esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Dynamics of an Electromagnetically Actuated
## 4 Geometric Nonlinearities and Potential Energy  
The nonlinear restoring force can be obtained from Equation (19):  
\[F_{r}=\Omega_{0}^{2}z+(k-8a_{0})z^{3}-12a_{0}z^{5}-16a_{0}z^{7},\Omega_{0}^{2} =1-4a_{0}a_{0}\
eq 0, \tag{21}\]  
such that the potential energy function can be expressed as follows:  
\[V_{p}=\frac{1}{2}\Omega_{0}^{2}z^{2}+\frac{1}{4}(k-8a_{0})z^{4}-2a_{0}z^{6}-2 a_{0}z^{8}, \tag{22}\]  
We considered that the constant of integration is zero.  
If \(\eta=z^{2}\), then Equation (22) can be rewritten in the following form:  
\[V_{p}=-2a_{0}\eta\left(\eta^{3}+\eta^{2}-\frac{k-8a_{0}}{8a_{0}}\eta-\frac{ \omega_{0}^{2}}{4a_{0}}\right), \tag{23}\]  
We considered the following parameters:  
\[p=\frac{2}{9}-\frac{k}{24a_{0}};q=\frac{1}{22}+\frac{k-6}{48a_{0}};D=q^{2}+p^{ 3}, \tag{24}\]  
and as a result, the potential (23) has two or four equilibrium points, as follows.  
If \(D>0\), then the potential (23) has two equilibrium points (Cardan formula):  
\[\eta_{1}=0,\eta_{2}=\sqrt[3]{\sqrt{D}-q}-\sqrt[3]{\sqrt{D}+q}, \tag{25}\]  
such that the potential (22) has the following three equilibrium points:  
\[z_{1}=0,z_{2}=\sqrt{\eta_{2}}-\frac{1}{3}z_{3}=-\sqrt{\eta_{2}}-\frac{1}{3} \tag{26}\]The first equilibrium point, \(z_{1}=0\), is a stable point, and \(z_{2}\) and \(z_{3}\) are unstable points (saddle points).  
If \(D<0\), then the potential (23) has four equilibrium points:  
\[\begin{array}{l}\eta_{1}=0,\ \ \eta_{2}=\sqrt[3]{\sqrt{D}-q}\ \ -\sqrt[3]{\sqrt{D+q}},\ \ \eta_{3}=\sqrt[3]{\sqrt{D-q}}\frac{-1+i\sqrt{3}}{2}-\sqrt[3]{\sqrt{D+q}}\frac{ -1-i\sqrt{3}}{2}\\ \eta_{4}=\sqrt[3]{\sqrt{D}-q}\frac{-1-i\sqrt{3}}{2}-\sqrt[3]{\sqrt{D+q}}\frac{ -1+i\sqrt{3}}{2},\ \ i^{2}=-1\end{array} \tag{27}\]  
Considering Equation (23) and equation,  
\[V_{p}=0 \tag{28}\]  
which follows from Equations (23) and (28) where  
\[\eta_{1}+\eta_{2}+\eta_{3}=-1,\eta_{1}\eta_{2}\eta_{3}=\frac{\omega_{0}^{2}}{ 4\omega_{0}}>0 \tag{29}\]  
and, therefore, Equation (28) has only one positive solution (\(\eta_{i}>0\)) and two negative solutions. In this case, the potential (22) for \(D<0\) has only three equilibrium points:  
\[z_{1}=0,z_{2}=\sqrt{\eta_{1}}z_{3}=-\sqrt{\eta_{2}} \tag{30}\]  
In conclusion, the potential (22) always has only three equilibrium points:  
\[z_{1}=0,z_{2}=\sqrt{\eta_{1}}-\frac{1}{3},z_{3}=-\sqrt{\eta_{2}}-\frac{1}{3} \tag{31}\]  
where \(\eta_{1}=\eta_{2}\) if \(D>0\), \(z_{1}\) is a stable point and \(z_{3}\), \(z_{3}\) are unstable points.  
Figure 3 shows that for \(a_{0}=0.001\), there exists one stable point and two saddle points for different values of parameter K.  
Also, the potential energy function (22) increases with the increasing parameter \(K\), and the abscissa of points of intersection between the potential and the horizontal axis increases with the increasing variable \(z\) for \(z>0\). For \(z<0\), this situation is reversed.
