esa-sceva/satcom-chunk-collection huggingface dataset:
# W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks
## 2 Related Works
classified the node deployment schemes for UWSNs into three major categories, static or fixed scheme of deployment, the self-adjusting scheme of deployment, and movement-supported deployment. In the static or fixed scheme of deployment, sensors have fixed or static positions. There are two types of fixed schemes of deployment--random deployment and regular deployment. In the self-adjusting scheme, the depths of sensor nodes are adjusted automatically after initial deployment in order to achieve the specific requirements. Furthermore, there are two types of self-adjusting schemes of deployment--such as uniform self-adjusting deployment and nonuniform self-adjusting deployment. In a movement-supported deployment scheme, sensor nodes cooperate with other sensors to carry out sensing and monitoring tasks [31]. Furthermore, [PERSON] et al. have proposed an optimal scheme for relay selection in UWSNs. In this scheme, they consider two factors, the depth and location of the sensor nodes, for selecting the relay nodes. The unique characteristic of the proposed scheme is the elimination of the synchronization requirement among the source node (SN), relay nodes (RNs), and destination nodes. The second unique feature is overcoming the packet drop issues [32]. Next, [PERSON] et al. have proposed an algorithm consisting of two parts--the establishment of links and transmission of data. The algorithms find out the neighboring nodes at the appropriate ranges. In addition, the proposed algorithm selects the relay nodes based on the depth of the neighbors. For balancing the energy in the network and consequently increasing the network lifetime, the communication links were modified [33]. Moreover, [PERSON] et al. proposed a routing protocol that uses three basic schemes, the detection of the channel, then the assignment of the channel and packet-forwarding mechanisms. The channel detection scheme had a high channel detection probability and the least false alarms [34]. Next, [PERSON] et al. have presented an optimal strategy for routing in UWSNs. This scheme initially considers residual energy and localization information. This routing mechanism collects information about the position for energy saving with vector-based forwarding (ES-VBF) [35]. The aforementioned underwater relay node optimization techniques rely majorly on either geolocation-centric node selection or the quality of service-centric node selection, without considering the dynamic self-mobility of the medium of communication in underwater environments. To this end, the objective of the proposed framework is to reduce energy consumption in the network by adapting underwater whale characteristics for the optimization of the overall performance of underwater networking.
## 4 Experimental Set-Up
### EEG Preprocessing and Reduction of EEG-Channel Montage Set-Up  
The collected EEG signals are preprocessed prior to training to ensure high data quality. First, an average reference is applied and then re-referenced to include the original reference electrode, thereby preserving the full rank of the data [72]. A fifth-order Butterworth  
Figure 2: Experimental timeline illustrating the structured sequence of events within a single MI trial conducted under the MI paradigm. The interval labeled as “Motor Imagery” represents the expected time window during which MI responses are elicited.  
bandpass filter (4-40 Hz) is subsequently applied to isolate the relevant frequency range, as suggested in [73]. Since the dataset focuses on motor imagery rather than higher-order cognitive processes such as multisensory integration, the analysis targets three primary frequency bands: Theta (4-8 Hz), Alpha (8-13 Hz), and Beta (13-32 Hz) [74]. The Gamma band (above 40 Hz) is excluded, as it is typically not informative for MI-based tasks [75].  
To standardize the input across deep learning models, the signals are downsampled from 512 Hz to 128 Hz, in line with the recommendations in [76]. Finally, to enhance the physiological relevance of the analysis, only the MI time window between 0.5 and 3 \(s\) is selected for detailed evaluation.  
To assess the consistency of DL model performance across subjects, the preprocessed EEG signals are configured into four electrode montages: 8-, 16-, 32-, and 64-channel configurations. The spatial configurations of the channel montages used for multichannel EEG acquisition are shown in Figure 3.
## 6 Concluding Remarks  
This paper has provided a brief overview of modern sparse direct methods for the efficient solution of large symmetric linear systems of equations (both positive-definite and indefinite systems) and, in particular, has looked at the algorithms implemented within the shared memory parallel solvers that have been developed for the HSL mathematical software library. Supernodal and multifrontal solvers have been considered and their performance illustrated using problems from a range of practical applications. We conclude that the limiting factor on the use of modern sparse direct methods is the available memory for storing the matrix factors. Out-of-core techniques can significantly extend the range of problems that can be tackled and, in our tests, imposed a relatively modest time penalty. If bit-compatible answers are desired, these can be achieved and our results suggest that, for a multifrontal approach, the penalty is not prohibitive.  
