esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Characterization Techniques  
Morphologies of the structures present within the obtained powder were analysed by field emission scanning electron Microscopy (FESEM) using a JEOL JSM 6700F microscope (JEOL, Akishima, Japan) equipped with an EDAX PV 9760 detector used to perform energy dispersive spectroscopy (EDS) on the samples, which identified their main chemical composition. High-resolution SEM images were taken in order to observe the resulting morphologies of the powder.  
The powder was also analysed by a PANanalytical X'Pert Pro X-ray diffractometer (Almelo, The Netherlands) using monochromated Cu-K\(\alpha\) radiation (wavelength 1.54 A) over the 20-100\({}^{\circ}\) 20 range with a step size of 0.02\({}^{\circ}\) in order to obtain its crystalline structure and phase composition. Identification of phases was achieved by comparing the diffraction patterns of HA with standards from the International Centre for Diffraction Data (ICDD), formerly known as Joint Committee on Powder Diffraction Standards (JCPDS).  
Figure 1: A flowchart describing the recovery of bones/calcium phosphate as a co-product of fish protein hydrolyses (FPH) production from fish discards and aquaculture by-products.  
Fourier transform infrared spectroscopy (FTIR) was applied to obtain the functional groups of the resulting powders by using a Thermo Nicolet 6700 spectrometer. Spectra were acquired between 400 and 4000 cm\({}^{-1}\). The typical acquisition conditions were 4 cm\({}^{-1}\) of resolution, averaging of 32 scans, an apodization Happ-Genzel, a Mertz phase correction, and zero filling 2.  
To obtain a quantitative evaluation of the chemical composition of the materials obtained, as well as the Ca/P ratio, and identify the possible presence of heavy metals, a PerkinElmer Optima 4300 DV spectrometer was used for inductively coupled plasma optical emission spectrometry (ICP-OES). Calibration for Ca, P, Na, Mg and K analyses were performed with 10-mg/L standards with RSD values below 2.0%.  
The characterization techniques used in this study were performed and repeated for the powders obtained from the three different fish species and after thermal treatment at temperature levels of 750 \({}^{\circ}\)C and 950 \({}^{\circ}\)C.
## 2 Methods
### Robustness Enhancement Method of Illumination  
Similarity calculation has been used in many research fields of image processing [23]. In this paper, the similarity between pixels and the center point in the image is used for the robustness enhancement method of illumination.  
Let \(I\) denote the input image, \(w\) denote the width, and \(h\) denote the height. First, subtract the mean value of the image from the input image:  
\[\left\{\begin{array}{l}u=\frac{1}{w\times h}\sum\limits_{i}^{h}{{I_{i,j}}} \\ I_{i,j}^{m}=I_{i,j}-u\end{array}\right.. \tag{8}\]  
Then, the image is normalized by calculating maximum value and minimum value:  
\[\left\{\begin{array}{l}m_{a}=\max(I)\\ m_{i}=\min(I)\\ \widetilde{I}=(I_{i,j}^{m}-m_{i})/(m_{a}-m_{i})\end{array}\right.. \tag{9}\]  
The difference image is calculated by using the normalized image. Let \(c\) be the center of the image and \(B\) be the difference image. Thus, the formula is as follows:  
\[B_{i,j}=I_{i,j}-I_{c} \tag{10}\]  
Next, the difference image is mapped to [0, 1] space by a sigmoid function. After this, the final similarity image \(I_{i,j}^{\prime}\) can be obtained as following, where \(\alpha\) is the adjustment factor:  
\[I_{i,j}^{\prime}=\frac{1}{1+e^
# Thermal Conductivity and Rheology of Graphene Oxide Nanofluids and a Modified Prediction Model
## 3 Results and Discussion  
### Stability of Graphene Oxide Nanofluids  
It is expected that graphene oxide has good hydrophilicity and water compatibility, which ensures the good stability of graphene oxide nanofluids. To demonstrate the stability of our prepared graphene oxide nanofluids, we compared the newly prepared nanofluids with the nanofluids after standing for 20 days. As shown in Figure 1, there is no obvious sedimentation of nanoparticles after 20 days of standing under atmospheric conditions. This means that the graphene oxide nanofluids we prepared have good suspending stability, no visible aggregation and sedimentation can be observed after a long time of standing.  
