esa-sceva/satcom-chunk-collection huggingface dataset:
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 5 Technologies for Autonomous Water Basin Mapping and Monitoring
### Issues Related to Robotic Systems
#### 5.2.2 Data Transmission  
While tethered solutions generally do not face significant challenges with data transmission, wireless and real-time monitoring in aquatic environments can encounter several obstacles. Surface robots that rely on radio and satellite communication require a clear line of sight and can be adversely affected by weather conditions. In contrast, underwater communication primarily relies on acoustic modems, which offer limited bandwidth (typically ~50 kbps) and range (typically 300 m-1 km) [130]. Acoustic communication can be disrupted by various water conditions, such as temperature gradients, salinity changes, and background noise from marine life or human activities. Such disruptions can result in data loss or delays, impacting the real-time monitoring capabilities of wireless underwater robots (e.g. AUVs) or sensors, potentially leading to missed defect detection of the FPV or an inaccurate real-time assessment of their environmental impact.  
Various alternative communication channels have been investigated [131], both between drones and/or drone-to-surface buoy/infrastructure, from radio frequency (10-40 m) to optical blue/green (10-120 m) [132; 130]. Underwater acoustic/optical modems, when used in a mesh network (Figure 4), can facilitate data transmission between submerged sensors, surface buoys/infrastructures, and underwater drones. These systems are equipped with dedicated and georeferenced communication systems to relay data to the cloud. On the other hand, choosing to fully automatize the submerged structure with underwater drones would mean using quite expensive localization systems. Video streaming involves even greater costs to achieve high wide communication bands to bring back a strong signal to the surface for heavy remote monitoring purposes. Hence, wireless and real-time underwater solutions are not ideal for FPV monitoring.
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 3 A Novel Adaptive Sparrow Search Algorithm
### The Model of the CWTSSA  
A novel adaptive sparrow search algorithm based on chaotic mapping and t-distribution mutation, namely the CWTSSA, is proposed. Its features enable the CWTSSA to avoid falling into the local optimum, and also improve the convergence speed and accuracy. The flow of the CWTSSA is shown in Figure 2.  
The specific steps of the CWTSSA are described in detail as follows.  
Step 1. Initialize parameters of the CWTSSA, including population size, maximum number of iterations, proportion of discoverers, proportion of scouts, and the sparrow population using sine mapping.  
Step 2. Calculate the fitness value of each sparrow to find the current optimal fitness value and the worst fitness value, and their corresponding positions.  
Step 3. From the sparrows with better fitness values, some sparrows are selected as discoverers, and the position is updated according to the weight update strategy.  
Step 4. The remaining sparrows are used as joiners, and their positions are updated.  
Step 5. Randomly select some sparrows as guards and update their positions.  
Step 6. If rand \(<\) p, adaptive t-distribution mutation is performed according to formula (11), the current optimal value is disturbed, and a new solution is generated.  
Step 7. Calculate fitness value and update position.  
Step 8. Determine whether the conditions are met, and output the results if they are met. Otherwise repeat the Step 2 until the end condition is met.  
Figure 1: Probability density curves of Cauchy, Gaussian and t-distribution.
###### Abstract  
To satisfy the demands of wireless communication systems for tunable shielding materials, in this work, a graphene/ion gel/graphene sandwich structure is proposed, based on graphene and a solid ionic material ion gel. After modelling, preparing and testing, it was found that the structure could achieve more than 10 dB shielding effectiveness tuning in GHz and THz bands. Meanwhile, the adjusting speed of the structure was also studied, displaying effective dynamic tuning in the second order. Furthermore, the fabricated samples have the advantages of a low profile, easily conformable, and convenient processing, which are of great potential in emerging electronic devices.  
shielding effectiveness; tunable; graphene; ion gel; multiband +
Footnote †: journal: Applied sciences
## 4 Experiment and Result Analysis
### Training Environment and Parameter Configuration  
All the experiments were performed on a server using an Intel Xeon CPU E5-2680 [EMAIL_ADDRESS] GHz, 256 GB DDR4 memory, an NVIDIA TITAN RTX graphics processing unit (GPU) with 24 GB memory, and an Ubuntu operating system. All experiments were performed using Python 3.9 and NVIDIA CUDA-12.0, and the compiler environment was developed using the PyTorch 2.0.1 deep learning framework.  
The setting of parameters in the model-training process has a great influence on the performance of a model. Appropriate parameter settings can accelerate the convergence speed of a model and improve the accuracy of classification. The parameter settings in the training process are shown in Table 4. We set the epoch to 100 and the batch_size to 32. We used the cosine annealing method to dynamically adjust the learning rate, and we used the stochastic gradient descent method (SGD) as an optimizer. In order to prevent a local gradient extremum in the process of training, momentum was introduced to execute gradient correction.  
