esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficiency of Using a Simulator for Ultrasound and Laser Dose Calculation in Physiotherapy  
[PERSON] 1  
1 Department of Nursing and Physotherapy, University of Cadiz, 11009 Cadiz, Spain 1  
[PERSON] 2  
2 Department of Physotherapy, Faculty of Health Science, Ampliacion de Campus de Teatinos, University of Malaga, C/ [PERSON] 3, 29071 Malaga, Spain 2  
[PERSON] 1  
1 Department of Nursing and Physotherapy, University of Cadiz, 11009 Cadiz, Spain 1  
[PERSON] 1  
1 Department of Nursing and Physotherapy, University of Cadiz, 11009 Cadiz, Spain 1  
[PERSON] 1  
1 Department of Nursing and Physotherapy, University of Cadiz, 11009 Cadiz, Spain 1  
[PERSON] 1  
1 Department of Nursing and Physotherapy, University of Cadiz, 11009 Cadiz, Spain 13 Research Unit, Biomedical Research and Innovation Institute of Cadiz (INiBICA), Puerta del Mar University Hospital, University of Cadiz, 11009 Cadiz, Spain 34 Research Group: CI-986 Physical Therapy and Health (FISA), Institute of Research in Social Sustainable Development (INDESS), 11009 Cadiz, Spain 4  
[PERSON] Abstract  
Background: In the context of the lockdowns due to COVID-19, e-learning has become the sole tool allowing learning objectives to be achieved successfully. However, for some subjects, training aided only by this type of tool encounters much difficulty, especially because of the experimental nature of such subjects. Aim: to assess the efficiency of a simulator for estimating dose calculation of ultrasound and laser based on surveys and a written test in a group of students. Methods: Surveys conducted voluntarily and anonymously by a group of students enrolled in the subject General Procedures in Physiotherapy I of the undergraduate degree in Physiotherapy. Furthermore, an objective test containing dose calculation problems for ultrasound and laser had to be solved. Prior to the completion of the objective test, the simulator for calculating ultrasound doses was provided to half of the subjects, whilst the other half were provided with the simulator for calculating laser doses, with both of which they were allowed to practice for a whole week. Results: Out of all the students enrolled in the first year of the undergraduate degree in Physiotherapy, a total of 38 students completed the surveys and 44 took part in the test for solving dose calculation problems. The surveys showed that a substantial number of students consider the use of a simulator for learning purposes efficient. This consideration was corroborated: the response times were reduced and the quantifications were the same for ultrasound, and better for laser. Conclusions: the use of a simulator for ultrasound and laser dose calculation is welcomed by a substantial number of students, and also represents a good additional tool when learning problem resolution.  
efficiency; simulator; ultrasound; laser; dose calculation; physiotherapy Article
## 5 Experiment
### Performance Comparison with Setup 4  
Figure 4 presents an extensive evaluation of the attack performance of our proposed GCIA method in response to variations in \(\Delta_{v}\) and \(\Delta_{e}\). Within the comparative methodologies, only the G\({}^{2}\)A2C method supports attacks with multiple fake nodes and multiple fake edges. Therefore, our comparative analysis is exclusively focused solely on the results obtained from the GCIA and G\({}^{2}\)A2C methods when attacking the GAT model on the Cora dataset. It is important to notice that within the Figure 4, the data have been proportionately  
Figure 3: Misclassification rates of various attack methods in Setup 3. The horizontal coordinate corresponds to the different attack methods with different fake edge numbers, while the vertical coordinate illustrates the associated misclassification rates. The blue, orange, and green bars denote the conditions in which the fake edge number \(\Delta_{e}\) is 1, 2, and 3, respectively. (**a**) Result in GCN model with Cora dataset, (**b**) result in GCN model with PubMed dataset, (**c**) result in GAT model with Cora dataset, (**d**) result in GAT model with PubMed dataset.  
scaled without altering the inherent relative proportions, thereby optimizing the visual representation for clearer elucidation.  
