esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Descriptions of Different Topologies of WSNs
### Cluster-Based Topology
#### 4.2.3 The Clustering Process  
During the establishment of the cluster, it is necessary to take into account aspects like: cluster size and form, how to select the cluster head, how to control inter-cluster and intra-cluster collisions, and energy saving issues. The design of the clustering process is one of the more important issues for the correct functioning of the WSN, due to the probed efficiency of using a hierarchical scheme for communications between the network elements.  
In all the cluster-based protocols, three main phases can be identified during the clustering establishment process: (a) cluster head election phase, (b) cluster formation or set up phase, and (c) data transmission phase (steady-state phase). Different approaches exist to implement each one of these stages. For example, it is possible to use a fixed distribution of the SN and the CH, or to use a dynamic algorithm for the location of the sensors and the CH election. Clusters may be formed in any one of the following ways:  
* _Probabilistic Method_: LEACH protocol uses this method where each sensor randomly picks a real number from 0 to 1. If the number is greater than a threshold value, the sensor declares it as a cluster leader and broadcasts invitation messages to all other sensors. A sensor, not picking a real number greater than threshold joins any one of the leaders. Thus clusters are formed.  
* _By Election Phase_: In this method all the sensors broadcast their information to all other sensors and form a knowledge base. Based on the local knowledge they form cluster and then select a leader.
* _Assigned by the Base Station_: In this method, clusters are formed by the base station. After deployment of the sensors, all nodes communicate with base station and based on the information in the base station, it tries to form optimal nodes. Although this method can form optimal clusters, this method is rarely used because of the cost incurred for the communications of all sensors to the base station. An example protocol of this is the BCDCP [10] (Base station Controlled Dynamic Clustering Protocol).
###### Abstract
However, this type of measurement has some inaccuracies, as participants may not accurately describe task completion [17]. In contrast, behavioral and physiological measurements can more objectively reflect the responses and emotional states of subjects, making the assessment and analysis of situational awareness more accurate [18]. The recording of brain activity via physiological signals can simultaneously reflect various sensory inputs, so as to conduct an in-depth analysis of participants' physiological responses [19]. Each of the above measurement methods has its own advantages and disadvantages, and a single measurement method is insufficient to achieve the accurate measurement of situational awareness. Therefore, this study combined questionnaire measurements, performance evaluations and physiological measurements to effectively evaluate the level of situational awareness among the subjects. This study used a randomized controlled trial in which the training group received 15 working days of working memory training and the control group received a blank control. All training and assessments were conducted in the laboratory. This study used multiple measures of situational awareness for pre- and post-training assessments to examine the effects of working memory training on situational awareness in monitoring tasks. This study extends the relevant theory on SA failure prevention to the field of coal mining and provides suggestions to improve the skills of coal mine monitoring personnel.
## 3 Results and Discussion
### Lateral Displacement of the Piles  
Figure 12 shows the displacement of the existing and new piles under different working conditions. The lateral displacements of the new and existing piles decreased with the distance from the pile top for working conditions 1 to 5. The change rate of the lateral displacement was significantly higher in the cantilever section than in the anchorage section. The lateral displacement at the top of the existing pile was 0.04, 0.092, 0.206, 0.684, and 6.531 mm (>control value of 5.6 mm) for the five working conditions, respectively. The lateral displacement at the top of the new pile was 0.14, 0.273, 0.984, and 8.713 mm for working conditions 2 to 5, respectively. The displacement curves of the new and existing piles are similar in the cantilever section in working conditions 2 to 5. The effect of the fill soil and the upper preload on the existing sheet pile wall was small, and the lateral deformation caused by the upper uniform load was large under condition 1. In working conditions 2 and 3, the lateral displacement of the anchorage section was larger for the  
Figure 11: Bending moment of existing and new piles under different working conditions: (**a**) working conditions 1, 2, and 3; (**b**) working conditions 4 and 5.  
Figure 10: Additional earth pressure under a strip load.  
existing pile than for the new pile. In working conditions 4 and 5, the lateral displacements of the anchorage sections of the new and existing piles were similar at the same buried depth, indicating high stiffness of the pile. Due to the earth pressure of the cantilever wall, the retaining plate of the existing wall bears more pressure than that of the new wall. The lateral displacement was small in working conditions 1 to 4. When the upper uniform load was applied under working condition 5, the lateral displacements of the existing and new piles increased abruptly, and the displacement of the anchorage section changed significantly, indicating that the resistance of the soil in front of the pile stabilized [26; 27].