Finally, we note that a number of other (non-HSL) parallel sparse solvers have been developed in recent years. For general symmetric systems, the most well-known are PARDISO [44, 45], MUMPS [46], PaStiX [47] and WSMP [48]. The algorithms implemented within these codes are essentially those that we have discussed in this paper (PARDISO and PaStiX are supernodal packages, while MUMPS and WSMP are based on multifrontal techniques), although each varies in the fine details (such as the choice of pivoting and scaling strategies). While it is beyond the scope of this paper to compare the HSL solvers with these packages, results presented in [5] demonstrate that the performance of the HSL solvers is competitive.  
Figure 6: Cumulative proportions of the time for different phases of HSLMA97 (gaps between curves represent the proportion of the total time spent in that phase). Indefinite problems.
# Train-Induced Vibration Analysis and Isolation Trench Measures in Metro Depot Structures
## 4 Study on Vibration Isolation Trench Measures for Depot
### The Effect of Trench Filling Materials on Vibration Isolation
For rigid infilled trenches, although the overall vibration isolation performance is weaker compared to flexible infilled trenches, rigid materials have higher overall mechanical strength and exhibit good resistance to compression creep. As a result, rigid infilled trenches are superior to flexible infilled trenches in terms of construction convenience and maintainability. However, as shown in Table 8 and Figure 28, when the impedance ratio between the rigid material and the surface soil reaches 8.7, further increasing the stiffness of the rigid material does not lead to significant improvement in vibration isolation performance. The influence of open trenches and infilled trenches on the vibration isolation effects of the over-track building was discussed in detail in Sections 4.1-4.4. However, with regard to the arrangement of trenches, this study considered the isolation trenches to be symmetrically arranged on both sides of the track. The effects of single-sided trenches or trenches asymmetrically arranged on both sides of the track were not analyzed. Moreover, there are multiple tracks within the depot area. The variation in distances between different tracks and the trenches leads to changes in the incidence angle of vibration waves when they propagate to the interior wall of the trenches. This study did not investigate the vibration isolation performance of the trenches under the operating conditions of trains running on different tracks. Therefore, further research on the above aspects is required in future studies.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 2 Background and Overview of WSNs
### Network Coverage
If the distance from a node to the BS is shorter than the distance from a node to the CH, then it will send data to the BS directly; otherwise, it will transmit data through the CH. Energy consumption is improved in this protocol, but in large networks, the lifespan of nodes is short. Another protocol that handles the distance factor in WSNs when forming clusters is Intermediate Cluster Head LEACH (ICH-LEACH) [102], in which the problem of LEACH related to the distance from a CH to the BS is considered. As mentioned before, in the LEACH, data are sent directly from a CH to the BS regardless of the distance between them; hence, the necessary energy to send data will be very high if a CH and the BS \begin{table}
\begin{tabular}{c c c} \hline \hline
**Protocol** & **Strengths** & **Weaknesses** \\ \hline P-LEACH [96] & Energy conservation, accurate tracking of & High complexity due to sink mobility. \\  & the mobile sink, stability. & High complexity and overhead. \\ \hline C–LEACH [97] & Network coverage, & \\  & multi-hop communication. & High complexity and overhead. \\ \hline O-LEACH [98] & Good connectivity and coverage. & Delay of data delivery and control overhead \\ \hline EZ–LEACH [99] & Efficient CH selection based on & Extra overhead. \\  & different zones. & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of network coverage-based cluster formation protocols. are far from each other, thus affecting the lifetime of the network noticeably. To overcome this problem, a new method has been proposed to maximize network lifetime and minimize power consumption. The idea is to use an intermediate CH if the current CH locates far from the BS. The selection of an intermediate CH depends on the BS, CH, and intermediate CH positions. In the initialization stage, the BS transmits its coordinates to all nodes in the network, and these coordinates are used by CHs to determine if they can transmit data to the BS directly. In the cluster setup stage, each CH sends its distance from it to the BS with its position to other CHs. Such information is used to form a routing table in each CH to be used in the next stage. During the steady stage, a CH checks its routing table to decide whether it can send the collected data to the BS without any relay CH (if it is the nearest node) or through intermediate CH. The source CH selects an intermediate CH in a case wherein the distance from the intermediate one to the BS is less than the distance from the source CH to the BS. The ICH-LEACH protocol is good at extending lifetime of a network compared with the LEACH protocol, and the receiving packets by the BS increase. Moreover, the dead nodes and the total consumed energy decrease. A summary of these protocols based on distance in cluster formation is presented in Table 9. The strengths and weaknesses of each protocol are also provided.