From the experimental results, it is evident that the fake node number \(\Delta_{v}\) has a more significant impact on our proposed GCIA method than the fake edge number \(\Delta_{e}\), with the method achieving peak performance at \(\Delta_{v}=3\), \(\Delta_{e}=3\). The G\({}^{2}\)A2C method benefits from both the fake node number and the fake edge number, and surpasses the attack capability of GCIA at \(\Delta_{v}=3\), \(\Delta_{e}=3\). Overall, the proposed GCIA method is adept at realizing heightened attack efficacy within scenarios that impose limitations on the attacker's capabilities.
# Wide-Aperture Bimorph Deformable Mirror for Beam Focusing in 4.2 PW Ti:Sa Laser
###### Abstract  
The bimorph deformable mirror with a diameter of 320 mm, including 127 control electrodes, has been developed and tested. The flatness of the initial mirror surface of about 1 \(\upmu\)m (P-V) was achieved by mechanically adjusting the mirror substrate fixed in the metal mount. To correct for the aberrations and improve the beam focusing in the petawatt Ti:Sa laser, the wide-aperture adaptive optical system with the deformable mirror and Shack-Hartmann wavefront sensor was developed. Correction of the wavefront aberrations in the 4.2 PW Ti:Sa laser using the adaptive system provided increases the intensity in the focusing plane to a value of 1.1 \(\times\) 10\({}^{23}\) W/cm\({}^{2}\)  
adaptive optics; wavefront corrector; deformable mirror; wavefront sensor; reference wavefront; titanium-sapphire laser; beam focusing
## 1 Introduction
The findings in the [16], show that the Information and Communication Technology (ICT) produces around 830 million tons of CO\({}_{2}\) every year and is expected to double by 2020, which means more energy waste since carbon emission is a straightforward result of energy usage in the world, especially when we know that the main components of heterogeneous networks, which are the Macro Base Stations (MBSs) and FBSs, since the number of these stations in every country is growing exponentially. This means the rising demand of energy consumption increases carbon emission in the environment [17]. Thus, HeNB power should be carefully tuned for reducing the CO\({}_{2}\) emissions but still maintaining QoS of the user. In this scenario, adaptive, active and cognitive resources management techniques can be the advanced solution rather than conventional resources management and customary cell planning [18; 19]. In this paper, we proposed an Active Power Control (APC) technique, which not only reduces ICI to the victim MUE, generated by the downlink transmission power of the abysmal set up of femtocell, but also reduces unnecessary power losses by actively tuning its downlink transmission power. Although femtocells are low powered base stations, these power savings are crucial for the green deployment of femtocells, especially for dense deployment of femtocells that would result in increasing the total network consumption as millions of femtocells are expected to be deployed in the next few years. The simulation results show that the proposed APC technique effectively reduces the ICI and optimizes the throughput performance of MUE. Compared with the power control techniques based on the objective Signal-to-Interference-plus-Noise-Ratio (SINR) of Femto User Equipment (FUE), the MUE-assisted power control technique and range-based power control technique, the APC technique provides a balanced trade-off in attaining necessary Quality-of-Service (QoS) for FUE and mitigation of ICI to the macro user existing in the close proximity of the femtocell. The rest of the paper is arranged in the following sections. Related work and the system model are discussed in Sections 2 and 3, respectively. In Section 4, we discussed the existing power control techniques, whereas we presented the proposed Active power Control Technique (APC) in Section 5. We analyze the performance of the proposed and the existing approaches by conduction of simulation in Section 6. Finally, in Section 7, we draw a conclusion.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 4 Simulation and Performance Evaluation  
### Simulation Environment  
In this paper, the simulation is performed on the MATLAB platform. The underwater environment area is set to 500 \(\times\) 500 \(\times\) 500 m, the quantity of sensor nodes varies between 50 and 450, and the transmission distance of each node is set to 150 m or 200 m [16]. The sink node is positioned on the sea surface, situated at the central right area of the sea surface. Each sensor node sends two data packets. Table 1 presents the values of additional parameters utilized in the simulation.  
Figure 4 shows the network after implementing the HECRA under a 500 \(\times\) 500 \(\times\) 500 m area with 50 sensor nodes and a transmission distance of 200 m. The figure includes the sink, the CH, the CM, and the AUV that acts as a dynamic relay between the CHNs. The light blue dashed lines indicate intracluster communication, the black dashed lines indicate intercluster communication, and the red dashed lines indicate AUV relay communication.