## 6 Conclusions  
This work optimizes the IRS configuration to obtain a better data rate for the considered user with fixed transmit power. A low-complexity solution based on a greedy method has been proposed to achieve near-optimal performance. The simulation results validate the algorithm numerically. Our work has revealed that with the proposed algorithm, rates can be increased by six times and three times for LOS and NLOS users, respectively, compared to pilot transmission. The results are compared analytically with those obtained from other  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sl. No.** & **Algorithm** & **LOS** & **NLOS** \\ \hline
1 & Pilot Transmission [23] & 21.255 & 21.469 \\
2 & Gradient Descent & 111.683 & 51.389 \\
3 & Genetic Algorithm & 117.384 & 55.521 \\
4 & Heuristic Technique [35] & 100 & 50 \\
5 & Water-filling Algorithm [36] & 119.178 (26 th configuration) & Author not verified \\
6 & Proposed Approach & 126.890 & 66.093 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weighted sum rates across all subcarriers (MBits/s).  
Figure 9: Rate improvements for different users.  
existing optimization algorithms with the same channel conditions, and with the proposed algorithm, a significant improvement is observed across LOS and NLOS users.  
Conceptualization, S.A.J.; methodology, S.A.J.; software, S.A.J., S.S.H., K.R. and A.C.; validation, S.A.J., S.S.H., P.K. and K.K.; formal analysis, S.A.J.; investigation, S.A.J.; resources, S.A.J.; data curation, S.A.J. and A.C.; writing--original draft preparation, S.A.J.; writing--review and editing, S.A.J., K.K. and P.K.; visualization, S.A.J.; supervision, P.K. and K.K; project administration, S.A.J.; funding acquisition, S.A.J., K.K. and P.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article. The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.
## 3 Results
### TCE Suppresses TXA\({}_{2}\) Production by Inhibiting COX-1 Activity  
TXA\({}_{2}\), a potent positive feedback mediator of platelet aggregation, is synthesized from membrane phospholipids. To determine whether TCE inhibits TXA\({}_{2}\) production during collagen-induced platelet aggregation, we measured levels of TXB\({}_{2}\), a stable metabolite of TXA\({}_{2}\). As shown in Figure 5A, collagen (2.5 \(\upmu\)g/mL) markedly increased TXB\({}_{2}\) levels (from 4.1 \(\pm\) 0.9 ng/mL to 160.9 \(\pm\) 5.2 ng/mL). However, TCE pretreatment significantly and dose-dependently suppressed TXB\({}_{2}\) levels (12.5 \(\upmu\)g/mL, 127.8 \(\pm\) 5.5 ng/mL; 25 \(\upmu\)g/mL, 121.1 \(\pm\) 6.9 ng/mL; 50 \(\upmu\)g/mL, 92.4 \(\pm\) 1.2 ng/mL; 100 \(\upmu\)g/mL, 52.3 \(\pm\) 18.8 ng/mL).  
To explore the underlying mechanism, we evaluated the activity of COX-1, the key enzyme responsible for TXA\({}_{2}\) synthesis. TCE significantly and dose-dependently reduced COX-1 activity, achieving a maximum inhibition of 6.8% compared to controls (Figure 5B). These findings suggest that TCE inhibits TXA\({}_{2}\) production through suppression of COX-1 activity.
###### Abstract  
To protect the copyright of the color image, a color image watermarking scheme based on quaternion discrete Fourier transform (QDFT) and tensor decomposition (TD) is presented. Specifically, the cover image is partitioned into non-overlapping blocks, and then QDFT is performed on each image block. Then, the three imaginary frequency components of QDFT are used to construct a third-order tensor. The third-order tensor is decomposed by Tucker decomposition and generates a core tensor. Finally, an improved odd-even quantization technique is employed to embed a watermark in the core tensor. Moreover, pseudo-Zernike moments and multiple output least squares support vector regression (MLS-SVR) network model are used for geometric distortion correction in the watermark extraction stage. The scheme utilizes the inherent correlations among the three RGB channels of a color image, and spreads the watermark into the three channels. The experimental results indicate that the proposed scheme has better fidelity and stronger robustness for common image-processing and geometric attacks, can effectively resist each color channel exchange attack. Compared with the existing schemes, the presented scheme achieves better performance.  
