. The proposed model can be utilized by telecommunication network operators who use FSO links in their networks to accurately predict the level of impairment in their optical signals. Based on the model output, operators can determine the best course of action to mitigate channel impairments. This may involve implementing adaptive modulation formats, adjusting the baud rate, or applying digital signal processing techniques to the transmitter, receiver, or both. Future work could extend the model to predict other system impairments, such as DGD in fiber channels and pointing errors in outdoor channels. In addition, techniques such as quantum and graph neural networks could be investigated to improve the performance of predicting the channel impairments.  
This research was funded by Prince Sultan University and the APC was funded by Prince Sultan University.  
Not applicable.  
The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APCs) for this publication.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AAH & asynchronous amplitude histogram \\ ADTS & asynchronous delay-tap sampling \\ ANN & artificial neural network \\ ASE & amplified spontaneous emission \\ CD & chromatic dispersion \\ CNN & conventional neural network \\ DD & direct detection \\ DGD & differential group delay \\ DNN & deep neural network \\ EDFA & erbium-doped fiber amplifier \\ FSO & free space optic \\ GP & Gaussian process \\ \end{tabular}
# Fronthaul Design for Wireless Networks  
[PERSON]  
1 Instituto de Telecomunicacoes, IST, University of Lisbon, 1049-001 Lisbon, Portugal; 1 [EMAIL_ADDRESS] (N.S.); [EMAIL_ADDRESS] (M.P.Q.); [EMAIL_ADDRESS] (A.R.)1  
[PERSON]  
1 Instituto de Telecomunicacoes, IST, University of Lisbon, 1049-001 Lisbon, Portugal; 1 [EMAIL_ADDRESS] (N.S.); [EMAIL_ADDRESS] (M.  
Nevertheless, the costs of fronthauling the mobile cells--i.e., the expenses to establish connections between the multiple distributed Remote Radio Heads (RRHs) and the centralized Baseband Units (BBUs)--still pose an important challenge.  
This paper considers the C-RAN concept and aims at finding out, for a wide range of scenarios, which underlying fronthaul technologies are cost-effective; the key technologies examined are Microwave Radio Transmission (MRT), Free Space Optics (FSO), and Fiber Optics (FO). The following contributions are made by this paper: (1) a cost-effectiveness comparison of the aforementioned communication technologies is performed for individual fronthaul segments under different weather conditions, lengths of the links, and bit rate requirements; (2) an assessment is also performed regarding the impact of the RRH density on the selection of cost-effective communication technologies for C-RANs. These studies were made possible by the development of a methodology that enables determining the most cost-effective solution for each RRH-BBU link, as well as to compute the required number of BBUs and where they should be positioned in order to minimize the overall network costs. In a nutshell, the provided methodology along with the findings of this work aim at serving as useful guidelines when designing fronthaul networks.  
This paper is organized as follows. After the introduction, Section 2 provides a background on the topics of interest for this work, namely C-RAN and fronthaul communication technologies, as well as a review of related work. Section 3 describes the developed methodology to evaluate and compare the performance of MRT, FSO, and FO technologies when applied to the fronthaul. Section 4 provides and discusses the results of the studies performed herein, namely an evaluation of which technology (MRT, FSO, or FO) is the most cost-effective choice for an individual RRH-BBU link regarding the conditions of its implementation, as well as a cost-effectiveness analysis of communication technologies for C-RANs with different RRH densities. Finally, Section 5 concludes the paper.
# Method for Measuring Surface Charge on Insulating Materials Based on the Vibrating Capacitor Method
## 2 Methods and Simulation
### Boundary Conditions and Material Parameters  
In the configuration of the physical field, steady-state calculations are performed using Comsol Multiphysics with the AC/DC electrostatic interface. For the two-dimensional axisymmetric model, the boundary conditions are set as follows: charge conservation throughout the entire air region. Charge conservation within the tested dielectric material. The lower surface of the tested dielectric material is grounded. The upper surface of the tested dielectric material is divided into two regions: one region with uniform surface charge density, and the other region with zero charge density. The probe is set as a terminal, allowing for the application of voltage. In the three-dimensional model, the boundary conditions are set as follows: charge conservation throughout the entire air region. Charge conservation within the tested dielectric material. The lower surface of the tested dielectric material is grounded. The upper surface of the tested dielectric material is divided into a ring-shaped region, uniformly subdivided into 12 equal parts, as shown in Figure 1b. Applying different charge densities to these regions can simulate uniform charge distribution similar to the ring-shaped region, point charge distribution, and sinusoidal characteristic charge distribution. The remaining regions are set as zero-charge regions. The probe is set as a terminal, allowing for the application of voltage.  
The material parameters remain consistent across both models. Copper has been selected as the material for the probe. Its linear resistivity is specified as 1.72 \(\times\) 10\({}^{-8}\)\(\Omega\)-m, while conventionally, its relative permittivity is set to 1. However, for metallic materials, the relative permittivity tends towards infinity. Consequently, in the software, this value is adjusted to 10\({}^{10}\). The dielectric material subjected to testing is polytetrafluoroethylene (PTFE), characterized by a relative permittivity of 2. The remaining constituents are represented by air, with a relative permittivity of 1.