In the model-training stage, the image size is first adjusted to 224 \(\times\) 224 and converted into a feature tensor. The data set is divided into a training set and a verification set  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline Optimizer & SGD \\ Scheduler & Cosine annealing \\ Learning rate & 0.001 \\ Batch size & 32 \\ Epoch & 100 \\ Momentum & 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training parameter settings.  
according to a ratio of 8:2, and the divided data are input into the model in batches for training after disrupting the order.
# Sensor Technologies for Transmission and Distribution Systems: A Review of the Latest Developments
## 6 Conclusions  
The latest developments in sensing technologies in transmission and distribution systems have been reviewed in this paper. The newest literature pertaining to modern-day sensors and their requirement on major power system components has been studied, compared, and reported in the paper. Furthermore, the paper presents a timeline for the development of sensor technology in transmission and distribution systems. The paper also gives the utility grid and consumer grid's point of view on sensor technology. Finally, the paper charts out the beneficial impacts of sensor technology on power systems, its future impacts, as well as some of its key issues as topics of interest for future researchers. It can be concluded from the above review that sensing technologies are fast becoming an important component for the control and monitoring of the power grid. The main goal of modern sensing technology has evolved from the collection of grid parameter values for human interpretation to the complete transformation of the sensed data to provide critical real time information, thereby enhancing the other key areas of the power grid, such as accurate fault detection and localization, condition monitoring, outage management, fault forecasting, and predictive maintenance. These advances help in the development of traditional electrical grid by enabling the penetration of distributed and renewable energy sources, the addition of power electronic devices, and nonlinear loads and its transformation into smart grids in the future. Hence, it is of utmost importance for the government and power companies to promote the research and development of sensing technologies as well as to facilitate their deployment to boost the overall asset utilization of the modern power grid.  
Conceptualization, A.S. and P.W.P.; writing--original draft preparation, A.S.; writing--review and editing, E.A., A.M. and P.W.P.; visualization, A.S. and P.W.P.; supervision, P.W.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that they have no conflicts of interest.
## 9 Results and Discussion
### Power Consumption Based on the Sleep/Wake Scheme  
Figure 11 shows the current consumption of each component of the sensor node with and without the sleep/wake scheme. The current consumption of XBee S2C is the highest among all the components of the sensor node because RF components frequently transmit data with maximum output power to ensure data delivery.  
In the current work, +5 dBm (3.1 mW) is adopted to ensure communication between WSN nodes in the farm field. Figure 12 shows the power consumption of the router and main router nodes. The current consumption of the router nodes is considerably improved by the sleep/wake scheme. The power consumed by the router nodes is higher than that consumed by the sensor node (Table 4). This result is attributed to the following conditions:  
1. The router node collects climate condition data from four sensor nodes within 16 s, whereas the sensor node measures data using two sensors within 2 s.
2. The payload of the router node is higher than those of the sensor nodes, as indicated in Figure 4b,c.
3. The DC of the router node is larger than that of the sensor node, as shown in Figure 6.  
Figure 13 illustrates that when the sleep/wake scheme is applied, the power savings of the sensor node are considerably increased to 96% relative to that in traditional operation, which consumes 19.35 mA. Power saving is computed using Equation (17) [57]:  
\[\textit{Power savings}=\left(1-\frac{current\textit{ consumption}_{sleep/wake\textit{ scheme}}}{current\textit{ consumption}_{traditional\textit{ operation}}}\right)\ \times\ 100\% \tag{17}\]  
Figure 11: Current consumption of each sensor node component with and without the sleep/wake scheme.  
Figure 12: Current consumption of the components of the router and main router nodes with and without the sleep/wake scheme.  
In addition, power savings are improved to 90%, 99%, 98%, and 95% for soil moisture, DHT11, Atmega 328p, and XBee S2C relative to their values in traditional operation, respectively. Power savings are increased to 94% and 89% for the router and main router nodes, respectively (Figure 13), relative to traditional operation (i.e., without the sleep/wake scheme), which consumes 17.4 mA for the router and main router nodes. Power savings are computed using Equation (17). Figure 13 shows that the power savings of the sensor node are better than those of the router and main router nodes for the same reasons mentioned previously. The power saving of the router node is 96.7% and 93% for Atmega 328p and XBee S2C relative to traditional operation, respectively. The power saving of the main router is 91.5% and 88% for Atmega 328p and XBee S2C relative to traditional operation, respectively.