Keywords: watermark; quaternion discrete Fourier transform (QDFT); tensor decomposition(TD); pseudo-Zernike moments; MLS-SVR +
Footnote †: journal: Applied
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 1 Introduction
### Anomaly Detection  
Anomaly detection refers to distinguishing between normal and abnormal samples in data [43; 44]. Anomaly detection mainly detects anomalous data or situations in various fields, such as manufacturing, medical care, and image processing [45; 46; 47; 48; 49; 50]. The difficulty facing anomaly detection is that the frequency of occurrence of abnormal data is significantly lower than normal. Therefore, significant time and effort are required to extract abnormal data. Methodologies for training normal data can solve problems arising from imbalances in abnormal data [51].  
The autoencoder methodology [52; 53] is a method for training the characteristics of normal data in an autoencoder. When information containing abnormal data is input, the autoencoder, which has learned normal data, restores abnormal data. The difference between input data and restore data implies faults in the input data. However, this method uses unsupervised learning that does not contain label data. It also depends on the hyperparameters and the performance of the autoencoder. Therefore, there is the disadvantage that the overall restoration performance is somewhat unstable.  
The DeepLabV3+ neural network model used in tire error detection in this paper uses a structure similar to the autoencoder methodology of anomaly detection to segment the data [42]. Because it labels all training data, it still solves various problems with conventional autoencoders. However, when using images with only simple depth information, there is a possibility of a limit to the training results. Therefore, in this paper, based on the neural network architecture described above, neural network training is performed through anomaly detection and global preprocessing that are not dependent on a specific tire error. The system architecture presented in this paper generates additional information by applying the anomaly detection concept based on the tire depth image. Because the convolution by the image filters does not generate these images, these give the neural network new data about tire errors.
## 2 Results and Discussion
### Relating Findings to the Ofractometric Results  
Since \(\beta\)-pinene stands out in the obtained results, it is of interest to discuss this component's possible response effects. It was reported earlier [29] that \(\beta\)-pinene elicits an antennal response from _A. araheniensis_, which would suggest that a similar response from _A. gambiae_ is plausible as these mosquitos belong to the same family. From the work presented by [PERSON] et al. [20], it was suggested that in the short-range olfactometry tests, all three graminoid plants generated a similar preference from the malaria vector when compared to lake water. However, during long-range attraction trials, _P. repens_ showed the weakest attraction when compared to lake water. Relating these olfactory measurements to the results reported here, regarding the occurrence of \(\beta\)-pinene is not straight-forward. It could be argued that the reduced attraction of _P. repens_ at long range could be a result of \(\beta\)-pinene showing a significantly higher abundance in the roots, which were more exposed in short-range experiments, than in the shoots of the plant. This could suggest the role of \(\beta\)-pinene as a short-range cue, while not acting as a long-range cue. _C. exaltatus_ showed similar attraction effects as _C. dacylylon_, despite the fact that a significantly larger normalized peak was detected for \(\beta\)-pinene in the root sample of _C. exaltatus_. A \"blend\" effect from the combined compounds present in the headspace of the plants is possible as discussed in Section 2.1. In order to move forward with identifying new attractants for the mosquitoes, using the blends of compounds reported for _C. dactylon_ and/or _C. exaltatus_ in Table 3 could be a starting point. This choice is motivated by the fact that it was reported that _P. repens_ induced a lower attraction at long range, while the other two graminoid plants were reported to show both short- and long-range attraction. Still, no significant difference in the abundance of the compounds common to these two plants could be elucidated. This suggests that investigating different ratios of the compounds that are common for the roots and shoots of the plants should be considered for the testing of new blends. These
