esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
###### Abstract  
In underwater optical wireless communications (UOWC), scattering of the propagating light beam results in both intensity and phase variations, which limit the transmission link range and channel bandwidth, respectively. Scattering of photons while propagating through the channel is a random process, which results in the channel-dependent scattering noise. In this work, we introduce for the first time an analytical model for this noise and investigate its effect on the bit error rate performance of the UOWC system for three types of waters and a range of transmission link spans. We show that, for a short range of un-clear water or a longer range of clear water, the number of photons experiencing scattering is high, thus leading to the increased scattering noise. The results demonstrate that the FEC limit of \(3\times 10^{-3}\) and considering the scattering noise, the maximum link spans are 51.5, 20, and 4.6 m for the clear, coastal, and harbor waters, respectively.  
U
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 5 Results
### Computed Torque Proportional-Integral-Derivative Control  
For trajectory sequence B simulation, Figure 21 illustrates the comparison between desired and actual trajectories in the XYZ Cartesian plane.  
Figure 22 illustrates the graph of the actual trajectory in 3D.  
The results presented in Figures 21 and 22 confirm accurate trajectory execution by the controller, with minimal displacement along the Z coordinate axis.
# Energy Saving in Wireless Sensor Networks via LEACH-Based, Energy-Efficient Routing Protocols
## 4 Threshold LEACH with Sleep-Awake Scheduling Protocol
### Theoretical Background  
T-LEACH\({}_{\text{SAS}}\) is proposed as an enhanced extension of T-LEACH, in the sense that the incorporation of a sleep-awake mechanism provides additional energy conservation and, consequently, extends the network lifetime in WSNs.  
Actually, sleep-awake scheduling is just one among plenty of mechanisms that have been proposed in order to achieve energy conservation in WSNs. Specifically, the protocols that pursue the conservation of sensor nodes' energy in WSNs can be classified into three main categories, which are data-driven, duty-cycling, and energy-efficient routing, along with various subcategories [21, 26, 27, 28]. While data-driven approaches aim to limit the amount of sampled data, and energy-efficient routing protocols aim to reduce the amounts of energy consumption during data transmission, duty-cycling schemes pursue energy saving by managing the activity status of the communication module of the sensor nodes. Duty cycling is based on either topology control, sleep-awake scheduling, or the MAC protocol.  
Particularly, sleep-awake scheduling algorithms regulate the operational state of sensor nodes between the awake mode and the sleep mode, in order to minimize energy consumption, while maintaining the appropriate network performance. In awake mode, sensor nodes keep active of all their components in order to sense ambient conditions, process the relative data, and communicate with other network nodes or/and the base station. In the sleep state, nodes deactivate most of their components, their radios included, in order to save energy when there is not any need to either sense ambient conditionsor process data or communicate. The schedule used in order to switch between sleep and awake modes can be of various types (e.g., deterministic, randomized, event-driven etc.). Additionally, sleep-awake scheduling can be either centralized or distributed. In centralized sleep-awake scheduling, the base station performs global control of the scheduling processes for all network nodes. In this way, better scheduling performance can be achieved. However, global knowledge of the overall network is needed, and consequently, both processing and communication are associated with high complexity and overhead. On the other hand, in distributed sleep-awake scheduling, specific sensor nodes need only local information in order to regulate the operational state of themselves and their neighboring nodes between the awake mode and the sleep mode. Thus, such schemes are easier to implement. Yet, the achievement of optimal results is inevitably more difficult [54].  
Based on the above analysis, it becomes evident that T-LEACH\({}_{\text{SAS}}\) aims to combine the benefits of energy-efficient routing, along with those provided by duty cycling via a centralized sleep-awake scheme. On the other hand, it is associated with higher complexity.
## 2 Materials and Methods
### Model Architecture
#### 2.2.2 StyleGAN2-ADA  
StyleGAN2-ADA is a training method for generative adversarial networks (GANs). It aims to address the discriminator overfitting issue that arises when training GANs with limited data, which can lead to divergence in the training process and a decline in the quality of generated images. The core technique of StyleGAN2-ADA is adaptive discriminator augmentation (ADA), an advancement over Stochastic Discriminator Augmentation (SDA). SDA applies a series of augmentation transformations to all the images seen by the discriminator while ensuring these transformations do not leak into the generated images, preventing the discriminator from overfitting to the training samples. SDA's augmentation pipeline comprises 18 transformations, categorized into pixel-level operations, geometric transformations, color transformations, image space filtering, additive noise, and cropping. Each transformation is applied independently to each image with a certain probability. As shown in Figure 2, ADA dynamically adjusts the intensity of augmentations based on the degree of overfitting during training. It employs heuristic methods to quantify overfitting by measuring the difference between the training set and generated images and adjusts the augmentation probability p accordingly. If overfitting is detected, ADA increases the augmentation intensity; if overfitting is not apparent, it reduces the augmentation intensity.