# Performance Comparison of ANFIS Models by Input Space Partitioning Methods
## 3 Fuzzy Rule Generation Method According to Input Space Partitioning Method
### Grid Partitioning Method  
Grid partitioning [46] is a method of dividing a space into a grid-like structure so that overlapping parts do not occur in the input space. In general, when the grid partitioning method is applied, the  
Figure 3: Input space partitioning methods: (**a**) grid partitioning method and (**b**) scatter partitioning method.  
Figure 2: Structure method of an adaptive neuro-fuzzy inference system (ANFIS).  
specific partitioned area, that is, the area having the fuzzy rule, is uniformly generated so that the fuzzy rule is smoothly analyzed. Grid partitioning is the method used when the number of input variables is small, that is, when the dimension of the input space is low. For a simple example, when there are 10 input variables and each input variable is divided into two member functions, it is divided into \(2^{10}=1024\) specific areas. In other words, one rule is created for each specific area, and the total number of rules is 1024, which is a very complicated structure. Therefore, the grid partitioning method is mainly used when the number of input variables is small.
# Knowledge Distillation Based on Fitting Ground-Truth Distribution of Images
## 4 Experiments and Analysis
### Experiment Details  
Our experiments were conducted using PyTorch 1.12.0, and the experimental setup used a single NVIDIA GeForce 4090 GPU (NVDIA, Santa Clara, CA, USA) for graphic processing.  
To evaluate the performance of our algorithm, we utilized two prominent datasets: CIFAR-100 and ImageNet-2012. The CIFAR-100 dataset consists of 100 classes of color images with a resolution of 32 \(\times\) 32 pixels. It comprises 50,000 training images and 10,000 testing images. The ImageNet-2012 dataset contains 1,200,000 training images and 50,000 validation images, divided into 1000 categories. The image resolution of ImageNet2012 is below 600 \(\times\) 600 and the average resolution is around 469 \(\times\) 387. The input image resolution after image preprocessing is 224 \(\times\) 224.  
The specific parameter settings used in the experiments for LSH-TL and LSH-T are as follows:  
To make a fair comparison and to better demonstrate the performance gain of our method on the original LSH [13]. The learning rate, number of epochs, batch size, and learning rate decay strategy are the same as the original LSH setup parameters. The learning rate was set to 0.05, and the training was conducted for 240 epochs using a batch size of 64. The learning rate was decreased by a factor of 0.1 at epochs 150, 180, and 210. The pre-trained teacher and untrained student models were obtained from the open-source files of LSH [13].  
Figure 5: Distribution of features in different conditions of a single image.  
In the LSH-TL experiment, the original LSH scale parameter \((\alpha,\beta_{1},\beta_{2})\) is (6, 1, 0) in terms of the scale parameter. To demonstrate the effectiveness of our proposed LSH-TL algorithm, the standard cross-entropy classification loss scale parameter \(\beta_{1}\) is changed to the scale parameter \(\beta_{2}\) of the label replacement loss function \(L_{tl}\) proposed in LSH-TL. The final scale parameter \((\alpha,\beta_{1},\beta_{2})\) is (6, 0, 1).  
In the LSH-T experiment, the scale parameter \((\alpha,\beta_{1},\beta_{2})\) is the same as the original LSH--both are (6, 1, 0). In terms of the selection of the feature temperature parameter, the experimental results are shown in Table 1. The feature temperature parameter taken as 1 represents the original LSH results. We find that the best performance is obtained when the feature temperature parameter is taken as 2. The performance of LSH-T decreases with the increase in the feature temperature after the value of the feature temperature is larger than 2. This is because too large a value of feature temperature can damage the original feature representation. To unify the experimental parameters, we set the feature temperature value to 2 in our LSH-T experiment.
## 4 Resource Allocation Mechanism  
In the case of large decision spaces, heuristic or intelligent optimization methods that obtain high-quality solutions at acceptable computational cost are more attractive in practical engineering environments. To deal with this problem, we decompose the problem into two sub-problems, which are the determination of illumination pattern and the joint allocation of bandwidth and power. In the first stage, we comprehensively consider the demand and urgency (whether it faces the dilemma of being discarded due to timeout) of each cell, and determine the illumination pattern of each time slot in real time based on a weighted greedy strategy. In the second stage, we adopt interference clustering and improved the genetic algorithm for the joint allocation of bandwidth and power to the active beams.