## 6 Experiment and Result Analysis
### Analysis of Feature Extraction Effectiveness  
The FG-TEFusionNet network proposed in this study extracts facial geometric and texture features independently and integrates them at the decision layer. Thus, the effec  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Meaning** & **Parameters Setting** \\ \hline \(Gram_{i,j}^{l}\) & The Gram matrix & – \\ batch size\({}_{1}\), batch size\({}_{2}\), batch size\({}_{3}\) & Training batch size of branch networks \(g_{1}\), & 2048, 32 and 32 \\ \(Image\_size\) & Input image size & 384 \(\times\) 384 \\ \(\operatorname{Lr\_scheduler}\) & Exponential LR strategy & – \\ \(\varepsilon\) & \(\varepsilon\) specifies the perturbation range & 16/255 \\ \(\operatorname{lr_{1}}\), \(\operatorname{lr_{2}}\), \(\operatorname{lr_{3}}\) & Learning rate of branch networks \(g_{1}\), \(g_{2}\) and \(g_{3}\) & 0.002, 0.002 and 0.01 \\ \(epoch_{1}\), \(epoch_{2}\), \(epoch_{3}\) & Epoch of branch networks \(g_{1}\), \(g_{2}\) and \(g_{3}\) & 1000, 800 and 100 \\ optimize & Parameter optimizer & Adam \\ a & Threshold of WSDAN & 0.45 \\ \hline \end{tabular}
\end{table}
Table 1: Meaning of parameters in the experimental section.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm Model** & **Parameters Setting** \\ \hline \(SENet\) & \(z_{c}=F_{sq}(u_{c})\), \(s_{c}=F_{xx}(z_{c},W)\), \(F_{scale}(u_{c},s_{c})\) \\ \(IMTCNN\) & \(k_{Conv5\times 5}=5\times 5\) \\ \(GramBlock\) & \(F_{ki}^{l}=l,i,k,\ Gram_{ij}^{l}=\sum_{k}F_{ik}^{l}F_{ki}^{l}\), \\ \(WSDAN\) & \(\theta_{c}=0.45\), \(GAvgPooling=1\times 1\) \\ \(DDPM\) & \(s_{\theta}(x_{t},t)\approx-\frac{\epsilon_{\theta}(x_{t})}{\sqrt{1-x_{t}}}\), \(\epsilon\sim\mathcal{N}=(0,1)\), \(q(x_{t}|x_{t-1})\) \\ EfficientNet & \(k_{DSC}=3\times 3\)\(k_{DSC}=5\times 5\), \(k_{eff}=3\times 3\), \\  & stride \(=2\), \(p_{\text{MBConv1}}=1\), \(p_{\text{MBConv6}}=6\) \\ \hline \end{tabular}
\end{table}
Table 2: Parameters settings in the experimental section.  
tiveness of the method in deepfake detection is further validated through an analysis of the experimental results on facial geometric and texture features.
###### Abstract  
The current study evaluated the effect of powder fractionation based on particle size on the chemical composition of macronutrients such as proteins and sugars, on the phytochemical properties (total content of polyphenic compounds, vitamin C, and antioxidant activity), on preservation capacity (water activity), powder functional properties (water absorption capacity and water solubility index), and physicochemical properties (particle size distribution and moisture content) of blackthorn berry (_Prunus spinosa_) powders. The fruits were separated from the plant material and seeds, dried, and then ground using an universal mill for dry materials. Eight fractions were obtained after sieving on sieves with different mesh sizes, such as 1 mm, 0.8 mm, 0.630 mm, 0.450 mm, 0.315 mm, 0.200 mm, and 0.125 mm. The grinding/sieving procedure was effective in separating _Prunus spinosa_ powder into sufficiently different size classes. The maximal moisture content and water activity were 5.61% and 0.250, respectively, showed good preservation from a microbiological point of view, and ensured the prevention of oxidation of biologically active compounds of blackthorn berry powders. For samples with reduced particle sizes, the powder functional properties were greatly improved. The total phenolic content, carbohydrates, and antioxidant activity showed significantly different values for some particle size classes compared to the un-sieved sample. A considerable content of vitamin C was presented in the fraction with large particle sizes, precisely because they did not undergo intense degradation processes. Therefore, the technique of grinding and successive sieving proved effective in enhancing the physicochemical and functional characteristics of powdered blackthorn berries, particularly for smaller particles.  
blackthorn berries; grinding; sieving; active biological compounds; phenolic content; antioxidant activity +
Footnote †: journal: Applied sciences  
0000-0002-2313-2274]Alina-Daiana Ionescu  
Faculty of Biotechnical Systems Engineering, National University of Science and Technology Politehnica Bucharest, 313 Splaiul Independent, 060042 Bucharest, Romania; alina.ionescu1007@upb.ro (A.-D.I.); mredres@upb.ro (M.F.); george.jeute@upb.ro (G.I.); gabriel.constantin@upb.ro (G.-A.C.); madalina.sefan@upb.ro (E.-M.S.); mhaeha.begea@upb.ro (M.B.)