## 5 Evaluation Results and Discussion
### Cost Analysis  
We perform a detailed analysis of the contribution of individual cost elements, namely, the lease of optical fibers and the cost of installed MUXs and OADMs in the network deployment scenarios considered.  
Figure 9 presents the cost of fibers, MUXs, and OADMs (represented on the charts as bars segments in different colours) assuming different traffic loads (\(\rho\)) and WDM system capacities (\(W\)) in network MESH-38. Note that the top of each bar represents the overall network cost (\(z\)). As it can be seen, scenarios Flex-D and Fix-D do not derive any costs related to OADMs since they assume the use of direct (dedicated) TPs between the remote sites and the hub, without traffic aggregation at intermediate sites. Still, the overall cost of these scenarios is higher than when using OADMs in Flex-O/Fix-O, which is due to the considerable contribution of the cost of fiber in comparison to the cost of OADMs. Moreover, scenarios Flex-D and Fix-D do not require any MUXs for \(\rho=1.0\). In this case, the remote sites request single wavelength connections and, therefore, there is no need for wavelength aggregation (using MUXs) at the ends of TPs. The cost of all four network deployment scenarios is the same only in the case of \(\rho=4.0\) and \(W=4\). Here, the  
Figure 8: Overall network cost in different scenarios (bars) and cost gains achieved with flex-O vs. reference scenarios (lines) as a function of traffic load (\(\rho\)) for different WDM capacities (\(W\)) in MESH-38.  
Figure 6: Overall network cost in different scenarios (bars) and cost gains achieved with flex-O vs. reference scenarios (lines) as a function of traffic load (\(\rho\)) for different WDM capacities (\(W\)) in MESH-9.  
Figure 7: Overall network cost in different scenarios (bars) and cost gains achieved with flex-O vs. reference scenarios (lines) as a function of traffic load (\(\rho\)) for different WDM capacities (\(W\)) in MESH-20.  
traffic (wavelength) demands at remote sites are high such that no traffic aggregation at intermediate sites is possible using the available WDM transmission resources, and only direct TPs can be established in the network.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 2 Materials and Methods
### Statistical Analyses  
The data are presented as means (M) and standard deviations (SD). The level of significance was seat at \(p<0.05\). The SPSS v20.0 software (SPSS Lead Technologies Inc., Chicago, IL, USA) was used for the statistical analyses. The data were subjected to a descriptive analysis and inferences; their normality was also verified using the Kolmogorov-Smirnov test. The total sample, i.e., female and male sailors, was divided into three groups on the basis of their performance level (ranking): high level (T1), medium level (T2), and low level (T3). In the upwind, downwind, and broad reach groups, we assessed differences on VMG, distance, and time by performance level groups (T1, T2, T3) in the total sample, females and males, using ANOVA. A Bonferroni correction was performed when statistically significant differences were detected. A nonparametric Kruskal-Wallis test was applied to establish differences in the cases that required it.
## 6 Conclusions  
We have mapped the ground surface deformation in Bolvadin using Sentinel-1 SAR images on both ascending and descending tracks within the time period of October 2014 and October 2018. As a result of multi-temporal InSAR analysis, two main products are produced: (i) Mean deformation velocity maps, (ii) Time-series of displacements for measurement points. The mean velocity maps show that the town of Bolvadin almost entirely subsides at different rates reaching 35 mm/year. The subsidence is taking place not only in Bolvadin, but also almost in the entire Afyon-Aksehir Graben.  
We observe a strong spatial correlation between subsidence and lithology, a high similarity between deformation rate and groundwater level changes, and a temporal correlation between subsidence rate and Eber Lake water surface changes. There is no specific deformation pattern observed around the Bolvadin fault that might continue westward into the city center. As a result, we conclude that the primary cause of surface deformations is the overexploitation of ground waters and hydrological changes that affect the entire basin.  
This study is the first step of the project related to the continuous monitoring of land deformation in the country scale using SAR data. The main purpose of this study is to investigate deformation phenomenon in a region-scale and extend our understanding of it to all subsidence regions across the country. Continuous monitoring of deformations throughout the country is crucial for sustainable planning and risk management. Sentinel-1 data show a high potential in the field of near real-time land deformation monitoring with wide coverage and regular data acquisition. This study suggests that the temporal and spatial behaviors of subsidence basins in Turkey can be investigated using Sentinel-1 data and PSI technique. To achieve this, all the processing steps used in this study will be updated to be capable of working on multiple processor stations and computer clusters to handle the enormous workload.  
