esa-sceva/satcom-chunk-collection huggingface dataset:
# Millimeter Wave Attenuation Due to Wind and Heavy Rain in a Tropical Region
## 4 Results and Analysis
### Rain Attenuation and Combined Rain-Wind Attenuation  
One of the major sources of the attenuation in the FWS link budget calculation is the attenuation due to rain, as discussed in Section 3.1. The maximum attenuations within 1-min intervals have been plotted against the average rain rate in the same intervals, as in Figure 9. Only the intervals with non-zero detected rain falls have been included. The data are divided into three groups: low wind, medium wind and high wind, according to the maximum detected wind speed in each interval. Low wind, medium wind and high wind are defined as the wind speeds in the ranges of 0-0.8989 m/s, 0.8989-5.2597 m/s and \(>\)5.22597 m/s, respectively. The threshold values depend on the statistical distribution of the wind speed, which is assumed to follow the Weibull distribution; they are the wind speeds at 50% and 99.9% of the time. The majority of data in all groups show similar rain attenuation dependency with the rain rate. However, in the medium and high wind groups, we can observe occasional large attenuations up to 36 dB and 47 dB, respectively. The measurement data indicate that the performance of FWS at mm-waves and higher frequencies should consider the effect of wind-induced attenuation in addition to the rain attenuation. In addition, we have demonstrated that the wind speed criteria using the Weibull distribution suggested in [6] can be used to represent the data in calculating the effect of wind-induced attenuation.  
In addition, we have compared the measured data with the ITU-R model for rain attenuation by using the following models: ITU-R P.838 [13], ITU-R P.530-18 [14] and APT-AWG-REP-81 [6]. In Figure 10, the data have been divided into two groups: rain with the top 50% of wind speeds, and rain with the bottom 50% of wind speeds. In contrast to Figure 9, the average attenuations within 1-min intervals have been used to comply with the model definition. The vertical dash line indicates the \(R_{0,01}=83.2\) mm/h rain rate. The results show that the current version of ITU-R models provides a good estimate for the  
Figure 8: Wind speed cumulative distribution function (cdf) and Weibull distribution fit.  
average attenuations when rain rates are close to the \(R_{0,01}\) value. At low rain rates, on the other hand, there was significant wind-induced attenuation where the two groups differ. The combined rain model (ITU-R) and wind model (APT-AWG-REP-81), the purple line, provides a better estimation at low rain rates. Using a 99.99% wind speed to calculate the inclination angle as summarized in Section 3.3.1, the inclusion of APT wind attenuation model adds extra 2.782 dB of attenuation.
## 3 Image Processing Technology Based on CT
### Threshold Cutting and Binarization of Images  
Threshold cutting is an effective method to analyze the grayscale distribution of images based on gray histograms. The image is scanned, marked and cut pixel by pixel. The setting of the threshold value is the key factor influencing the image processing result. In this study, three combination threshold settings (three limits of threshold setting: 0-175, 0-181 and 0-186) were proposed and verified as set out in the following part. Furthermore, the image was converted into a binary type for further analysis.
# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 3 Research Methodology
### Gesture-to-Text Translation  
The study's flow can be understood with the aid of Figure 1, but a more accurate and effective model must be developed to conduct a more thorough investigation. All these problems are addressed and properly evaluated in this work. It is also crucial to discover a less complex model capable of performing the task.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author Name** & **Algorithm** & **Dataset Acquisition** & **Accuracy** \\ \hline [PERSON] et al. [18] & Hidden Markov model (light HMM, multi-stream HMM, and tied-density HMM. & Camera using DCF (digitizer configuration format) file. & Light HMM—83.6\%, multi-stream HMM—86.7\%, tied density HMM—91.3\% \\ \hline [PERSON] et al. [19] & Multi-layered convolutional neural network. & Eshara-lipi dataset in JPG format, containing 1000 images divided 100 per class, which is 128 \(\times\) 128 pixels. & 95\% test accuracy \\ \hline [PERSON] et al. [20] & Deep learning-based models: Pre-trained VGG16 model, natural language-based output network, and hierarchical network. & Images were clicked on Redmi Note 5 pro, 20 megagixel camera. Where all the images were resized to 144 × 144 pixels. Total 15,000 images were stored in the dataset with 5500 images per alphabet. & VGG16—94.52\%, natural language-based output—92\%, hierarchical model—98.52\% for two-hand and 97\% for one-hand \\ \hline [PERSON] et al. [21] & Trbaggboost: ensemble-based transfer learning. & Multi-set and multi-modal database of signals recorded for 100 common signs. & Average classification accuracy was 80.44\%. Classification accuracy improves to 97.04\% as the number of new user data increases. \\ \hline [PERSON] et al. [22] & Naive Bayes (NB), support vector machine (SVM), and simple logistic (SL). & [PERSON]’s dataset called “[PERSON]”s dataset”. & NB—83\%, SVM—94.5\%, SL—89\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Literature Survey.  