# Atmospheric and Fog Effects on Ultra-Wide Band Radar Operating at Extremely High Frequencies  
[PERSON] and [PERSON]  
1 Faculty of Engineering, Ariel University, Ariel 40700, Israel; [EMAIL_ADDRESS] (N.B.); [EMAIL_ADDRESS] (G.A.P.)  
1 Faculty of Engineering, Ariel University, Ariel 40700, Israel; [EMAIL_ADDRESS] (N.B.); [EMAIL_ADDRESS] (N.B.); [EMAIL_ADDRESS] (N.B.)  
1 Faculty of Engineering, Ariel University, Ariel 40700, Israel; nezahb@ariel.  
haze, when other sensors fail to detect objects, and thus serve as an alternative to infrared and visible light cameras. They are good candidates for landing assistance instrumentation measuring the height from the ground during dust and haze conditions.  
Some of the key challenges in the development of modern radar in the EHF band are the emerging effects during the propagation of electromagnetic radiation through the atmosphere. When millimeter wave radiation propagates in the atmospheric medium, it is subjected to selective molecular absorption [9; 10; 11; 12; 13; 14]. Empirical and analytical models were suggested for evaluating millimeter and sub-millimeter wave propagation in the atmosphere. The transmission characteristics of the atmosphere in this band are shown in Figure 1 for different atmospheric water droplet concentrations, given by \(W_{0}\) in (g/m\({}^{3}\)). The calculations were performed using the millimeter propagation model (MPM) developed by [PERSON] [15; 16; 17; 18]. Millimeter wave signals propagating in the atmosphere suffer frequency-dependent absorptive and dispersive phenomena, causing distortions in amplitude and phase [19]. These effects should be considered in the realization of broadband radar systems [20; 21; 22]. While in millimeter wave communications atmospheric attenuation is the main cause of the reduction in the signal-to-noise ratio and thus leads to bit error rate growth, in FMCW radars, we show that atmospheric dispersion also plays an important role in the accuracy of distance measurements.  
In the current paper, a general approach for studying ultra-wideband FMCW radars operating in the EHF band under conditions of fog and haze is developed. The study is aimed at pointing out the implications of how dispersive effects in the medium (and not only absorption) play a role on the performances of wideband FMCW radars. The numerical Millimeter-wave Propagation Model (MPM) [15; 16] is employed for prediction of the atmospheric frequency response. The calculated propagation factor is used for the study of wide FMCW signal transmission through the atmosphere.
###### Abstract  
The aim of this study was to examine whether additional digital intraoral radiography (DIR) image preprocessing based on textural description methods improves the recognition and differentiation of periapical lesions. (1) DIR image analysis protocols incorporating clustering with the k-means approach (CLU), texture features derived from co-occurrence matrices, first-order features (FOF), gray-tone difference matrices, run-length matrices (RLM), and local binary patterns, were used to transform DIR images derived from 161 input images into textural feature maps. These maps were used to determine the capacity of the DIR representation technique to yield information about the shape of a structure, its pattern, and adequate tissue contrast. The effectiveness of the textural feature maps with regard to detection of lesions was revealed by two radiologists independently with consecutive interrater agreement. (2) High sensitivity and specificity in the recognition of radiological features of lytic lesions, i.e., radiodensity, border definition, and tissue contrast, was accomplished by CLU, FOF energy, and RLM. Detection of sclerotic lesions was refined with the use of RLM. FOF texture contributed substantially to the high sensitivity of diagnosis of sclerotic lesions. (3) Specific DIR texture-based methods markedly increased the sensitivity of the DIR technique. Therefore, application of textural feature mapping constitutes a promising diagnostic tool for improving recognition of dimension and possibly internal structure of the periapical lesions.  
digital intraoral radiography; image preprocessing; periapical lesions; texture analysis +
Footnote †: journal: Applied Soft 2019, 9, 2968