## 4 Experiment
### Discussion and Analysis
#### 4.3.2 Array Gain Analysis  
Array gain is an important indicator of array performance. Assuming that the sparse arrays receive a 300 Hz sinusoidal signal with a duration of 1 s. The array gains of the 40-element off-grid array and on-grid array designed using GEWOA are calculated to verify the feasibility of the proposed method under the Gaussian white noise background. The theoretical value of the array gain for the 40-element array is 16.02 dB.  
The change trend of the array gain with the input signal-to-noise ratio (\(SNR_{in}\)) is shown in Figure 20. It can be seen that the array gain decreases at a lower input SNR. When the \(SNR_{in}\) is \(-30\) dB, the array gain of both arrays decreases by about 1.5 dB compared with the theoretical value. When the \(SNR_{in}\) is higher than \(-20\) dB, the array gain of both arrays decreases by, at most, 0.3 dB compared with the theoretical value, which proves the feasibility of the designed array based on the proposed method from the perspective of array gain.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 2 Localization Analysis of Single Array
### Azimuth Precision  
According to Equation (5), azimuth \(\Phi\) was a function of time delay \(\tau\).  
\[\Phi\,=\,\mathrm{F}(\tau)\,=\,\mathrm{F}(\tau_{12},\,\tau_{13},\,\tau_{14},\, \tau_{15}) \tag{6}\]  
The transmission form of azimuth error \(\sigma_{\varphi}\) can be expressed.  
\[\sigma_{\varphi}^{2}\,=\,(\frac{\partial\varphi}{\partial\tau_{12}}\sigma \tau)^{2}+(\frac{\partial\varphi}{\partial\tau_{13}}\sigma\tau)^{2}+(\frac{ \partial\varphi}{\partial\tau_{14}}\sigma\tauTaking derivative of \(\tau\) in Equation (5):  
\[\left\{\begin{array}{l}\frac{\partial\varphi}{\partial\tau_{12}}=-\frac{\partial \varphi}{\partial\tau_{14}}=\frac{1}{1+\text{tan}^{2}\,\varphi}\cdot\frac{\tau _{15}-\tau_{13}}{\left(\tau_{14}-\tau_{12}\right)^{2}}\\ \frac{\partial\varphi}{\partial\tau_{13}}=-\frac{\partial\varphi}{\partial \tau_{15}}=-\frac{1}{1+\text{tan}^{2}\,\varphi}\cdot\frac{1}{\tau_{14}-\tau _{12}}\end{array}\right. \tag{8}\]  
So the expression of azimuth error \(\sigma_{\varphi}\) is  
\[\sigma_{\varphi}=\frac{\sigma\tau}{1+\text{tan}^{2}\,\varphi}\sqrt{\frac{2 \left(\tau_{14}-\tau_{12}\right)^{2}+2\left(\tau_{15}-\tau_{13}\right)^{2}}{ \left(\tau_{14}-\tau_{12}\right)^{4}}} \tag{9}\]  
Solving Equations (2) and (5):  
\[\left\{\begin{array}{l}\left(\tau_{14}-\tau_{12}\right)^{2}+\left(\tau_{15} -\tau_{13}\right)^{2}=\frac{D^{2}}{\
u^{2}}\\ \left(\tau_{14}-\tau_{12}\right)^{2}=\frac{D^{2}}{\
u^{2}\left(1+\text{tan}^{2 }\,\varphi\right)}\end{array}\right. \tag{10}\]  
Substituting Equation (10) into Equation (9):  
\[\sigma_{\varphi}=\frac{\partial\varphi}{\partial\tau_{i}}=\frac{\sqrt{2}c}{D }\sigma_{\tau} \tag{11}\]  
Thus, the azimuth error is determined by \(c\), \(D\), and \(\sigma_{\tau}\). We assume a value of \(c\) = 343 m/s for the sound velocity and employ a sampling rate of 5000 Hz. The sampling interval is 200 us and the distribution of azimuth error is shown in Figure 2.  
Figure 2 shows that the relationship of \(\sigma_{\varphi}\) and \(c\) was linear, as well as \(\sigma_{\tau}\). However, the one between \(\sigma_{\varphi}\) and \(D\) was inverse. In the condition of \(D\geq 2\) m, \(\sigma_{\varphi}\) stays at an optimal level as 0.03\({}^{\circ}\) in Figure 2a; when \(\sigma_{\tau}\) = 100 us in Figure 2b it stays 0.1\({}^{\circ}\) when \(c\) was set as 343 m/s.
