esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurements and Analysis of Sound Reflections from Selected Building Facades
## 2 Materials and Methods
### Analysis Methodology  
Sound sampling occurred at one time on three microphones located according to the measurement cycle. Based on the history of sound pressure changes obtained from measurements, the equivalent sound level A for each measurement minute L\({}_{\text{Aeq1 min}}\) was determined. The obtained values of equivalent sound level L\({}_{\text{Aeq1 min}}\) were compiled into 20 pairs of results for each measurement cycle. The measurement cycles carried out are summarized in Table 2. Then, from each cycle, 10 pairs of differences not containing interference were selected. Disturbances are understood as environmental noises such as dog barking, shouting, or passing vehicles. During the measurements, the occurrence times of such clearly audible events were recorded. These records allowed the elimination of samples laden with interference. Initially, results differing by \(\pm\)2 dB from the mean were discarded. Finally, 10 pairs remained, each differing from the average result by less than 0.6 dB. The goal of the work was to establish the differences in the results recorded by the three microphones. The differences of \(L_{\text{eqM1}}-L_{\text{eqM2}}\) and \(L_{\text{eqM1}}-L_{\text{eqM3}}\) were analyzed and compared with a standard correction value of \(-\)3 dB. A statistical evaluation of the results was carried out. The standard error was reported, and the non-parametric Wilcoxon signed-rank test was performed. This test determines whether the mean values of the differences are not remarkably different from zero.  
Differences in the distance of the measurement microphone from the source were taken into account. Measurement microphones M2 and M3 were not equidistant from the sound source by \(a_{1}\)'. For example, at the first measurement level, microphone M2 was  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Facade Type**} & \multicolumn{1}{c}{**Cycle Name**} & \multicolumn{1}{c}{**Height of Measurement at Elevation [m]**} & \multicolumn{1}{c}{**Parameters Measured in Each Cycle**} \\ \hline \multirow{3}{*}{A} & A1 & 2 (M1 and M2) & \\ \cline{2-3}  & A2 & 6 (M1F and M2F) & \\ \cline{2-3}  & B1 & 2 (M1 and M2) & \\ \cline{2-3}  & B2 & 6 (M1F and M2F) & \\ \hline \multirow{3}{*}{C} & C1 & 2 (M1 and M2) & \\ \cline{2-3}  & C2 & 6 (M1F and M2F) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of measurement cycles.  
3 m closer to the sound source. Therefore, the results obtained at M3 were normalized (standardized) and converted to \(a_{1}\)\({}^{\prime}\) distance using Formula (1).  
\[L_{eqM3n}=L_{eqM3}+10 log\left(\frac{a_{2}^{\prime}}{a_{1}^{\prime}}\right),\ [\text{dB}], \tag{1}\]  
where  
\(L_{eqM3n}\)--normalized (standardized) equivalent sound level;  
\(L_{eqM3n}\)--measured equivalent sound level;  
\(a_{2}\)--distance from the sound source to the point M3;  
\(a_{1}\)\({}^{\prime}\)--distance from the sound source to the point M2.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results  
AI models like artificial neural networks, support vector machines, and adaptive neuro-fuzzy inference systems have been used to predict the key parameters of wastewater that are quite difficult to predict.  
Hybrid frameworks that pair ANNs with supplementary methodologies, such as fuzzy logic, have been constructed to reinforce the two key aspects of any predictive model: reliability (it behaves the same way under similar conditions) and interpretability (humans can understand the model). These characteristics are especially important when assessing treated wastewater for reuse in agriculture [27]. The potential of these models to optimize the processes of treating wastewater, cut costs, and comply with environmental regulations has been shown. They provide real-time monitoring and even predictive analyses [53].  
Techniques of artificial intelligence have been embedded within digital twins and based on the Internet of Things to create even smarter and more responsive wastewater management systems that allow real-time data analysis and decision-making [7; 53].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DL Technique** & **Advantages** & **Disadvantages** & **Applications** \\ \hline \multirow{6}{*}{HDL} & High accuracy & High computational cost & WWTPs operational efficiency \\ \cline{2-4}  & Adaptable to nonlinear data & Overfitting risk & Anomaly detection \\ \cline{2-4}  & Strong optimization and & & Sensor-based malfunction \\  & fine-tuning & & detection \\ \cline{2-4}  & Robustness & Data quality dependent & Environmental and treatment \\  & & & process optimization \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Even with these advancements, difficulties persist, such as the necessity for extensive datasets to train models successfully and the sophistication involved in melding AI systems with current wastewater infrastructures [7, 27].  