M.I. carried out SAR data processing, data analysis, manuscript writing and revision. F.K. and Z.C. contributed to data interpretation, manuscript writing and revision. F.B.S. contributed to manuscript revision.  
This research was funded by Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency (AFAD) and TUBITAK BILGEM, project AYDES UZAL and project No. B740-100289.  
This is a part of the Ph.D. dissertation of [PERSON]. We thank the Republic of Turkey Ministry of Interior Disaster and Emergency Management Presidency (AFAD) for providing the active faults and geological settings maps of the study region. We thank the Turkish State Hydraulic Works for providing groundwater level data.  
The authors declare no conflict of interest.
## 4 Case Study and Result
### ModelE Simulation and Customized Geovisual Analytical System  
ModelE is a general circulation model (GCM) developed by NASA GISS. The model provides the ability to simulate many different Earth system parameters including interactive atmospheric chemistry, aerosols, carbon cycle and other tracers, as well as the standard atmosphere, ocean, sea ice and land surface components [11]. Relevant model experiments and results have been submitted as part of the Coupled Model Intercomparison Project (CMIP) [31]. Recently, we conducted 300 model runs with an 11-year simulation period for near-present boundary conditions using NASA and George Mason University cloud computing infrastructure in a spatial cloud computing fashion [43]. ModelE simulates more than 300 variables on a global scale. The spatial resolution is 4 degrees in latitude and 5 degrees in longitude. The selected outputs for these simulations were monthly binary data with a size of 16 MB. All data have been transferred into NetCDF files. The total volume of the NetCDF data is around 750 GB.  
The available variables ready for use in the database is listed in the control panel on the left column of the client (Figure 6).  
According to requests from scientists who use ModelE simulation, the initially-designed data analysis functions includes: (a) global and zonal mean based on month, season, year and 5-year; (b) ensemble mean and standard deviation; (c) relation between selected pairs of variables in scatter  
Figure 6: System interface: End users can use the GUI (Graphical User Interface) to select the parameters, time, and region, to be reviewed at a specific window. Multiple windows (up to six and with four shown) can be synchronized as needed for comparison.  
plot; (d) relation between input condition and output variable in scatter plot; (e) an assessment of the quality of the simulations as expressed in Taylor Diagram [28]. The interface for interacting with the server-side analysis functions are revised according to the available functions. All proposed map functions in Section 3.3 are usable for ModelE data. Users can define the input and see the output of statistical analysis through the panel on the bottom of the client.
## 6 Simulation Setup and Results
### Simulation Setup  
The purpose of this experiment was to test the performance of the proposed strategy, a dual-IMM filter, against its standard form, a dual filter. The dual-IMM algorithm was first tested on normal aging conditions using the B036 dataset and implementing a KF and SIF to estimate the battery states. Then, the algorithm was tested under faster aging conditions using the B034 dataset. The IMM part of the algorithm was only implemented during the parameter estimation process, and it was given access to two different degradation rates, hereby referred to as _slow_ and _fast_.  
The slow mode values of the dual-IMM algorithm were set to the same values as the standard dual filter, whereas the fast mode was given values that allowed for a faster degradation rate of the parameters.  
As mentioned above, the dual-filter algorithm was combined with the KF and SIF, resulting in a dual-KF and dual-SIF. When the IMM was combined with the dual strategies (dual-IMM), the resulting algorithms were the dual-KF-IMM and dual-SIF-IMM. Table 3 summarizes the initial conditions used for this experiment.
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
## 4 Surrounding Rock Control Technology for Trapezoidal Top-Cutting Roadway
### Control Effect  
In order to verify the effectiveness of the above-mentioned trapezoidal top-cutting roadway parameters and surrounding rock stability control technology, the surrounding rock deformation and roof separation in the roadway of the 21,100 panel after top-cutting were monitored. The results are shown in Figure 12.  
As shown in Figure 12a, during the process of top-cutting, the overall deformation of the roadway surface was slight. Within the range of 50 m when the panel was pushed, the sinking of the roof and the distance between the two sides increased sharply. As the panel continued to advance, the roadway surface deformation increased slowly, but the increase rate was greatly reduced; when the panel had advanced by 100 m, the roadway surface deformation basically reached the peak value and remained unchanged. The maximum roof subsidence was located on the roof of the top side, which was 120 mm; The maximum subsidence of the roof of the non-top side was only 44 mm, which was 36.7% of that on the top side. The maximum distance of the two sides in the roadway was 71 mm. It can be seen from Figure 12b that when the panel was 100 m away from the monitoring point, the roof began to produce delamination, but the amount of delamination was slight; the maximum total length delamination was only 3 mm. When the panel advanced to the  
Figure 11: Schematic diagram of key technologies for controlling surrounding rock of top-cutting roadway: (**a**) floor plan of gangue protection; (**b**) cross-sectional view of roof reinforcement support.  