## 4 Results and Discussion
### Validation
#### 4.3.3 Validating the Capacity to Estimate Degree of Corrosion  
In order to validate the results obtained by the INESSCOM monitoring system, and in parallel to the measurements taken during the 4-month study period, measurements were sporadically taken of the general column rebar using commercial equipment, which is a tool based on the corrosion intensity measurement taken by a galvanostatic technique. This equipment comprises two zinc rings that act as a counterelectrode and a guard ring with outer/inner diameters of 60/30 and 100/86 mm, respectively. The Ag/AgCl reference electrode was placed at the centre of the part. The working electrode was the analysed rebar. The working surface was limited by the guard ring. Figure 11 presents part of the obtained results.  
Figure 11: Comparison of the i\({}_{\text{CORR}}\) values obtained using INESSCOM on sensors to those acquired by commercial equipment on rebars: (**A**) the column free of aggressive agents; (**B**) the column with the chloride-contaminated lower zone; (**C**) the column with the carbonated lower zone.  
Figure 12 represents a comparison of corrosion intensity results obtained by two different methods to validate this study. On the one hand, on the ordinate axis, the corrosion intensity results obtained by the INESSCOM monitoring system (measurement performed on the sensors) are shown. On the other hand, the x-axis depicts the corrosion intensity data obtained with commercial equipment (measured on the general reinforcement of columns). As shown, the data are similar and tend to slightly overestimate the damage caused by corrosion as assessed by the monitoring system, but with a value close to 10% (Figure 12). These data corroborate the suitability of the taken measurements and suffice to validate the study data.
## 3 Key Technology of the A2S Integrated MIoT
### Spatial Location Optimization
#### 3.4.1 Trajectory Optimization
By taking into account traffic data, people flow data, and urban development needs, the system generates the best road planning scheme to improve traffic congestion and improve traffic efficiency. In the field of equipment inspection and maintenance, trajectory optimization can help plan the travel path of inspection personnel or maintenance personnel. The system can generate an optimal path planning solution based on the device distribution, inspection, or maintenance requirements to improve the work efficiency and resource utilization. Additionally, in order to improve the energy consumption of both UAV and sensor nodes (SNs), the authors in [141; 142] optimized UAV trajectories. Specifically, an SN-and-UAV collaborative communication framework was designed. Such a framework can smooth the trajectory and facilitate the network re-orchestration for a data gathering  
Figure 9: Trajectory optimization model in A2S integrated system. effort from spatially dispersed MIoT over a large space. Therefore, through trajectory optimization MIoT can enable more efficient data collection and communication and improve resource utilization efficiency, while reducing energy consumption and costs. This is of great significance for marine environment monitoring, resource exploitation and utilization, maritime safety, and other fields.
## 4 Applications of AI in Modern Communication Networks
### Network Optimization  
AI plays a crucial role in optimizing the performance of communication networks by improving bandwidth management, reducing latency, and ensuring the efficient allocation of network resources [91].
## 4 Discussion
### Influence of Implant Type  
In the present study, the BLX implant showed greater 3D deviation at the cervical level, as well as higher bucco-lingual and apico-coronal deviations when compared to the other two implant types.  
These results may be related to the fact that BLX implants use a different guided surgery kit, and their tapered geometry, combined with a different connection (RB instead of RC), may have influenced the results. This system also differs in aspects such as drill diameter progression, depth control design, and sleeve tolerance, which may reduce drilling stability. Additionally, the BLX implant presents more aggressive cutting threads, which, while intended to enhance primary stability, may also increase the risk of deviation during insertion, especially in dense bone. These combined factors could help explain the greater deviations observed in the BLX implants [25; 26].  
It is important to highlight that the BL and BLT implants share the same guided surgery kit, and that BLT implants, despite their tapered geometry, presented lower deviation values than the BLX. Therefore, it is not possible to attribute these differences exclusively to implant geometry [27].
# Optical Lensless-Camera Communications Aided by Neural Network
## 2 Proposed Scheme
### Adaptive Training Sequence Adjustment Mechanism
#### 2.2.2 Training Sequence Adjustment Process  
In this process, after each communication is completed, the receiver determines the number of required training images according to the current bit error rate (BER); it then informs the transmitter by transmitting a control frame. In this paper, via a large number of experiments, the relationship between training sequence length and BER has been established. The main implementation process is shown in Figures 6 and 7 and described below.  