Current applications are much simpler and not as reliable as would be needed for an AI model that could fully and accurately do the job of controlling a modern plant in real time [27, 80].
## 1 Introduction
Unfortunately, in order to meet energy efficiency requirements in directional high gain communications, it is not sufficient to consider to limit the most important source of waste of energy that is related to hardware resources, because issues introduced by directional communications are prone to degrade the battery life duration of mobile nodes and hardware operations [20, 21]; in particular, in sectorized antennas environments, it should be advisable to control the maximum number of communications performed by nodes or to reduce beamforming time, especially in data traffic exchange [22]. In view of these considerations, in the present paper, we propose addressing the energy consumption issue by enhancing the works proposed in [23, 24] using SASs along with a round-robin scheduling. The main purpose of the work is to limit the excessive energy consumption in the network derived by the queue and the time slice problems involved by the use of the round-robin. The other goal is to demonstrate how, using a mechanism enhancing the channel resources exploitation, could lead to MAC performance improvement especially in terms of packet delivery ratio (PDR).
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 1 Introduction
However, these reported results are based on ideal simulation environments and do not reflect the constraints of realistic IoT systems in terms of memory and processing power. In this work, we examine the realistic gains and reveal hidden challenges of leading AIS methods by implementing these in actual IoT systems with differing characteristics. Our aim is to delve into the implementation implications of an AIS within a realistic IoT system, shedding light on its practical applications and challenges. While prior work validates IoT security methods at a device level using field-programmable gate array (FPGA) or a Raspberry Pi, such as [10, 11], this article aims to validate the AIS methods at a system level with multiple devices. For this reason, we utilize Amazon Web Services (AWS) to simulate realistic IoT system scenarios.
## 6 Time Frequency Transforms
### Spectrogram  
The spectrogram is the squared magnitude of the continuous-time Short Time-Frequency Transform (STFT), which can be defined in the following equation for a signal x(t):  
\[Spectrogram(t,\omega)=\left|\int_{-\infty}^{+\infty}x(t)\omega(t-\tau)e^{-j \omega t}dt\right|^{2} \tag{2}\]  
where \(\omega(t)\) is a window function (the Hahn function was used in this study). Considering that function of STFT is the function of Fourier transform multiple by the window function, the STFT can be also called windowed Fourier transform or time-dependent Fourier transform. The size of the window is a hyper-parameter in the definition of the spectrogram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameter** & **Values Range** &
\begin{tabular}{c} **Optimal Values for CWT** \\ **Sombrero Wavelet** \\ \end{tabular} \\ \hline CNN Solver algorithm & (Adam, RMSProp, SGDM) & Adam \\ \hline Initial Learning Rate & (0.001, 0.005, 0.01, 0.05) & 0.001 \\ \hline Filter Size of first & (10, 20, 30) & 10 \\ Convolutional Layer & & \\ \hline Number of & (2, 3, 4) & 3 \\ Convolutional Layers & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the hyper-parameters used for the optimization of CNN and the related optimization range. An example of the optimal identified values for the CWT Sombrero wavelet is shown.  
The spectrogram has been used in this study because it was already adopted in the related literature in [15] and it also commonly used together with CNN in other wireless communication problems such as RF fingerprinting in [18; 25].
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 2 Model Order Selection
### Information Criteria
#### 2.1.1 General Approach  
Let the observed data be in the form \(X=P_{k}(\theta_{k})+\mathcal{E}\in\mathbb{R}^{N}\), where \(P_{k}(\theta_{k})\) is a model with the number of parameters \(k\), \(\mathcal{E}\) is a random noise with zero expectation.  