monitoring point, the roof delamination increased sharply, until the panel was pushed 25 m past the monitoring point, when the amount of roof separation basically reached its peak. Among other things, the maximum separation layers in the shallow part, deep part and full length are 9 mm, 20 mm and 29 mm, respectively. Based on the actual situation on site (Figure 12c), the roof and sides of the roadway were flat and intact, and the effect of top-cutting was efficient, fully meeting the needs of on-site production. This showed that the previously-determined top-cutting parameters and the surrounding rock stability control technology were reasonable and feasible.
## 3 Numerical Simulation
### Discrete Models and Mesh Validation  
For each of the analyzed cases, a three-dimensional model of the computational domain was prepared considering the shape of the finned tube profile placed in a cylindrical volume with a diameter \(d=250\) mm and a length \(l=1000\) mm. An example of the meshed model with boundary conditions for the case c5 is shown in Figure 7, where \(v_{inlet}\) stands for wind velocity and \(p_{atm}\) is atmospheric pressure. The computational domains were discretized using irregular tetrahedrons elements.  
The mesh quality adopted for further analysis was determined using a preliminary mesh independence test based on the case c5. For the mesh independence test, a wind velocity of 10 m/s was accepted. The results of the mesh validation analysis are shown in Figure 8, where points m1-m8 correspond to the results for different mesh qualities. It  
Figure 6: Analyzed cases (**c1**–**c6**) of profiles orientation.  
Figure 7: Discretized model with boundary conditions for the case c5.  
can be seen that the results for the quality of the m6-m8 mesh are significantly similar to each other with the percentage error <0.5%. Therefore, the corresponding mesh quality was assumed for all other computational cases, resulting in approximately 100,000 finite elements.
# An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
## 4 Resolution Analysis
### AA-BiSAR with Moving Transmitter
#### 4.1.2 Ground-Range Resolution
It can be seen from Equation (40) that the ground-range resolution of the system is not only related to the position of the target and receiver, but also related to the position of the transmitter, which is different when In the following, in order to analyze the influence of the transmitter position on the ground-range resolution of the system, four sets of comparative experiments are designed. The detailed simulation parameters of AA-BiSAR with one-moving transmitter system are shown in Table 1. The transmitter velocity is set as 50 m/s, and the initial positions of the transmitter are set as (0 m, 0 m, 1000 m), (0 m, 100 m, 1000 m), (100 m, 100 m, 1000 m) and (700 m, 0 m, 1000 m), respectively. Figure 6a-d shows the ground-range resolution results of different targets with different transmitter positions. According to the simulation results, the closer the transmitter to the point large, the worse the ground-range resolution, and when the transmitter is far away from the target, the ground-range resolution is mainly determined by the distance between the point target and the transmitter position.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 3 Physical Layer Aided Authentication and Key Agreement (PL-AKA)
### Physical Layer Challenge-Response Authentication (PHY-CRAM)
#### 3.1.2 Physical Layer Response  
In the physical layer response, a secret key for authentication is securely transmitted to the BS based on the estimated channel information from the challenge signal. In detail, a secret key denoted by \(\kappa=[\kappa_{1},\kappa_{2}\cdots\kappa_{L}]\) is shifted by the estimated channel phases \(\hat{\theta_{1}}\), \(\hat{\theta_{2}}\cdots\hat{\theta_{L}}\) to prevent the intrusion device from capturing any knowledge of the secret key from the response signal. Note that the channel phases accounted in the proposed scheme provide more secure encryption, compared to the gain, due to their sensitivity and unpredictable nature to the locations of transceivers. Then, the transmitted signal in the physical layer response is represented as follows:  
\[x_{R}(t)=\sum_{l=1}^{L}\sqrt{\frac{2E_{s}}{T}}\text{cos}(2\pi f_{l}t+\phi_{l}- \hat{\theta_{l}}), \tag{3}\]  
where \(\phi_{l}=\frac{(1-x_{l})\pi}{2}\). Then, due to channel reciprocity, the received signal at the BS through the \(l\)th subcarrier is given by the following:  
\[Y_{R}(f_{l})=e^{i\hat{\theta_{l}}}|H(f_{l})|X_{R}(f_{l})+n(f_{l}), \tag{4}\]  
where \(\hat{\theta_{l}}=\theta_{l}-\hat{\theta_{l}}\) and \(n(f_{l})\) are the estimation error for the \(l\)th channel phase and noise term, respectively.