1. Before the terminal device requests the connection, the terminal first captures several image frames sent by the AP for training its neural network to decode the data later;
2. Then, the terminal device sends a request frame to the AP;
3. The AP receives several request frame images for training the neural network at the AP end;
4. The AP sends information to confirm the connection after receiving the request frame.
5. Once the terminal device receives the confirmation message from AP, it sends CTS frame to the AP.
6. If the AP receives the CTS frame, which means the connection is established, the AP begins to send data to the terminal device.
7. After each communication, the receiver will determine the training sequence length required for the next communication according to the accuracy of the data received in this communication, and send CTS frame to notify AP to adjust the length of the training sequence.
8. Finally, either party of the communication can send the end frame to end the communication.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Full Name** & **Data Field** \\ \hline SFD & Start Frame Data & 0011111000 \\ Type & & 01/10 \\ RTC & Request To Connect & 1011 \\ CTS & Confirm To Send & 1101 \\ ETS & End To Send & 1100 \\ LTS & Length of Training Sequence & — \\ FCS & Frame check sequence & — \\ \hline \hline \end{tabular}
\end{table}
Table 2: Special field description of the frame.
## 2 Introduction of Speech Feature Representation and Acoustic Model Configuration  
In this section, we set forth the process of creating the widely used speech feature representation: mel-frequency filter-bank features (FBANK) and mel-frequency cepstral coefficients (MFCC). After that, we introduce the configuration of a deep-neural-network-based hidden Markov model (DNN-HMM), which is widely used as the acoustic model structure in the current automatic speech recognition (ASR) system. This section serves as the background knowledge for a novel noise-robust speech feature extraction algorithm, which is clarified in the next section.
###### Abstract  
Colloidal nano-silica (NS) hydrosols are electrochemically stabilized, polymerized amorphous silica in low viscosity solutions, and in the form of hydrated gels, silica globules or pellicles. Compared to applications in concrete technology, the use of silica-based binders for groundwork applications has received little attention. Silica-based hydrosols impose no known direct risks to humans and are generally courteous to the soil health and ecosystem service functions. Their localized impact on microorganisms however needs to be further investigated. To this end, NS hydrosols have a scope for use as an alternative low-viscose material in groundworks. The current understanding of interactions between NS hydrosols and soil (sand) is, however, confused by the limited availability of experimental evidence concerning undrained static flow and large strain behavior. The contributions, presented in this paper, advance the knowledge through experimental testing, molecular modelling, and micro-analytical measurements. Four grades of colloidal NS (1-15 wt.%) were synthesized for grouting medium-dense sub-angular fine siliceous sand specimens. Consolidated-undrained triaxial compression testing was performed on the base and treated sand for isotropic consolidation over the effective stress range 100-400 kPa. Overall, silica impregnation produced improvements in yield and residual undrained shear strengths, restricted unwelcomed impacts of excess pore water pressure, and led to the formation of generally more dilative, strain-hardening behavior. Steady states and static flow potential indices are also studied as functions of confinement level and viscosity of the NS grout.  
compensation grouting; flow; nano-silica; sand; steady-state; undrained compression Article
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 3 Proposed Approach
### Channel Attention Fusion Block  
In the encoder path, \(\big{(}\text{EB}_{\text{k}}^{\text{out}}\big{)}\downarrow\) is the downsample feature of the output of encoder stage k, and \(\text{SCM}_{\text{k}}^{\text{out}}\) is the output from the shallow feature extraction of \(l_{k}\); they are in the same scale and integrating these features together is an important part of the coarse-to-fine strategy. To fully integrate the features of \(\big{(}\text{EB}_{\text{k}}^{\text{out}}\big{)}\downarrow\) and \(\text{SCM}_{\text{k}}^{\text{out}}\), we introduced the channel attention fusion scheme to obtain better feature representation capabilities.  
As depicted in Figure 5, to better use the \(\big{(}\text{EB}_{\text{k}}^{\text{out}}\big{)}\downarrow\) feature, we introduced channel attention to pay more attention to important feature channels after passing through a \(3\times 3\) convolutional layer. Then, the \(\text{SCM}_{\text{k}}^{\text{out}}\) was multiplied with the attention mask to refine the shallow feature from \(l_{k}\), and then the multiplied feature passed through a \(3\times 3\) convolutional layer. Finally, we used an element-wise summation operation to fuse the refined \(\text{SCM}_{\text{k}}^{\text{out}}\) and \(\big{(}\text{EB}_{\text{k}}^{\text{out}}\big{)}\downarrow\) features.