## 2 Multiuser Hybrid Beamforming System Model
#### Channel Model  
In this work, the ray tracing channel model is employed for simulation purposes. Stochastic channel models lack the spatial detail required for accurate beamforming simulations, making deterministic models like ray tracing preferable for such tasks. Ray tracing channel models treat electromagnetic waves as rays, accounting for interactions like reflection and diffraction with various surfaces in the environment. These models provide detailed insights into signal propagation, aiding in the design and optimization of wireless communication systems.  
The ray tracing channel model is applied to an OpenStreetMap (.osm) file corresponding to Canary Wharf in London, UK. The latitude and longitude coordinates (51.50375, \(-0.01843\)) specify the BS location. The map is sourced from [[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)) (accessed on 1 August 2023), providing crowd-sourced map data worldwide. Loaded into MATLAB for ray tracing simulation, the map defines transmitter and receiver sites. Multiple receivers are initialized with respective positions, simulating non-stationary users traversing the area. High-rise structures are represented using concrete as the building material.  
Figure 3 depicts the ray tracing environment, where one UE experiences LOS conditions while the other encounters nLOS conditions.
## 6 Experimental Results
### Result and Discussion
'Initialization' indicates that we directly initialize the model parameters before training. 'Model-transfer' means that we share and adjust our model parameters with other models. In the table, we use the accuracy of the model's training and validation sets as evaluation metrics. The specific calculation method for accuracy is obtained from the computation method in Equation (4). In the fourth part, we used the 22 Layers model and combined transfer learning between different sub-models to obtain the key classification results for the complete AES-128 encryption algorithm, as shown in Figure 9, Due to the large number of models, to balance the training time cost and GPU space usage cost, we trained each model for only 20 batches. After simple training, the validation accuracies of all models were as follows: 88%, 91%, 68%, 69%, 66%, 81%, 82%, 84%, 80%, 71%, 65%, 80%, 84%, 79%, 75%, 82%, 81%, 74%, 82%, 78%, 77%, 77%, 64%, 81%, 77%, 75%, 82%, 76%, 84%, 78%, 86%, and 81%. Model-2 achieved the highest accuracy of 91%, and the overall average accuracy was 78%. This demonstrates the generalization effect of the model. Under the same experimental conditions, we compared the performance of three classic networks: SVM [9], VGG-16 [10], and Inception-V2 [11]. The comparative results obtained are shown in the following diagram. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**500**} & \multicolumn{2}{c}{**1000**} & \multicolumn{2}{c}{**3000**} & \multicolumn{2}{c}{**5000**} \\ \hline  & **Training** & **Verification** & **Training** & **Verification** & **Training** & **Verification** & **Training** & **Verification** \\ \hline \multirow{2}{*}{**Initialization**} & \multirow{2}{*}{100\%} & 41.2\% & \multirow{2}{*}{100\%} & 43.6\% & \multirow{2}{*}{77.5\%} & \multirow{2}{*}{65.8\%} & \multirow{2}{*}{83.2\%} & \multirow{2}{*}{83.7\%} \\  & & (Overfitting) & & & & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} **Model-** \\ **transfer** \\ \end{tabular} } & \multirow{2}{*}{70.2\%} & \multirow{2}{*}{69.5\%} & \multirow{2}{*}{80.4\%} & \multirow{2}{*}{77.2\%} & \multirow{2}{*}{83.8\%} & \multirow{2}{*}{82.5\%} & \multirow{2}{*}{84.3\%} & \multirow{2}{*}{80.5\%} \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The influence of transfer learning in small-sample datasets. Figure 9: The attack accuracy comparison of each model of AES-128.