## 3 Proposed Method
### Algorithm Training Procedure  
Based on a literature review of watermark embedding in static images and video signals [34; 36], it was decided to use a two-step training procedure to achieve higher algorithm performance. One training epoch of the discriminator was performed first, followed by one training epoch of the generator (watermark encoder and decoder). The implementation of the learning process for both modules used the Adam optimizer [45] with a learning rate of \(lr=0.0001\), which is often used in the literature to optimize multivariate objective functions.  
A standard approach used in generative adversarial networks was used to optimize the discriminator:  
\[L_{discriminator}=\min[-[\log(D(x))+\log(1-D(G(x,s)))]] \tag{7}\]  
where  
\(D\) is the discriminator;  
\(G\) is the generator (watermark encoder and decoder);  
\(x\) is the cover; \(s\) is the watermark;  
and \(G(x,s)\) is the watermarked image.  
Figure 14: Examples of generated watermarks for all tested values of \(N\) and \(n\).  
An aggregate loss function taking into account the generator error, encoding error, and decoding error using appropriate weighting factors was used to optimize the watermark encoder and decoder module. The generator loss function was defined as follows:  
\[L_{generator}=\min[\log(1-D(G(x,\ s)))] \tag{8}\]  
The optimization of the encoder is based on the use of a loss function described in [40], taking into account 3 measures of image similarity, i.e., the mean square error (MSE), similarity index (SSIM), and multi-scale structure similarity index (MS-SSIM), which allows the high transparency of the watermarked image:  
\[L_{encoder}=\lambda_{a}\big{(}1-SSIM\big{(}x,\ x^{\prime}\big{)}\big{)}+(1- \lambda_{a})\big{(}1-MSSSIM\big{(}x,\ x^{\prime}\big{)}\big{)}+\lambda_{c}MSE \big{(}x,\ x^{\prime}\big{)} \tag{9}\]  
where \(x\) and \(x^{\prime}\) are the cover and watermarked cover;  
\(\lambda_{a}\) and \(\lambda_{c}\) are weighting factors for similarity metrics.  
The above function was also used to optimize the decoder but supplemented with a lossy mapper, taking into account that the watermark is not required to decode the binary signature without error. The sum of both start functions gives the final decoder start function:  
\[L_{decoder\_visual}=\lambda_{a}\big{(}1-SSIM\big{(}s,s^{\prime}\big{)}\big{)}+ (1-\lambda_{a})\big{(}1-MSSSIM\big{(}s,s^{\prime}\big{)}\big{)}+\lambda_{c} MSE\big{(}s,s^{\prime}\big{)} \tag{10}\]  
\[L_{decoder\_mapper}=MSE(seq,\ seq\cdot) \tag{11}\]  
\[L_{decoder}=L_{decoder\_visual}+\lambda_{d}L_{decoder\_mapper} \tag{12}\]  
\[L=L_{encoder}+\lambda_{b}L_{decoder}+\lambda_{c}L_{generator} \tag{13}\]  
where \(s\) and \(s^{\prime}\) are the watermark and decoded watermark;  
\(seq\) and \(seq\) are the binary signature and decoded binary signature;  
and \(\lambda_{b}\), \(\lambda_{d}\), and \(\lambda_{c}\) are weighting factors taking into account the contribution of individual elements to the final loss function coding and decoding modules.