## 5 Experimental Results
### A Flexible Data Generator
Figure 10d shows that some of these RBSs are located such that they can deceive UEs and compete against legitimate BSs for the handover protocol. When the received signal at the platoon is more than 5 dB higher than the BS, the UE switches from BS to RBS, and the rogue can cause sudden turns resulting in dangerous road accidents. To date, an attempt has been made to design a software platform that mimics the real signals from legitimate BS and rogue BS so that they can be used for our generated dataset to test its effectiveness in various scenarios. The platform enables the user to control anything that might be variable in terms of the location of each BS, the proximity to the road, the separation between BS units and, the BS power output, and the velocity of the platoon leader to create a realistic dataset. The velocity varies during the simulation and is implemented based on the Markov model and according to the maximum and minimum highway speeds in the UK. Thus, we can easily generate a wide range of simulations with different parameters, resulting in a variety of signal profiles. From the perspective of the platoon leader, each BS might look different as the road curves and the linear distance between the platoon leader and BS units vary. Similarly, the distance from the road of the RBS can be varied. This study generates realistic data without the need for a complex, expensive 5G testbed. We can do this simply using the software we obtained very realistic results, which allowed us to simulate multiple scenarios more quickly. The simplicity of the simulation model enables the rapid and effective generation of data for any scenario in which RBS elements are positioned in a location that can potentially interfere with a 5G network. Changing parameters and geography of the simulation makes it possible to produce MR data for any UE in a dynamic environment and develop strategies to prevent such attacks. The simulator described here generates received signal level reports that are calculated from a defined geographical arrangement of base stations and roadways. This will allow the essential parameters of a UE's measurement reports to be generated, which can then be used to devise new methods to prevent RBS attacks. The time series measurement data were not available in any references as it is normal for drive test [12] results to be shown as a distribution of different RSS ranges accumulated throughout the drive test, but the results are broadly compatible with the contour plots in [13].
## 2 D2D Communication System Model
### Communication Link Model  
#### 2.5.1 Signal-to-Noise-Ratio of a Link  
Using SNR values in the probe replies, the source compares between the data rate offered by direct link and D2D link, for the selection of a communication path. The SNR of a direct link can be expressed as  
\[\gamma_{s,d}=\frac{P_{s,d}D_{s,d}^{-\theta}}{N}, \tag{3}\]  
where, \(P_{s,d}\) is the transmission power of a signal from the source to the destination, \(D_{s,d}\) is the distance between the source and the destination, \(N\) is the noise power and \(\theta\) is the pathloss exponent.
## 3 Prototype Description  
Figure 2 depicts the thermoelectric generator developed in the present paper to be installed in volcanic fumaroles and supply power to volcanic monitoring stations. The prototype is composed of two thermoelectric modules, each of them with its own heat exchangers.  
Figure 1: Schematics of a geothermal thermoelectric generator (GTEG) with heat pipes as heat exchangers.  
Figure 2: Designed thermoelectric generator composed of two thermoelectric modules and heat pipes as heat exchangers.  
The hot side heat exchanger of each module is composed of four 450 mm long copper sintered heat pipes with an external diameter of 8 mm. The tubes have been inserted in an 82 \(\times\) 50 \(\times\) 20 mm\({}^{3}\) aluminum plate in which four holes with a slightly smaller diameter than the heat pipes have been drilled with a separation of 3 mm, so that a good thermal contact is ensured.  
Each cold side heat exchanger has also been manufactured with the former procedure. Nonetheless, in this case, 500 mm long heat pipes have been inserted in the base of an aluminum fin dissipator. This dissipator has a base of 82 \(\times\) 50 \(\times\) 14.5 mm\({}^{3}\) and seventeen 40 \(\times\) 1.5 mm\({}^{2}\) corrugated fins. Fins have also been inserted on the heat pipe tubes in order to increase the heat exchange area and therefore, improve heat dissipation to the environment. More specifically, 72 aluminum 104 \(\times\) 27.5 \(\times\) 0.3 mm\({}^{3}\) fins have been added with a separation of 5 mm.  
The hot and cold heat exchangers are assembled by means of four M5 threaded rods, which due to their closeness, ensure a good pressure distribution in the thermoelectric modules and therefore a suitable thermal contact [45]. For compactness, the same threaded rods are used to assembly a generator with two thermoelectric modules, as shown in Figure 2. In this paper, commercial bismuth telluride modules have been used. More specifically, Marlow TG12-8-01 LS [46]. These modules are sealed with silicone, so that the thermocouples are protected, something especially important in this application due to the harsh volcanic environment.  
The heat exchangers also need to be protected. Hence, the heat pipes of the hot side heat exchanger have been coated with an epoxy-based primer [47], while the cold side ones with a marine primer [48]. Furthermore, the weakest parts, such as the ends of the heat pipes and the inter-phases with the aluminum plates or the fin dissipators, have been protected with an epoxy adhesive [49].