Indian gesture recognition movements captured in still photos are detected by the technique utilized in this study. The structure is split into several processes, as shown in Figure 1: picture collection, image pre-processing (segmentation), feature extraction, and classification. The BoVW paradigm was used to identify photographs. The concept of BoVW was influenced by NLP. In image processing, the BoVW concept is a histogram-based representation of independent features. A picture could be used as a script to portray any movement utilizing the BoVW paradigm. Similarly, it is critical to describe each \"word\" in visuals. The following processes are usually included in this process: feature description and codebook generation (visual words). Histograms can be made for all the photos using these codebooks. SVM and other techniques such as CNN, k-NN, logistic regression, and the naive Bayes model can classify photos.
## 2 Experiment
A series of pump-probe experiments were performed with the variation of the IR laser energy from 195 \(\upmu\)J to 691 \(\upmu\)J (3.2 \(\times\) 10\({}^{13}\)-1.1 \(\times\) 10\({}^{14}\) W/cm\({}^{2}\)) for PS and silica samples and from 195 \(\upmu\)J to  
Figure 1: The scheme of the pump-probe experiment. X-ray free-electron laser (XFEL) pulses are generated by the undulator and reflected by the first diamond crystal. The second diamond crystal reflects the beam in the direction to the sample position. The beam is focused by the compound refractive lenses (CRLs) to the size of 50 \(\upmu\)m at the sample position. The Cornell-SLAC Pixel Array Detector (CSPAD) is positioned 10 m downstream from the colloidal sample. Figure 2: Microscopic images of colloidal crystals made from (**a**) polystyrene (PS); (**b**) PS covered with an Al layer; and (**c**) silica samples measured at Linac Coherent Light Source (LCLS). 326 \(\upmu\)J (3.2 \(\times\) 10\({}^{13}\)-5.4 \(\times\) 10\({}^{13}\) W/cm\({}^{2}\)) for PS films covered by Al. Measurements with different IR laser energies were performed at the new position of the sample to avoid sample damage. The upper limit of IR laser energies was set due to the damage threshold of the colloidal crystals and was lower for Al-covered samples due to enhanced absorption of IR light by the Al film. The results for the highest non-destructive IR laser energies, which are marked with arrows on the calibration curve, are presented in this work. At energies higher than 1 mJ, ultrafast melting of the colloidal crystals was observed and was investigated in a separate work [25]. The pump-probe experiment was performed with a time delay variation \(\tau\) from \(-\)50 ps to +1000 ps, with a 10 ps time increment. This time delay region and the time interval were considered in order to resolve GHz frequency dynamics of colloidal crystals. In order to obtain sufficient statistics of the measured data, for each time delay a large number of diffraction patterns (600 for polystyrene and 120 for silica) with and without the IR laser were measured. Typical single-shot diffraction patterns for three different samples are shown in Figure 4. The six-fold symmetry of the diffraction pattern is due to the hexagonal close-packed structure of the colloidal crystals. Two orders of Bragg peaks can be seen in single diffraction patterns shown in Figure 4. A family of 11.0 Bragg reflections, indicated in Figure 4 by squares, was used in further analysis. Indices of the structure are adopted from the hexagonal structure and the point symbolizes the fourth index equal to the negative sum of the first two indices (11.0 equals to 1120).