To be more convinced, we used a UV-visible absorption spectrometer to measure the light absorptivity of nanofluids at different wavelengths, and the results are shown in Figure 2a. It can be seen that with the increase in mass concentration, the absorptivity at the whole range of wavelength also increases. We then recorded the maximum absorptivity for the GO nanofluids of various concentrations and plotted them against the nanoparticle mass concentration, and the results can be seen in Figure 2b. As is shown, there is a good linearity between the maximum absorption and mass fraction of nanofluid. The nanofluids we prepared conform to [PERSON]'s law, and the nanoparticles are believed to have good dispersibility in the base solution.  
Zeta potential and particle size distribution of nanofluids with mass fraction of 0.002% were measured by Zeta potential analyzer. The results are shown in Figure 3. In fact, Zeta potential is one of the most critical values to validate the quality of the nanofluids stability. According to stabilization theory, the electrostatic repulsions between the particles increase if zeta potential has a high absolute value, which indicates a good stability of the suspensions. Generally, a suspension with a measured zeta-potential above 30 mV (absolute value) is considered to have good stability, and very good stability and little  
Figure 2: (a) UV–Vis absorption spectra of GO nanofluids with different concentrations and (b) relationship between mass concentration and maximum absorption.  
settling can be obtained when the zeta potential is above 60 mV [16]. From Figure 3a, it can be seen that the absolute value of zeta potential corresponding to the peak value is 45.3 mv, which indicates the fairly good stability of nanofluids. From the particle size distribution curve in Figure 3b, it can be seen that the particle size corresponding to the intensity peak is in the order of hundreds of nanometers. For instance, it is ca. 400 nm for 0.002% GO nanofluid. In general, the nanoparticles of the suspension system undergo sever aggregation and then achieve an equilibrium size distribution conforming to the specified particle size range.
## 4 Results
### Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Sensitivity** & **Specificity** & **F1 Score** & **MCC** & **Size** \\ \hline CNN & 89.7 \(\pm\) 1.0 & 92.1 \(\pm\) 2.0 & 84.2 \(\pm\) 4.0 & 92.6 \(\pm\) 1.0 & 76.0 \(\pm\) 3.0 & 1,542,785 \\ LSTM & 87.0 \(\pm\) 1.0 & 89.6 \(\pm\) 1.0 & 81.0 \(\pm\) 2.0 & 90.5 \(\pm\) 1.0 & 69.7 \(\pm\) 2.0 & 208,801 \\ BiLSTM & 87.1 \(\pm\) 1.0 & 90.0 \(\pm\) 1.0 & 80.6 \(\pm\) 1.0 & 90.7 \(\pm\) 0.0 & 70.1 \(\pm\) 1.0 & 470,689 \\ CNN-LSTM & 87.9 \(\pm\) 1.0 & 89.1 \(\pm\) 1.0 & 85.4 \(\pm\) 1.0 & 91.1 \(\pm\) 1.0 & 72.5 \(\pm\) 2.0 & 619,969 \\ CNN-BiLSTM-Attn & 91.8 \(\pm\) 0.0 & 92.7 \(\pm\) 0.0 & 89.6 \(\pm\) 0.0 & 94.0 \(\pm\) 0.0 & 81.0 \(\pm\) 0.0 & 1,239,691 \\
**CNN-BiLSTM-Attn w/HRV** & **96.7 \(\pm\) 0.0** & **95.4 \(\pm\) 0.0** & **97.3 \(\pm\) 0.0** & **94.7 \(\pm\) 0.0** & **92.4 \(\pm\) 0.0** & **1,239,883** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative metrics reporting model performance on ECG signals across different architectures. \begin{table}
\begin{tabular}{c l c c} \hline \hline
**Layers** & **Type** & **\#Kernels/Units** & **Kernel Size** & **Configuration** \\ \hline
0–1 & Input 1: Raw Signal & - & - & - \\ \hline
1–2 & Convolution 1D & \(T:32\to S:8\) & 3 & Activation = ReLU, Strides = 1 \\ \hline
2–3 & BatchNormalization & - & - & - Momentum = 0.9 \\ \hline
3–4 & Dropout & - & - & Rate = 0.3 \\ \hline
4–5 & Flatten & - & - & - \\ \hline
5–6 & BiLSTM & \(T:32\to S:4\) & - & Return\_Sequences = True \\ \hline
6–7 & Attn & - & - & Return\_Sequences = False \\ \hline
7–8 & Dropout & - & - & Rate = 0.2 \\ \hline
8–9 & Input 2: HRV/PRV & - & - & - \\ \hline
9–10 & Concatenate & - & - & - \\ \hline
10–11 & Fully Connected & \(T:32\to S:8\) & - & Activation = ReLU \\ \hline
11–12 & Fully Connected & 1 & - & Activation = Sigmoid \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of properties for the proposed teacher and student CNN-BiLSTM-Attn models, where \(T\to S\) indicates the respective parameter reduction.