# Deep-Unfolded Tikhonov-Regularized Conjugate Gradient Algorithm for MIMO Detection
## 2 Materials and Methods
### MIMO Channel Model  
MIMO channels are critical to today's modern communication systems. These systems can significantly improve transmission rate, reliability, and spectrum efficiency by using multiple antennas at both the transmitter and receiver. Each element in the matrix characterizing the MIMO channel represents the channel coefficient between a given pair of transmitting and receiving antennas. The effects of multipath propagation are well captured by this matrix, which is important for understanding and improving the functionality of advanced communication networks. A Rayleigh channel in MIMO systems is a model in which Rayleigh fading affects the channel coefficients. This phenomenon happens when there is no direct line-of-sight path and multipath propagation, causing changes in the signal's magnitude. Usually, the model for each element of the MIMO channel matrix \(H\) is an independent, identically distributed (i.i.d.) complex Gaussian random variable with unit variance and zero mean. A mathematical framework used in MIMO systems to make it easier to characterize spatial correlations between antennas at the transmitter and receiver is called the Kronecker channel model [32]. According to the model, transmitter and receiver correlation matrices at each end correspond to separable correlation structures that can be formed from the overall channel matrix. The mathematical expression for this model is as follows:  
\[H=R_{r}^{1/2}H_{w}R_{t}^{1/2} \tag{3}\]  
where \(R_{r}^{1/2}\) and \(R_{t}^{1/2}\) are the Cholesky decompositions of the receiver and transmitter correlation matrices, respectively, and \(H_{w}\) is an uncorrelated Rayleigh fading matrix.  
In fifth-generation (5G) cellular systems, TDL (Tapped Delay Line) channel models--such as TDL-A and TDL-E--defined by 3 GPP simulate multipath propagation [33]. The TDL-A channel model uses a Tapped Delay Line structure to simulate realistic time-varying and frequency-selective scenarios. The taps represent discrete paths with unique delays and power levels. The TDL-A model, which is widely used to simulate urban environments, pre-calculates the channel impulse response to facilitate the assessment of system performance in terms of signal fading and inter-symbol interference (ISI). The channel model holds great significance in the advancement and assessment of 5G technologies, including massive MIMO and beamforming. It offers valuable perspectives for enhancing communication protocols and algorithms, hence guaranteeing reliable performance in practical situations. As such, the TDL-A paradigm plays an important role in the design and implementation of high-performance 5G networks. On the other hand, the TDL-E channel model is particularly well known for its severe multipath conditions, which represent difficult time-varyingand frequency-selective scenarios. These conditions include extended delay spreads and large Doppler shifts. The TDL-E model provides a realistic simulation of harsh urban and suburban environments.
## 3 Materials and Methods
### Proposed Method
#### 3.3.1 Data Exploration and Data Cleaning  
Some data preprocessing techniques are adopted to reformulate the dataset in such a way the deep learning models can learn better. The authors refer to this stage as _dataset cleaning_ and the generated output as the _clean dataset_.  
A preliminary observation of the dataset reveals the following:  
* Pest category distribution is highly unbalanced, ranging from classes with less than 10 samples to classes with more than 1000 samples.
* About 93% of images composing the dataset have a single instance.
* Different life stages of a specific insect are grouped into the same class.  
The consequences of such remarks are as follows:  
* Models trained on the dataset as is are likely to become biased towards the majority class and may have trouble generalizing with respect to the less represented classes.
* The detection model will tend to look for a single bounding box on each image under test rather than hunting for a greater number of insect instances as a consequence of the dataset composition.
* There is low similarity between insects belonging to the same class because the same insect can assume very different aspect throughout its lifecycle, thus increasing the in-class variability.
* There is high correlation among dataset categories due to the similar characteristics shared by different insects at the same stage that decrease intra-class diversity.  
Figure 2 provides an analysis of the challenges introduced by the IP102 dataset and an overview of the visual features of the samples in the dataset. It is evident that images with a different resolution, size, and condition of acquisition make up the dataset. Figure 2a shows insects belonging to different classes which have very similar physical characteristics, making the discrimination process more difficult. On the contrary, some classes contain images of the same pest in different life-cycle stages (larva, caterpillar, and butterfly) which have different features. Alternatively, in Figure 2b, some samples from the IP102 are indicated which show visual patterns that do not refer specifically to the insect depicted. In fact, the IP102 comprises a lot of samples collected by the internet which report unnecessary writing.  
To overcome the above drawbacks, the first strategy proposed by the authors consists of removing all the images with more than one instance from the IP102 dataset. This operation does not have a considerable impact on the dataset's cardinality (given the low percentage of discarded images) but also makes the dataset suitable for a classification task.  
A small 7% of the dataset images with more than one instance is not sufficient to let the model learn to detect more than one insect inside the image under test. The authors have therefore chosen to focus on the accuracy of the detected bounding boxes and on classification performance rather than focusing on the development of a model capable of detecting several instances per image (for which the IP102 dataset is probably not suited).  