## 3 System Overview
### Hidden Markov Model  
The motion of the user can be modelled as a Markov process [61] and a HMM is applied to track the mobile user, where the hidden states comprise the possible cell locations and the RSS measurements are taken as observations.  
The formal definition of a HMM is as follows, depicted in Figure 5. The set of states are identical to the set of cells. Let \(S_{1},S_{2},\ldots,S_{T}\) be the sequence of hidden states in the state set \(R\) during a time sequence \(t=1,\ldots,T\), which constitutes the user moving trajectory. The observed Wi-Fi RSS sequence \(O=O_{1},O_{2},\ldots,O_{T}\) up to time T in correspond. The model is characterised by parameters \(\lambda=\{A,B,\alpha\}\).  
Given an observation sequence, the Viterbi algorithm determines the most probable hidden state sequence.  
\[P(O|\lambda)\simeq\max_{S_{1},S_{2},\ldots,S_{T}}P(O_{1},O_{2},\ldots,O_{T},S_ {1},S_{2},\ldots,S_{T}|\lambda) \tag{19}\]  
A is the transition probability matrix. The segmentation rule based on the building topology is encoded in the state transition probability, which is the probability of the user moving from _cell_\(i\) to _cell_\(j\), denoted as \(p_{i,j}=P(S_{t+1}=S_{j}|S_{t}=S_{i})\). If a given cell is linked to \(n\) other cells (including itself), then the probability of moving to one of these cells is defined to be \(1/n\), and the probability of moving to other isolated cells is 0. Here, we use equal probability for simplicity.  
B is the emission probability, i.e., the likelihood of producing observation \(O_{t}\) from _cell_\(S_{j}\), which is to fit the observation to the signature of each cell calculated by the MVGMM and the conditional observation likelihood, referring to Equations (6), (15) and (17):  
\[b_{j}(O_{t})=P(O_{t}|S_{t}=S_{j})=\sum_{k=1}^{K}\pi_{k,j}\mathcal{N}(O_{t}|\mu _{k,j},\Sigma_{k,j})\times\prod_{i=1}^{P}P(O_{Z}(i)|S_{j}) \tag{20}\]  
where \(\Phi_{j}=\{\mu_{k,j},\Sigma_{k,j},\pi_{k,j}\},k=1,\ldots,K\) is the mixture parameters associated with _cell_\(S_{j}\). \(\alpha\) is the prior state probability; here, we assign equal prior probability to each state.
## 1 Introduction  
The quality of a water-supply service lies in the ability of the utility to continue the water-supply service despite the occurrence of various types of adverse events. The primary entity to whom the concept of water safety applies is the consumer [1, 2]. The probability measures the likelihood of the safety loss risk that an adverse event (cause) affects the health or the life of water consumers (effect) [3, 4].  
As a result of the development of water treatment technology, water directed to the water-supply network meets strictly-defined standards and recommendations [5, 6]. Water supplied to recipients must be of proper quality at the point of entry into the supply system and at the point of delivery to consumers [7, 8]. During the transport of water to the recipient, there is often a deterioration of its quality, reflecting the network structure materials, the generation and peeling off of biofilm, and the accumulation and releasing of deposits [9, 10]. However, irregular water quality changes can cause physicochemical and microbiological instability of the pipeline's material characteristics [11, 12]. This occurrence may release sediments, chemicals, and microorganisms that have accumulated over decades, posing health risks and, at the very least, creating aesthetic issues with the turbidity and color of tap water [13, 14].  
Alongside the supply network's technical condition, water's lack of physical, biological and chemical stability is considered the main reason for water quality degradation [15].  
* Treated water introduces into the distribution system [16; 17];
* Material charge (suspensions and particles);
* Microbiological charge; and
* Organic and inorganic nutrients.  
Secondary water pollution entailing changed parameters, may compromise safety and pose a threat to water consumers. The risk to consumers lies in possible water supply disruption or in supplying water with degraded quality. This can result in damage to health, even threaten people's lives, or cause losses in the case of business customers [18].  