## 2 Existing Solutions and Similar Testbeds  
The rapid evolution of Digital Twin technologies, Edge computing, and IoT ecosystems has paved the way for advanced platforms that aim to address real-time industrial needs [9]. However, the development of such frameworks is still in its early stages, particularly when it comes to large-scale adoption and seamless integration across the IoT-Edge-Cloud continuum. Existing solutions [10] primarily focus on specific applications, and while they demonstrate promising results, gaps remain in areas such as scalability, latency management, and orchestration efficiency. In this section, we review state-of-the-art frameworks, platforms, and testbeds that lay the foundation for our proposed approach, highlighting their strengths and limitations in meeting the demands of 6G-enabled Digital Twins and hyper-distributed [11] Edge architectures, enabling flexible and decentralized resource utilization. These testbeds serve as critical experimental environments, enabling researchers to validate novel concepts and technologies that will ultimately shape future industrial systems. In fact, ref. [12] explores how multimodal sensing data inform real-time Digital Twins, aligning closely with the objectives of our hyper-distributed IoT-Edge-Cloud platform for real-time industrial sensing and communication. This study's insights into the 6G research landscape shed light on critical improvements needed to support the platform, especially in testbed configuration and operational requirements.
## 8 Conclusions  
In this paper, we propose a novel strategy to implement sleep mode in the base stations within a multi-tier 5G network using the Markov Decision process. The proposed method considers a three-state base station model with active, sleep and standby modes where the states are adopted between each other based on the MDP-based algorithm. In the MDP-based approach we have proposed a novel reward function, which helps us to find the optimal policy depending on the traffic condition and QoS requirement to improve the energy efficiency. The results are compared with other state-of-art algorithms, from where we find that 'MDP-based 2 state (active-sleep) model' offers the lowest energy consumption, but the highest amount of delay. Whereas, 'Rangisetti 2 state (active-standby) model' reduces this delay but increases the energy consumption significantly. On contrary, Our proposed MDP bases 3 state model offers a fair share of energy efficiency and delay, where the network can save a good amount of energy when compared to Rangisetti 2 state model with ignore-able delay  
Figure 11: Effect of varying variance of shadowing.  
Figure 12: Delay experienced by users for various shadowing effect.  
compared to MDP-based 2 state model. Therefore the proposed model has proven to offer a good trade-off between the energy efficiency and delay. Our future work is focused on considering the effect of interference introduced in HetNet and updating the algorithm to incorporate the effect of interference. Moreover, a stochastic geometry based energy efficiency analysis is also expected to be done as part of the future work.  
Conceptualization, N.I.; methodology, N.I.; software, N.I.; validation, N.I., A.A. and J.A.; formal analysis, N.I., A.A. and J.A; investigation, N.I.; resources, N.I.; data curation, N.I.; writing--original draft preparation, N.I.; writing--review and editing, N.I., A.A. and J.A; visualization, N.I.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BS & Base station \\ HetNet & Heterogeneous network \\ MDP & Maarkov decision process \\ QoS & Quality of Service \\ SNR & Signal to Noise ratio \\ VIA & Value iteration algorithm \\ \end{tabular}
# Evaluation of Noise Levels in a University Dental Clinic
## 2 Background
For this reason, sound levels at the low-frequency end of the spectrum are reduced as the human ear is less sensitive at low audio frequencies than at high audio frequencies. On the decibel scale, audible sounds range from 0 dB, which is the threshold of hearing, to over 130 dB, which is the threshold of pain. Although doubling the sound pressure corresponds to an increase of 6 dB, it takes about a 10 dB increase for the sound to subjectively appear twice as loud. The smallest change that humans can hear is about 3 dB. The sound pressure level (SPL) can be described subjectively based on the decibel (dB) scale. A range of 0 to 40 dB is considered quiet to very quiet, whereas 60 to 80 dB is generally described as noisy. A sound pressure level of 100 dB is perceived as very noisy, whereas anything greater than 120 dB is intolerable. Further, A, C, and Z are frequency weightings used in noise level meters to adjust the measured sound pressure level (SPL) to better match the human perception of sound. The A-weighting curve shows how the human ear reacts to different sound pressure levels, and it is often used to measure noise in the environment and in industry. The A-weighting curve attenuates low- and high-frequency sound pressure levels, giving more weight to frequencies between 500 Hz and 10 kHz, where human hearing is most sensitive. dB(A) is then a weighted scale for judging the loudness that corresponds to the hearing threshold of the human ear [47; 55]. Noise levels given in dBA or dB(A), as they are sometimes written (A-weighted sound levels) instead of dB, are usually found in the relevant literature and are also used in the present study. The main effect of this adjustment is that low and very high frequencies are given less weight compared to the standard decibel scale. Compared to dB, A-weighted measurements underestimate the perceived loudness, annoyance factor, and stress-inducing capability of noises with low-frequency components, especially at moderate and high volumes of noise [48; 49; 50]. The C-weighting curve measures the overall sound pressure level across all frequencies without attenuation. It is used for measuring sound in high-level noise environments, such as rock concerts or airport runways. The Z-weighting curve is also known as \"linear\" or \"flat\" weighting and does not apply any frequency weighting to the SPL measurement. It measures the sound pressure level across all frequencies and is used for scientific measurements or calibrating instruments [51; 52; 53].