We can write out the general form of the information criterion (abbreviated as IC in formulas and algorithms) as follows [27]:  
\[\mathrm{IC}(k)=2\ln\mathcal{L}_{k}-\phi(k), \tag{3}\]  
here the maximum value of the likelihood function within the model \(P_{k}\) is denoted by \(\mathcal{L}_{k};k\) is the number of parameters in the model; and the penalty for the number of parameters is represented by the function \(\phi(k)\). In this form of criteria, the optimal model is indicated by the maximum value.  
If the elements \(\varepsilon_{i}\), \(i=1,\ldots,N\), of the noise \(\mathcal{E}\) are independent and \(\varepsilon_{i}\sim N(0,\sigma^{2})\), then instead of searching for the maximum of (3), it is possible to search for the maximum in \(k\) of  
\[\mathrm{IC}(k)=-N\log(\hat{\sigma}^{2})-\phi(k), \tag{4}\]  
where  
\[\hat{\sigma}^{2}=\hat{\sigma}_{k}^{2}=\min_{\theta}\|\mathrm{X}-P_{k}(\theta) \|^{2}.\]  
Different information criteria are distinguished by the penalties that are associated with them. For Akaike information criterion (AIC), \(\phi(k)=2k\)[28] and for Bayesian information criterion (BIC) [29], \(\phi(k)=k\log N\).  
Another criterion is the Maximum Description Length criterion (MDL [30]), where the penalty additionally depends on the particular model. In the case of independent noise, the MDL criterion is asymptotically equivalent to the BIC criterion as \(N\to\infty\). However, the penalty for the number of parameters differs for finite \(N\).  
The following section will present the application of this approach to the model order selection problem for time series, specifically in the context of a low-rank signal.
# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 3 Materials and Methods
### Performance Evaluation Metrics  
The pipeline introduced in this study was evaluated in two stages. In the first stage, the lesion location detection performance of re-trained Yolov3 in skin lesion images, was evaluated. In order to obtain more accurate segmentation results, the location information of the lesion in the image is very critical. Therefore, the detection performance of model was evaluated by using IOU metric. The detected lesion location was regarded as true if the IOU score was greater than 80%. In the second stage, the following performance metrics to further evaluate our method were used: sensitivity (Sen), specificity (Spe), the Dice coefficient (Dic), the Jaccard index (Jac) and accuracy (Acc). Sen represents the amount of correctly segmented lesion pixels while Spe shows the correctly segmented non-lesion areas ratio [53; 56]. Dic is an evaluation metric which is used to measure segmented lesions and annotated ground truth similarity [56; 74]. Jac is an evaluation metric for the intersection ratio between the obtained segmentation results and ground truths masks [75]. The main difference between Jac and IOU is that Jac is used for the segmentation and segmentation boundaries can be irregular, but IOU is used for the localization and it uses rectangular boundaries. Finally, accuracy shows the overall pixel-wise segmentation performance [56]. All aforementioned evaluation metrics are calculated by the following formulas:  
\[IOU=\frac{\text{Area of Overlap}}{\text{Area of Union}} \tag{9}\]  
\[\text{Sen}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
\[\text{Spe}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{11}\]  
\[\text{Dic}=\frac{2*\text{TP}}{(2*\text{TP})+\text{FP}+\text{FN}} \tag{12}\]  
\[\text{Jac}=\frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP}} \tag{13}\]  
\[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{TP}} \tag{14}\]  
The _TP_, _FP_, _FN_, and _TN_ symbolize the true positive, false positive, false negative and true negative, respectively. The lesion pixels in the image are considered as _TPs_ if they are segmented correctly; otherwise, they are regarded as _FNs_. On the contrary, the non-lesion pixels in the image are considered as _TNs_ if their prediction is non-lesion pixel; otherwise, they are regarded as _FPs_.