Risk analysis should be based on the system's historical and operational feedback, robust analytical methods, and field experience [19; 20; 21]. Ways of limiting the exposure risk of consumers would be as follows [1; 2]:  
* Threat removal = elimination;
* Consumer evacuation from the danger zone = evacuation;
* Danger confinement barriers = insulation;
* A danger barrier = insulation;
* Individual consumer protection (e.g., Through the use of water in containers).  
The multi-barrier system effectively reduces consumers' exposure risk to contaminated water [1]. This system aims to ensure a high level of security in the drinking water supply. It thus assumes that specific countermeasures will be developed for each identified type of threat as the so-called \"barrier.\" Where these come together to form a comprehensive, cohesive system to combat threats of all kinds, this is termed \"multi-barrier\".
## 4 Discussion  
Non-adhesive receding contact appears due to force-less opening in a loaded system at the point where the stresses would become tensile. Opening in adhesive receding contact is, on the contrary, equivalent to the initiation and propagation of a crack. Theoretical analysis based on the Griffith-like energy balance shows that if the adhesion is reversible, then both the opening and closing of receding contact shows instabilities. The experiments partially confirm the theoretical analysis. For example, for both completely adhering contact and contact without adhesion, the force-indentation dependencies practically do not have hysteresis and are almost linear. However, the predicted jump into complete contact predicted for incomplete receding adhesive contact during the unloading stage is not seen in the experiment. The reason for this discrepancy is probably the difference in works of adhesion during the opening and closing of adhesive cracks [33]. As a matter of fact, most experiments with soft elastomers show that during indentation (closing gap), the contacts behave almost as if there were no adhesion, while at the detachment stage, the same contacts show strong adhesion [33]. Presumably, more reversible adhesion can be produced by a liquid layer as in hip joints [34], which then would lead to opening and closing instabilities [35].  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app14209311/s1](https://www.mdpi.com/article/10.3390/app14209311/s1)]([https://www.mdpi.com/article/10.3390/app14209311/s1](https://www.mdpi.com/article/10.3390/app14209311/s1)): Video S1: Experimental video to Figure 6. Video S2: Experimental video to Figures 7 and 8. Video S3: Experimental video to Figures 9 and 10. Video S4: Experimental video to Figures 11 and 12.  
**Author Contributions:** Conceptualization, R.I.K., K.K. and V.L.P.; methodology, V.L.P.; analytical analysis, K.K. and V.L.P.; experimental analysis, visualization (Supplementary Videos), L.A.L.; resources, V.L.P.; writing--original draft preparation, V.L.P.; writing--review and editing, R.I.K.; supervision, V.L.P.; funding acquisition, V.L.P. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Photographs of the contact area corresponding to the points in Figure 11. Supplementary Video S4, which shows the whole experiment in detail, is available.  
**Funding:** This research was funded by DFG, project numbers STA 1106/1-1, PO 810/71-1, and PO 810/55-3.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
**Acknowledgments:** The authors thank [PERSON], [PERSON] and [PERSON] for discussions and critical comments. V.L.P. acknowledges the support of DAAD for research stays in Samarkand.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Broadcast Support  
CR-WSNs should support broadcasting, because WS nodes need to listen for emergency signals, and leave the channel whenever incumbent license holders claim it. Additionally, some applications, such as routing protocols, use broadcast information. Many WSNs use passive sensors with an omnidirectional antenna, where all nodes communicate with each other over the same channel. Therefore, it is easy for them to support broadcast information. However, different nodes may communicate on different channels in CR-WSNs, and they may not receive broadcast information. This may cause higher delay and/or network partition.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 4 Results and Discussion
### Average Shear-Wave Velocities \(V_{s30}\)  
#### 4.4.1 \(V_{s30}\)  
The \(V_{s30}\) for each site has been estimated from the obtained Vs models. The results of the \(V_{s30}\) are shown in Table 2 above.  
In the Oran region, the \(V_{s30}\) values vary between 400 and 670 m/s. This diversity may be due to the lithology and the thickness of each layer.  