# Parallel and Distributed Frugal Tracking of a Quantile +
## 2 The Frugal Algorithm
To accelerate the convergence, larger update values may be used until the estimate is close to the true quantile value; then, extremely small values are used to increase or decrease _step_. ```
0: Data stream \(S\), quantile \(q\), one unit of memory \(\tilde{m}\), one unit of memory \(step\), a bit \(sign\)
0: estimated quantile value \(\tilde{m}\)
1:\(\tilde{m}=0\), \(step=1\), \(sign=1\)
2:for each \(s_{i}\in S\)do
3:\(rand=random(0,1)\)
4:if\(s_{i}>\tilde{m}\) and \(rand>1-q\)then
5:\(step+=(sign>0)\)? \(f(step):-f(step)\)
6:\(\tilde{m}+=(step>0)\)? \([step]:1\)
7:\(sign=1\)
8:if\(\tilde{m}>s_{i}\)then
9:\(step+=s_{i}-\tilde{m}\)
10:\(\tilde{m}=s_{i}\)
11:endif
12:else if\(s_{i}<\tilde{m}\) and \(rand>q\)then
13:\(step+=(sign<0)\)? \(f(step):-f(step)\)
14:\(\tilde{m}-=(step>0)\)? \([step]:1\)
15:\(sign=-1\)
16:if\(\tilde{m}<s_{i}\)then
17:\(step+=\tilde{m}-s_{i}\)
18:\(\tilde{m}=s_{i}\)
19:endif
20:endif
21:if\((\tilde{m}-s_{i})*sign<0\wedge step>1\)then
22: step = 1
23:endif
24:endfor
25:return\(\tilde{m}\)
```
**Algorithm 2** Frugal-2U  
Of course, there is a tradeoff between speed of convergence and estimation stability. Since this tradeoff is directly related to the \(f(x)\) function, the authors set \(f(x)=1\) to prevent huge oscillations, and we shall use this definition of \(f(x)\) throughout this paper. Algorithm 2 only updates the estimate when strictly necessary. There are two different scenarios to be considered: the arrival of stream items greater or smaller than the current estimate. Since these two cases are symmetric, we shall just cover the former here. In this particular scenario, an update is required when observing a large stream item. It is worth noting here that the estimation is updated by at least one, and that the _step_ variable is only used when positive. The authors describe this as follows:  
\"The reason is that when algorithm estimation is close to true quantile, Frugal-2U updates are likely to be triggered by larger and smaller (than estimation) stream items with largely equal chances. Therefore the step is decreased to a small negative value and it serves as a buffer for value bursts (e.g., a short series of very large values) to stabilize estimations. Lines 8-11 are to ensure estimation does not go beyond the empirical value domain when step gets increased to a very large value. At the end of the algorithm, we reset the step if its value is larger than 1 and two consecutive updates are not in the same direction. This is to prevent large estimate oscillations if the step gets accumulated to a large value.\"
## 3 Results and Discussion
### Base Isolation Effectiveness Analysis
These values indicate that the BIKD system could have the same benefits as the existing alternatives. However, the BIKD system has the advantage that it can seismically isolate the rack from the effects of the earthquake acting in any horizontal direction. This system increases the stability of these structures and reduces the seismic demand on them by appropriately choosing the design parameters, as indicated in this investigation. The BIKD system could lead to benefits such as: (i) increasing the safety of the personnel and clients that operate around the racks; (ii) reducing product losses; (iii) increasing the serviceability of racks and consequently; (iv) a faster return to normal industrial activities after the occurrence of a severe seismic event.