Figure 1: Overview of the proposed method.
## 3 Results and Discussion
### Mip  
Figure 6 presents the MIP results for the samples at 7 and 28 days. The suitability of the MIP measurement method has been questioned as a valid means of evaluating a real pore size distribution in cement-based systems mainly due to the bottle-neck effect and systematical misallocation of pore sizes [34]; thus, the direct comparison between different labeled samples (e.g., CA-A vs. NG-A) using the MIP result may not be appropriate, and it may show particular inconsistencies with the interpretation of the results of strength testing, XRD, or TGA/DTG. However, the MIP results remain valuable in the current study because they allow a relative comparison of pore size distribution in each mixture proportion between 7 and 28 days.  
Figure 5: TGA and derivative thermogravimetry (DTG) results at 28 days; (**a**) effect of calcium sulfate type under CaO-activation, (**b**) effect of calcium sulfate type under NaOH-activation, (**c**) effect of activator type when anhydrite was present, and (**d**) effect of activator type when gypsum was present. *: Weight loss possibly related to glass of GGBFS.  
Figure 6 indicates that all the samples exhibited the reduction of total porosity and overall pore size with the progress of curing time, although the reduction degrees were much different from each other.  
In the CaO-activation, the incorporations of both calcium sulfates (i.e., gypsum and anhydrite) produced similar effects on the pore size distributions; for instance, a large portion of pores in the range from 0.02 to 1 \(\upmu\)m were produced in both samples with calcium sulfates (i.e., CA-A and CG-A) as shown in Figure 6a at 3 days, unlike the sample with no calcium sulfates (i.e., C-A), but these pores were significantly removed at 28 days, whereas the sample C-A showed no significant difference between 7 and 28 days in terms of total porosity and pore size. Thus, the use of calcium sulfates in CaO-activation was effective for reducing overall pore size and total porosity, and this would likely lead to the great strength increase from 7 to 28 days.  
In the NaOH-activation, all the samples showed similar pore size distributions and porosity at each age, and thus the addition of calcium sulfates did not show any positive effect on the reduction of porosity and pore size. It is worth noting that the sample with gypsum (i.e., NG-A) showed the largest values of total porosity at all ages, and this should be related to the lowest strength development of NG-A and NG-B. Thus, the combination of NaOH and gypsum for activating GGBFS was found to be the least effective in this study.  
Figure 6: Pore size distributions from MIP: (**a**) effect of calcium sulfate type under CaO-activation, (**b**) effect of calcium sulfate type under NaOH-activation, (**c**) effect of activator type without calcium sulfates, (**d**) effect of activator type with anhydrite, and (**e**) effect of activator type with gypsum.
## 3 Experimental Results
### Propagation Velocity Analysis of the Deformation Waves at the Elastic Stage
Using the same method, the propagation velocities of the deformation waves in the three groups of samples under the loading rate of 0.1 mm/min were estimated as 3.0 \(\times\) 10\({}^{-5}\), 3.3 \(\times\) 10\({}^{-5}\), and 3.5 \(\times\) 10\({}^{-5}\) m/s, respectively. The average wave propagation velocity was 3.27 \(\times\) 10\({}^{-5}\) m/s. Under the loading rate of 1 mm/min, the propagation velocities of the deformation waves in the three tested samples were 10.1 \(\times\) 10\({}^{-5}\), 10.5 \(\times\) 10\({}^{-5}\), and 10.7 \(\times\) 10\({}^{-5}\) m/s, respectively, and the average velocity was 10.4 \(\times\) 10\({}^{-5}\) m/s. The wave propagation velocities under the three loading rates were roughly equivalent to those of the plastic flow waves controlling earthquake migration in the lower lithosphere [6]. The deformation waves' propagation velocities obtained at the loading rates of 0.1, 0.5, and 1 mm/min by testing nine samples are plotted in Figure 12. As the loading rate increases, the propagation velocities of the deformation waves increase linearly. Thus, a positive linear correlation between these parameters with a high correlation coefficient \(R^{2}\) = 0.9964 was revealed. The above dependence was best-fitted by a linear regression formula presented in Figure 12.  
Figure 12: Dependence of the propagation rate of the deformation waves on loading rate.