## 5 Analysis of Hydrodynamic Coefficients
### OC3-Hywind Spar/UMaine-Hywind Spar  
Since the UMaine-Hywind spar is the same as the OC3-Hywind spar with the exception of the installation water depth, the spar buoy concept with varying mesh discretization would be applied to the grid-independence tests in different water depths. The mesh distribution as shown in Figure 8 can be divided into three parts, i.e., upper column, taper, and base column. The panel properties of the spar buoy for each section are given in Table 2.  
The properties of added-mass and radiation damping coefficients with different panel numbers in the surge, heave and pitch directions are illustrated in Figure 9a-f for the OC3-Hywind spar and Figure 10a-f for the UMaine-Hywind spar, respectively. Due to the variation in water depth,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Length/Width (m/m)** & **Panel Type** & **Panel Number** \\ \hline
**Upper Column** & 1.63/0.33 & Triangular & 642 \\ \hline
**Taper** & 0.86/0.5 & Triangular & 236 \\ \hline
**Lower Column** & 1.72/1 & Triangular & 442 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Panel properties of the wetted hull for the OC3-Hywind spar/UMaine-Hywind spar.  
Figure 8: The mesh discretization of the OC3-Hywind spar/UMaine-Hywind spar with triangular panels. (Unit: m).  
it is obvious that the peak values of added-mass coefficients in the pitch mode \(\mu_{55}\) for the OC3-Hywind spar are smaller than those for the UMaine-Hywind spar, whereas the peak values of radiation damping coefficients in the pitch mode \(\
u_{55}\) for the OC3-Hywind spar are larger than those for the UMaine-Hywind spar. By means of the grid independence test, it is exhibited that the difference ratios of 1320 panels and 1494 panels for all hydrodynamic coefficients are less than 1%. Thus, the number of 1320 panels is refined enough for the subsequent simulation of both the OC3-Hywind spar and the UMaine-Hywind spar as shown in the grid-independence test.  
In addition, the aspect ratios of triangular panels for upper column, taper and base column of the spar buoy are set to 1.63/0.33 for 642 panels, 0.86/0.5 for 236, and 1.72/1 for 442 panels, respectively. Eventually, all panels can be grouped into three families of the same size and proportion by panel clustering.  
Figure 9: The mesh convergences for hydrodynamic coefficients (**a**) \(\mu 11\); (**b**) \(\
u 11\); (**c**) \(\mu 33\); (**d**) \(\
u 33\); (**e**) \(\mu 55\); and (**f**) \(\
u 55\) of wetted hull for the OC3-Hywind spar under different panel numbers.
###### Abstract  
**Background:** Few studies report prevalence rates of hearing loss in newborns for nationwide populations. The Dutch well-baby newborn hearing screening covers almost all eligible children and has high participation rates for follow-up screening rounds and diagnosis. This allows calculating reliable prevalence rates of permanent neonatal hearing loss specified by severity. **Methods:** Results from the well-baby newborn hearing screening program and diagnostic follow-up of referred children from 2015 to 2019 were included in calculating prevalence rates. Hearing loss was classified according to the degree of severity. **Results:** A total of 99.7% of 833,318 children eligible for screening were included. A total of 0.3% were referred for audiological diagnostics. Permanent bilateral hearing loss of \(\geq\)40 dB was diagnosed in 23.7% of them and unilateral hearing loss in 14.4%. A prevalence rate of 1.23 per 1000 children was found, 0.46 for unilateral hearing loss and 0.76 for bilateral hearing loss. Moderate hearing loss is most common in children with bilateral hearing loss (0.47), followed by profound (0.21) and severe (0.06) hearing loss. In children with unilateral hearing loss, prevalence rates are the highest for profound hearing loss (0.21), followed by moderate (0.16) and severe (0.09) hearing loss. A total of 87.5% of the children were diagnosed within the age of 3 months. **Conclusions:** Because of the high quality of the Dutch well-baby hearing screening program, reported results approximate true prevalence rates of permanent hearing loss by severity.  
**Keywords:** neonatal hearing screening: prevalence; bilateral hearing loss; unilateral hearing loss; newborn; severity of hearing loss; deaf; UNHS; population screening program +
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