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 2 Background
### Action Rules
In the above example, it is stated that attribute \(A\) should equal \(a_{1}\), attribute \(G\) should equal \(g_{2}\), attribute \(B\) should change from \(b_{1}\) to \(b_{2}\), and attribute \(H\) should change from any value to \(h_{2}\). If all of these happen, then a change in our consequent \(D\) from \(d_{1}\) to \(d_{2}\) is expected. Let's say we have two classification rules:  
\[r_{1}=[(a_{1}\wedge b_{1}\wedge c_{1}\wedge e_{1})\Rightarrow d_{1}],r_{2}=[( a_{1}\wedge b_{2}\wedge g_{2}\wedge h_{2})\Rightarrow d_{2}]. \tag{3}\]  
Attributes \(A\) and \(G\) are stable, while \(B\) and \(H\) are flexible. If we have an object with features \(a_{1}\), \(b_{1}\), \(c_{1}\), and \(e_{1}\), the rule \(r_{1}\) says that these four values impact the decision feature \(d_{1}\) of that item. Assuming that the item also has a feature \((G,g_{2})\) and we wish to change its decision feature to \(d_{2}\), based on the info from \(r_{2}\), we could change its value of \(B\) from \(b_{1}\) to \(b_{2}\) and the value of \(H\) to \(h_{2}\). This would be expressed using the action rule [24]:  
\[r=[[a_{1}\wedge g_{2}\wedge(B,b_{1}\to b_{2})\wedge(H,\to h_{2})]\Rightarrow(D,d _{1}\to d_{2})]. \tag{4}\]  
Similarly to classification rules, we evaluate action rules using support and confidence, though calculated differently. In the above example, \(r\) is an action rule generated from the classification rules \(r_{1}\) and \(r_{2}\). The support of our action rule \(r\) is the same as \(sup(r_{1})\). The confidence of \(r\) is the product of both classification rules' confidences: \(conf(r_{1})\times conf(r_{2})\).
## 6 Simulation and Results
### Network Lifetime  
Network lifetime can be defined as \"_the time at which the first network node runs out of energy to send a packet_\" [20]. Therefore, we have investigated the number of dead nodes after a number of rounds to get a clear indication of the network lifetime of both schemes. Figure 9 shows that, after 1500 rounds, a node was out of energy in the Cui et al. scheme, whereas RWFS reached 2800 rounds before the first node died. We can also see that, after 10,000 rounds, only seven nodes had died in our scheme, which is one-third of the number of dead nodes that were in the Cui et al. scheme.  
Figure 8: The average remaining energy analysis between RWFS (the proposed scheme) and the Cui et al. scheme. The average energy of all nodes in the network is calculated at each round for a duration of 10,000 rounds.  
Figure 9: The network lifetime analysis between RWFS (the proposed scheme) and the Cui et al. scheme. The number of dead nodes is calculated at each round in a duration of 10,000 rounds.  
This implies that our scheme is more energy-efficient than the one presented by Cui et al. This can be due to the fact that our scheme consumes less energy per round and uses a smaller packet.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols  
In multi-channel-based distributed MAC protocols, the access channel is divided into one CCH and several SCHs. The protocol mostly focuses on TDMA for the synchronous transmission and CSMA/CA for the asynchronous transmission. As the channel is divided, there is prioritization for safety and non-safety messages; thus, latency in terms of safety messages can be minimized.
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
###### Abstract  
In this work, we propose a deep-learning-based mapping model for simulating and predicting the flow field of Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) of propellers. The model employs image processing and computer vision methods to process the two-dimensional propeller RANS and LES simulation data. First, images are obtained by simulating the flow fields with the location data used to acquire a set of features specific to the corresponding positions. Second, the regression models for the flow fields and the mapping between the two different flow fields are established to predict the LES flow field at that position. Specifically, we utilize a deep convolutional neural network (CNN) for feature extraction from the flow field, which is then integrated with a nonlinear module for the purposes of regression and mapping. The effectiveness and accuracy of the proposed model in flow field prediction are demonstrated by its application to propeller RANS and LES simulations. It is shown that the overall error rate between the LES flow field predictions generated using this method and actual flow field data is 7.92%. Additionally, we also evaluate the model's generalization ability, stability, and robustness by testing it on the data of propeller flow fields at different Reynolds numbers. The results verify the applicability of the proposed model in various problems of flow field simulation and prediction.  
turbulence model; deep learning; turbulent flow simulation mapping; regression model