## 4 Simulation Analysis Results and Discussion
### Heat Production and Heat Dissipation Pattern during Braking  
During the braking process of high-speed trains, the ventilated brake disc constantly generates heat in the contact friction zone, and at the same time, the surface of its structure is affected by turbulent air, which dissipates the heat of the disc into the air continuously, as shown in Figure 19. The ventilated brake disc in the braking process generates and dissipates heat with time-change curve. From the figure, it can be seen in the braking period that its heat production rises more violently; Figure 8 shows that in the early stage of braking, the temperature rise more apparent; this is due to the high-speed train's initial speed being higher. It can be seen from Formula (15) and Formula (16) that more heat is generated by the friction between the ventilated brake disc and the brake pad in unit time, which makes the energy of the ventilated brake disc rise faster As the braking time increases, the heat generated by friction decreases due to the decreasing speed of the high-speed train, and the rate of growth of the energy of the ventilated disc decreases. The air around the ventilated disc flows differently and carries away the energy transferred from its interior to the surface. Through convection heat dissipation as well as thermal radiation and dissipation into the air, and from the graph of the change curve, the difference between heat production and heat dissipation compared to the heat production curve can be seen in the middle and late stages of braking. Heat dissipation has a significant effect on the energy of ventilated brake discs, resulting in the temperature of the ventilated brake disc after 41 s of braking showing a decreasing trend.
# Finite Element Analysis of Geogrid-Incorporated Flexible Pavement with Soft Subgrade
## 2 Research Approach and Numerical Simulation
### Model Validation  
Validating numerical models is crucial for ensuring the accuracy and dependability of computational simulations mirroring real-world scenarios. Therefore, in this study, the development of finite element models undergoes validation against another study [19]. In that particular investigation, both experimental and numerical models were developed to comprehend rutting in asphalt pavements. In this study, the finite element (FE) model is sized at 8 m in length, 6 m in width, and 3.74 m in depth, with all the physical properties and loading conditions of the pavement layers sourced from the referenced study. However, meshing particulars, elemental behaviour, boundary conditions, etc., are based on the current study. The analysis focuses on the pavement's rutting behaviour, with a comparison of results depicted in Figure 3. The alignment between the outcomes of the present study and those from the reference study indicates the consistency of the process used to prepare the numerical or FE model in this study, suggesting its suitability for further analysis of geogrid-reinforced pavements.  
Figure 2: Model mesh and boundary selection.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 3 Results
### Relationship between Forest Stand Age and Canopy Spectral Characteristics  
#### 3.2.1 Relationship between Canopy Reflectance and Forest Stand Age  
Clarifying the decoupling relationship between forest canopy spectral information and forest stand age, especially the linear or nonlinear relationship between forest stand age and canopy spectral parameters, is the basis of remote sensing inversion of stand age. In this study, the Pearson correlation coefficient was used to analyze the relationship between the reflectance of the B\({}_{11}\) bands of remote sensing data and forest stand age, as shown in Table 2. The results show that there is a significant correlation between forest stand age and canopy spectral reflectance.  
Figure 4: Spectral reflectance characteristics of forest canopy at different ages ((**a**) spectral characteristics at different forest stand ages, (**b**) variation pattern of _NIR_ with forest stand age, (**c**) variation pattern of red edge (\(B_{5}\)) with forest stand age, (**d**) variation pattern of red edge (\(B_{8A}\)) with forest stand age).  
Forest stand age and canopy near-infrared (\(B_{8}\)) spectral reflectance and red edge (\(B_{8A}\)) reflectance were the most significant, with \(r\) values of 0.53 and 0.39, respectively. This shows that forest stand age significantly affects the spectral reflectance of the canopy. The reflectance of the canopy is a comprehensive reflection of the canopy structure and the photosynthetic capacity of forest leaves. These results indicate that the variation in canopy structure and photosynthetic capacity with forest stand age can be captured by remote sensing data.
## 2 Design Details of the Proposed System
### Hardware Details  
1. Acoustic sensor: Since the frequency range of sound audible to the human ear is 20-20 kHz and there is a large background noise in the outdoor environment, we prefer to select the sensor  
Figure 1: The overview of the proposed system.  
from the two aspects of frequency response and noise reduction. The sensor has the frequency range of 20-20 kHz and the maximum monitoring area of 80 square meters. The sensor has an amplification circuit, filter circuit, and noise reduction circuit inside, which can effectively filter out background noise.
2. Embedded processing board: Based on the low cost and the slow power consumption, we chose the extra processing board with a rich audio interface and high-performance audio encoder to improve audio quality. The board is paired with the A33 processor which uses a quad-core CPU (CortexTM-A7: Advanced RISC Machines, Cambridge, UK) and a GPU (Mali-400 MP2: ARM Norway, Trondheim, Norway) at frequencies up to 1.2 GHz and has 1 GB DRAM and 8 GB FLASH. In addition, it allows users to expand the storage space by using SD cards.