The obtained results have been compared with those of a previous study carried out in the same area, using HVSR curve inversion [27]. The Vs ranges obtained are approximately similar to the values obtained in this study. However, one must note that array-based techniques provide more accurate results for shallow layers. From the obtained results, it has been noticed that the soils of our study area can be classified according to NEHRP code [51] as soft rock (category C, see Table 3).  
Figure 7: Two-dimensional shear-wave velocity profiles 1–7, from up to down, respectively. The location of these cross-sections is represented with red lines in Figure 2. The colored areas represent the lithology of the sedimentary formations, with a shear-wave velocity range for each layer.
## 3 System Model
### Dynamic Power Management Model  
We assume that the wireless card can switch to a low-power state to reduce power consumption. The card has two power management states, _i.e._, \(X\)\(\in\) {_on_, _idle_}. Furthermore, the power state can be switched between _on_ and _idle_ by the corresponding actions which is in the set \(Y\)={_s__on_, _s__idle_}. The corresponding power overheads are defined as \(P_{on}\) and \(P_{idle}\) in the _on_ and _idle_ states, respectively. When the state transitions from _on_ to _idle_ or _vice versa_, \(P_{tr}\) is the power consumption. In the \(n\)-th slot, the packet throughput is \(z\), and the required power is as follows:  
\[\rho(\{h_{n},x_{n}\},BER_{n},y_{n},z_{n})=\begin{cases}P_{on}&\text{,if }x_{n}=idle,y_{n}=s_{-}idle\\ P_{n}+P_{n}(h_{n},BER_{n},z_{n})&\text{,if }x_{n}=on,y_{n}=s_{-}on\\ P_{tr}&\text{,otherwise}\end{cases} \tag{8}\]  
where \(x_{n}\) is the power management state, \(y_{n}\) is the power management action, \(P_{tr}\) is the transmission power, \(h_{n}\) is the channel state. \(u\)_is_ the number of symbols per slot. The transmission power is given by:  
Figure 4: Buffer timing diagram.  
\[P_{\alpha}\geq\frac{W\cdot N_{\circ}}{h_{n}}\cdot\frac{-\log(5\cdot BER_{n})\cdot(2 ^{\frac{z_{n}\cdot L}{w}}-1)}{1.5} \tag{9}\]  
We assume that the delay of the power state switching from one state to another is negligibly small when the power management operation is implemented. Let \(P_{x}(y)=[p(x\,\left\lvert x,\,y)\right\rvert_{x,x}\) represent the transition probability matrix. It means that the power state is switched from \(x\) to \(x\,\)' when the power management action is \(y\). In Figure 5, the power management states can be modeled as a constrained Markov chain with transition probabilities.
## 3 Graphene Oxide
### Toxicity and Biocompatibility
The conclusions recommended that GO in low doses is an acceptable candidate for biomedical applications, but when the dosage exceeds 10 mg kg\({}^{-1}\) body weight, some pathological changes, such as pulmonary edema, chronic inflammation, and granuloma formation, are likely to occur. Based on a review report by [PERSON] et al. [19], after incubation for 3 h, the micro-sized (3 \(\upmu\)m) graphene sheets (10% hemolysis at 100 mg/mL) caused less hemolysis than nano-sized GO (350 nm) particles (70% at 25 \(\upmu\)g/mL). The superior hemocompatibility of the smaller-sized graphene oxide sheets was no doubt due to the limited overall surface area available for interaction with red blood cells. [PERSON] et al. [3] also investigated the biocompatibility of the PVA/CS/modified GO (mGO) films by measuring viability of HaCaT cells after seeding on the surfaces of different concentrations of composite films (Figure 3). The PVA/CS/1.0 wt% mGO film could lead to some detrimental effects on HaCaT cell viability after 5 days, and no remarkable differences in cell viability of other films were observed, indicating that films with concentrations below 0.5 wt% mGO had no obvious in vitro cytotoxicity. Mouse preosteoblast cells seeded in contact with chitosan/GO 0.5 and 3 wt% had a significantly (\(p<0.001\)) higher rate of viability and proliferation than the ones in contact with chitosan control after 2 days of culture (Figure 4). This can be related to the presence of GO, which could have a role in increasing cells' metabolic activity and proliferation [20].