###### Abstract  
In order to study the dynamic stress distribution and the fracture area of rock around the tunnel under different orientations of blasting disturbance, AUTODYN finite difference method software was used to conduct numerical simulation research. Gauge monitoring points were set around the numerical model of the tunnel to conduct real-time monitoring of the stress distribution, displacement and fracture area of the tunnel. Based on the analysis of the stress wave propagation law, the following conclusions are obtained: (1) under the condition of the same blasting loads, the stress and displacement of the tunnel is relatively small when the blasting disturbance source is located above the roof, i.e., the stress state of the tunnel is relatively stable and the fracture area around the tunnel is minimal; (2) from the uniaxial stress around the tunnel and the tunnel peripheral displacement, it can be seen that the displacement caused by horizontal direction stress of the tunnel is the largest, and the deformation is mainly concentrated above the floor and at the shoulder, while the vertical wall part has almost no deformation; (3) for brittle materials such as rock, the arch-shaped stress-bearing surface is more likely to disperse stress, while the straight wall and flat floor of the tunnel cannot well disperse stress, resulting in uneven stress on the stress-bearing surface, uncoordinated deformation and ultimately, failure.  
blasting loads; rock; numerical simulation; dynamic failure; tunnel Article
## 3 Numerical Experiments with FM-OFDM-QPSK in Multipath Channels
First, two test responses can be analytically described, as follows (Figure 4):  
\[\dot{H_{1}(\omega)}=1+0.72\times e^{-j\omega\times 5.7\times 10^{-3}}+0. 34\times e^{-j\omega\times 22.83\times 10^{-3}}+0.19\times e^{-j\omega\times 5.712\times 10^{-3}}\] \[+0.09\times e^{-j\omega\times 100\times 10^{-3}}\] \[\dot{H_{0.5}(\omega)}=1+\frac{0.72}{2}\times e^{-j\omega\times 5.7\times 10^{-3}}+\frac{0.34}{2}\times e^{-j\omega\times 22.83\times 10^{-3}}+ \frac{0.19}{2}\times e^{-j\omega\times 57.12\times 10^{-3}}\] \[+\frac{0.09}{2}\times e^{-j\omega\times 100\times 10^{-3}}\]  
Also, for all four modes, the numerical simulations were conducted to evaluate BER (SNR) ratio under the contraction of transmitting signals with channel responses \(H_{1}(\omega)\) and \(H_{0.5}(\omega)\). The results of simulation are presented in Figure 5.  
Figure 4: Test channel responses of the underwater acoustic channel. Figure 3: Results of the bit error rate (BER) (signal-to-noise ratio (SNR)) ratio modeling under AWGN conditions. These values show the increase of the noise-sustainability of the system with increasing of the spectrum spread coefficient \(m_{FM}\) of FM-OFDM under relatively complex multipath responses of underwater acoustic communication channel. Determining the optimal mode of the work for the real condition relates to the compromise between system spectral efficiency and noise-sustainability. With the channel error being lower than \(10^{-1}\dots\ 10^{-2}\), it is possible to get decent values of BER using effective methods of noise-sustainable coding [18].
## 4 Discussion  
### Limitations of the SFR for Low-LAI Sparse Forests Modeling within DART  
The forest representation chosen for this study aimed at minimizing the a priori knowledge necessary to monitor the Tonzi site. Trees modeled within the DART scenes had simplistic crown shapes and few nonphotosynthetic elements, as branches were not included. This has an impact on both LAI and C\({}_{ab}\) estimations, as woody elements' influence on canopy reflectance can be important ([PERSON] et al., 2017; [PERSON] et al., 2018) for heterogeneous and defoliated stands such as the Tonzi site. In particular, [PERSON] et al. (2018) and [PERSON] et al. (2018) showed that inclusion of woody elements within the DART canopies affects NIR reflectances for the resolutions considered in this study, which could explain the overestimated reflectances of the LUT in this spectral region (Figure 5).  
These mismatches between computed and measured reflectances are such that some of the inversion methods tested in this study are not appropriate. Indeed, Table 6 shows that when considering the INT LAI and the INT CAB intervals, a large proportion of the AVIRIS-C images' pixels could not be explained by the LUTs. The consequence is that VI-based inversion criteria outperform those based on spectral intervals. While some VI may use reflectances in the NIR which are overestimated by DART (Figure 5), it appears that the overestimation effect is dampened as VI are less sensitive to differences in reflectance levels, and pixel-derived VIs are almost always within the boundaries of LUT-derived VIs.  
The simplistic ground representation within the DART scenes, with a flat ground having a lambertian reflectance, may also be a source of errors, especially for the spring acquisitions, as important spatial variations of ground reflectance can be expected (see Sections 2.1 and 4.4). Unfortunately, such variation cannot be taken into account by the simple forest representation implemented in this study. A possible way to tackle this, though it would increase computation time, would be to introduce ground reflectance as another LUT parameter. However, the difficulty would be to make sure that most of the possible ground reflectances are captured by the LUT, which may not be an easy task when working with medium- to low-resolution images.