3. Wireless transmission module: Adopting the 3 GPP Rel-11 LTE technology, the Quectel-EC20 4 G module delivers 150 Mbps downlink and 50 Mbps uplink data rates. A rich set of Internet protocols, industry-standard interfaces, and abundant functionalities extend the applicability of the module to a wide range. The module also combines high-speed wireless connectivity with an embedded multi-constellation high-sensitivity GPS + GLONASS receiver for positioning.
4. Solar panel: Since our system is used for the long-term outdoor monitoring, we chose the solar panel with a battery and a power controller inside. During the day, the solar panel absorbs energy to charge the battery, and supplies power to the embedded processing board at the same time. It is only used to power the processing board at night. In addition, the solar panel we chose is embedded with a 5 V and 10,500 mAh battery, which allows the processing board to work continuously about two days without sunshine.  
Table 1 shows the hardware costs.
# Adaptive Clustering of Users in Power Domain NOMA
## 3 Global Fading Coefficient  
The proposed clustering method uses information from the users' channel state information to define the formation and evolution of clusters. In this work, the path gain associated with each user channel is defined in terms of a relative global fading among network users, which considers the small-scale and large-scale fading as defined by:  
\[v=\sqrt{PL_{d}-PL_{d_{0}}}\,h, \tag{4}\]  
Figure 1: NOMA downlink example formed by one base station and M users.  
where \(h\) is the small-scale fading, and \(PL_{d}\) is the path loss to a distance \(d\) from the base station, defined by the COST 231 Hata model as follows:  
\[\begin{array}{c}PL_{d}=-46.3-33.9\log_{10}(f_{c})+13.82\log_{10}(h_{t})+\alpha (f_{c},h_{r})\\ -44.9+6.55\log_{10}(h_{t})\log_{10}(d),\end{array} \tag{5}\]  
where \(d\) is the distance between the base station and the user's equipment in kilometers, \(f_{c}\) is the carrier frequency in MHz, and \(h_{t}\) and \(h_{r}\) are the base station antenna and mobile station antenna effective height in meters, respectively. \(\alpha(f_{c},h_{r})\) is the mobile station antenna height correction factor for suburban or rural environments, defined by:  
\[\begin{array}{c}\alpha(f_{c},h_{r})=[1.1\log_{10}(f_{c})-0.7]h_{r}\\ -[1.56\log_{10}(f_{c})-0.8].\end{array} \tag{6}\]  
The small-scale fading, \(h\), is defined by a time-varying complex Gaussian process generated by the Monte Carlo method, which has Rayleigh distribution in magnitude and uniform distribution in phase and power spectrum density as defined by [31]:  
\[\begin{array}{c}H(f)=\begin{cases}\frac{1}{\sqrt{1-(\frac{f}{f_{D}})^{2}}},& \text{if }|f|<f_{D}\\ 0,&\text{if }|f|\geq f_{D}\end{cases}\end{array} \tag{7}\]  
where \(f_{D}\) is the maximum Doppler shift.  
The clustering algorithm proposed here considers the relative global fading among network users to define the clusters over time. Therefore, the path loss of each user in (4) is calculated relative to a common reference distance from the base station, \(d_{0}\).
## 5 Experiments
### Experiment 4: Static Splitting Factors with Stopping Criteria
#### 5.4.3 Connections Saved Criterion
Notably, applying stopping criteria based on connections saved percentage, such as preserving at least 80% of connections, yielded substantial reductions in both time and resource requirements compared to the rest of this Section's experiments. On the flip side, saving 80% translates as 19%, 15%, and 10% sacrifice of the benign connections, respectively, for 1%, 5%, and 10% of pollution factors. At the same time, the application of time or resource constraints achieves better success rates but at the expense of either heightened time or resources. Overall, in real-time scenarios, adjusting the criteria based on the system's urgency for recovery and its available resources can lead to acceptable success rates, even with static splitting factors. However, more sophisticated schemes may better balance the time and resources.