###### Abstract
The authors evaluated whether a single overnight in-laboratory PSG session would be suitable for assessing SB episodes in patients with OSAS. The authors showed that a greater proportion of stage N2 sleep, a longer sleep time in a supine position, a lower AHI, and a higher oxygen saturation were associated with SB episodes in patients with OSAS. A longer sleep time in a supine position (especially lasting for a duration of over 280 min) might be strongly associated with SB episodes in such patients [13]. [PERSON] et al. evaluated the modification of a Complications Associated SRDB (i.e., nocturnal enuresis) in children treated with the rapid maxillary expansion (RME). Nocturnal enuresis (NE) is the involuntary loss of urine during sleep. This condition affects between 7% and 10% of all 7-year-olds and between 0.5% and 2% of young adults. This randomized controlled clinical trial (RCT) was designed to evaluate whether RME could reduce the frequency of NE and improve quality of life in children, after ruling out a placebo effect, in order to investigate whether the effects of RME on NE are related to the morphology of the nasal and pharyngeal airway. The hypothesis was that RME treatment would lead to a reduction in nocturnal urine production as a result of improved breathing obtained by enlarging the nasal and pharyngeal airway. The authors suggested that RME reduces the frequency of NE and that there might be an association between the amount of expansion and the reduction in night urine volume [14]. [PERSON] et al. evaluated the association of oral parafunctions (i.e., SB) in young adults. The authors evaluated SB behaviors in a sample of healthy young adults using a portable EMG/ECG device. They showed that during sleep time, Masticatory Muscles activities (sMMA) events associated with heart-rate increase are quite frequent. The average SB index over the four nights that were recorded was 3.6 +/ - 1.2 [15]. [PERSON] et al. presented a paper regarding the Pediatric Sleep Questionnaire (PSQ). They assessed the accuracy of specific items added to the original PSQ, particularly related to the patient's anamnestic history and the quality of sleep, for the screening of OSAS in a paediatric population. The authors highlighted how the investigation of sleep quality can be an important parameter in order to more effectively identify potential pediatric subjects at risk of OSAS. The assumption of bizarre or abnormal positions during sleep (e.g., hyperextending the neck or sitting), especially if present with other determinants that alter sleep quality, should be considered an alarm bell for the investigation of possible obstructive sleep apnoea [16].
###### Abstract
[35] found that an increase in the air flow rate from 14 SLM to 42 SLM increased the intensities of the exited NO\({}_{\gamma}\), OH, and N\({}_{2}\) species in the plasma. [PERSON] et al. [34] observed that the intensity of the emission lines related to NO, N\({}_{2}\)+, and N\({}_{2}\) was enhanced with a reduction of the air flow rate from 150 L/min to 20 L/min. [PERSON] et al. [20] demonstrated that the N\({}_{2}\), N\({}_{2}\)+, and OH species were dominant in air plasma, while the intensity of the O and NO emission lines was very low, and that the optimal treatment time of grape seeds was from 40 to 60 s. [PERSON] et al. [21] observed that the GAD plasma produced using a mixture of N\({}_{2}\)/O\({}_{2}\) gases was a strong source of nitrogen oxides (NO and NO\({}_{2}\)), while the dominant species in its emission spectra were N\({}_{2}\) particles. The variation in the gas composition also had an influence on the plasma's temperature. [PERSON] et al. [26] indicated that the dominant particle in their GAD plasma was N\({}_{2}\), with low amount of NO and OH species. [PERSON] et al. [31] observed that the intensity of the atomic oxygen lines in air plasma was very low due to the rapid dispersal of the exited atomic oxygen. [PERSON]. [PERSON] et al. [33] observed that the dominant species in DBD air plasma were N\({}_{2}\) and N\({}_{2}\)+, while the intensity of the emission lines related to the NO and atomic oxygen species was very low, indicating that the amount of oxygen species in the plasma was very low. Therefore, to successfully use gliding arc discharge plasma for the irradiation of plants or algae, it is important to determine the optimal discharge parameters and to estimate the composition of the used plasma. It is also important to determine the relationship between the composition of the plasma and the discharge parameters (voltage, frequency, gas flow rate, etc.). The main aims of this work were to create a gliding arc discharge plasma device to investigate the arc evolution over time at various output voltages, to identify the main species produced in GAD air plasma and to determine the influence of the voltage on the emission spectra of the air plasma. Additionally, the influence of plasma irradiation on the physical and chemical parameters of BG-11 freshwater microalgae growth medium was investigated.
## 4 Methodology
### The 12-Parameter Transformation
#### 4.6.2 Inverse Transformation  
To perform the inverse transformation for the 12-parameter transformation, Equation (72) needs to be modified by expressing the element \(\mathbf{X}_{i}\) on the left side of the equation:  
\[\mathbf{X}_{i}=\mathbf{S}^{-1}\mathbf{R}^{-1}\mathbf{H}^{-1}(\mathbf{X}_{o}- \mathbf{t}) \tag{77}\]  
where matrix \(\mathbf{R}^{-1}\) is defined by Equations (54) or (55), inverse of the scale matrix is defined by Equation (71), and inverse of the skew matrix can be easily calculated:  
\[\mathbf{H}^{-1}=\begin{bmatrix}1&-h_{xy}&h_{xy}h_{yz}-h_{xz}\\ 0&1&-h_{yz}\\ 0&0&1\end{bmatrix} \tag{78}\]  
In a shorter form, Equation (77) can be written as  
\[\mathbf{X}_{i}=\mathbf{U}^{-1}(\mathbf{X}_{o}-\mathbf{t}) \tag{79}\]