# _Apis mellifera_ Bee Verification with IoT and Graph Neural Network
## 6 Materials and Methods
### Materials Used
#### 6.1.1 Apiary System  
The apiary system consists of test apiaries where the tests of the research are achieved, and a suitable beehive with the environmental requirements to be used with live honeybees; each beehive is identified by the registry provided by the Government Office of Agriculture.  
The test apiaries are the spaces where a set of hives is located; from this set of hives, one or two hives were selected for the tests. Apiaries were selected with two main characteristics: first, they were located at distances that did not affect the transit of people, and second, there should not be any domestic animals near the apiaries.  
The suitable beehive contains a set of special devices that allow for data and image acquisition; these devices are placed on the top of and the entrance to the beehive. The following paragraph lists the devices, functionality, and technical aspects.  
1. Two solar panels placed on the top of the hive, built crosswise for the acquisition of solar energy which feeds the Arduino devices, Raspberry Pi (Sony UK Technology Centre, Wales, UK), video camera and humidity, and temperature sensors. Technical information: Smart Projects, Ivrea, Italy 50 W, 12 Vdc, Polycrystalline, 36 Grade A Cells connected to a regular and an inverter; a deep-cycle battery acts as an energy-charging center (Epcom power line solar module-5012 of Changzhou City).
2. A Raspberry Pi board, for sending and receiving information between the apiary and the DPC. Technical information: Raspberry Pi 4 Computer Model B Version 8 GB RAM (Sony UK Technology Centre, Wales, UK).
3. A video camera for the acquisition of images in the hive entrance. The video camera is located at the top of the beehive entrance, with a direct focus towards the access, which allows the acquisition of images of individual bees and not in groups. (Technical information: Raspberry Pi High-Quality Camera 12.3 MP 7.9 mm diagonal image size.)
4. An Arduino UNO to connect the humidity and temperature sensors of the hive. (Technical information: Arduino UNO microcontroller board. Technical information: Smart Projects, Ivrea, Italy, based on the ATmega328P. Humidity sensor DHT11. Temperature sensor LM35.)
5. A wireless antenna to communicate the hive with the communication antenna router.  
The general scheme of the suitable beehive is shown in Figures 1 and 2. They show the real suitable beehive in operation inside the apiary. The system works 12 h a day to transmit information to the DPC. For the tests, 10 bee racks were installed inside the beehive with approximately 3000 bees, according to the beekeeper's estimates.
# Uncertainty Quantification for Infrasound Propagation in the Atmospheric Environment
## 4 Quantitative Results Presentation and Analysis Based on the Actual Atmospheric Data
### Uncertainty Quantification Analysis for the Infrasound Propagation Distance
It can be seen that the atmospheric environmental parameter with the greatest influence on the infrasound propagation distance is the meridional wind from these two sensitivity indices. In the non-uniform atmospheric environment, the infrasound propagation distance \(R\) is mainly related to the infrasound propagation velocity, i.e., the effective sound velocity. From the effective sound velocity model in Equation (1), it can be seen that the zonal wind and the meridional wind are the main parameters affecting the effective sound velocity. The atmospheric temperature is weakened by taking the root sign, which leads to the effect on the effective sound velocity. The wind speed in the latitudinal direction after sampling is (\(-\)27.64 m/s, 53.81 m/s), i.e., the interval length is 81.45 m/s. The wind speed in the meridional direction after sampling is (\(-\)26.80 m/s, 60.05 m/s), i.e., the interval length is 86.85 m/s. Therefore, it can be inferred that the influence of the meridional wind on the infrasound propagation distance \(R\) is greater than that of the zonal wind. This conclusion needs to combine the data of zonal and meridional winds in a specific area and time, and it is not generalizable.
## 5 Experimental Results
### Performance Comparison between the Proposed Method and Baseline Approaches
#### 5.1.1 Maximum Embedding Capacity  
We compared the embedding capacity and the image quality when the cover image was embedded once from beginning to end. The comparison is between the proposed method and the methods of [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [21], and [PERSON] et al. [5]. Shown in Table 9 is the comparison of maximum embedding capacity for six test images between the proposed method and the other schemes. In addition, the Table 10 is the comparison of PSNR for maximum embedding capacity between the proposed method and the other schemes.  
From the results in Table 9, whether in a smooth image (like image Lena) or in a complex image (like image Baboon), the proposed method has a better embedding capacity.  
According to Table 10, the average PSNR of the stego-image among the previous schemes [5; 21; 46; 47] and the proposed method are 53.04 dB, 51.75 dB, 51.61 dB, 63.72 dB, and 48.55 dB, respectively. Clearly, the larger the embedding capacity is, the lower the quality of the image we get. Although the PSNR of the proposed method is lower than other methods, the embedding capacity of it is much more than other methods. According to the above results, when the cover image is only embedded once, our proposed method can have the maximum embedding capacity and maintain good image quality.