esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Deep-Learning-Based Methods  
A review of existing traffic identification methods reveals that traditional port- and protocol-based, statistical-feature-based, and machine-learning-based methods have significant limitations in encrypted traffic identification.  
Deep-learning-based approaches [12] have already achieved significant advantages in the area of traffic classification. [PERSON] et al. [13] first proposed a scheme using a convolutional neural network (CNN) to extract encrypted traffic features, and its classification performance is observably better than that of traditional machine learning methods. [PERSON] et al. [14] converted raw bytes of encrypted flow into images and encoded them using a masked autoencoder [15] (MAE), capturing traffic patterns through the reconstruction. [PERSON] et al. [16] dug out the deep sequence characteristics of traffic through an in-depth analysis of the potential characteristics of traffic and proposed a multi-layer flow codec structure. [PERSON] et al. [17] introduced an attention mechanism while extracting the time series characteristics of traffic through recurrent neural networks. [PERSON] et al. [18] transformed raw bytes of traffic into text tokens, employing models based on Transformer architectures [19].  
Since data encryption does not alter the overall structure of the data flow, even after encryption, network traffic remains a sequence of data with a start and end point. Therefore, convolutional neural network (CNN) models remain effective for classifying encrypted traffic. However, encryption can scramble request information in certain parts of the traffic protocol, making it difficult to identify features across different data segments. Thus, this study considers incorporating the recognition of temporal features in encrypted trafficto improve traditional deep learning models. In this domain, recurrent neural networks (RNNs) and their improved versions, such as BiLSTM, have shown better performance. Therefore, this article combines the spatial features extracted by CNN with the time features extracted by BiLSTM to learn the flow features.
## 2 Simulation and Experiments Methods  
### Design and Optimization of CMA  
To investigate the optical effects of CMA, optical phenomena of reflection and refraction of the sinusoidal microlens array were analyzed using the ray-tracing software ZEMAX(r). Figure 1 illustrates the schematic of the optical CMA homogenizer constructed in ZEMAX(r) software. The microlens array beam homogenizer usually contains two lens arrays and a condenser lens, which will have much greater flat-top uniformity. The incident beam cross-section is split into an array of beams by a sub-microlens array. Both beamlets then pass through the condenser lens, converge, and overlap at the same location. With the involvement of the second microlens array, every microlens, in conjunction with the condenser lens, may have a field lens effect. The lens array can produce overlapping pictures on the illumination plane of different kinds of fields of the light source. Due to the field lens effect, rays in all directions can be refracted to the desired location of illumination. Since for the beam generation mechanism the pattern of the excimer laser beam profile is rendered as an irregular shape, also accompanied by complex spatial energy distribution, the image framework as a design infrastructure is a good choice [28].  
Figure 1: Optical schematic illustration of optical CMA homogenizer.  
It should be noted that general imaging homogenizers are often two similar microlens arrays parted by the focal length, but this is not a feasible solution for the excimer laser. Due to the high average strength of the excimer laser beam in the UV to DUV region, the light can be easily absorbed by materials. The second microlens array can be connected by the concentrating energy of the first microwave array using only two identical microlens arrays configuration.  
After optimizing all parameters of the optical system, an ideal distribution of light energy intensity was obtained on the optical detector in the mean beam plane. The shaping beam effect is shown in Figure 2. The corresponding parameters are shown in Table 2. The light intensity distribution of this point was analyzed through the spot coordinates in the detector to achieve an ideal degree of uniformity. The undulating state of the light spot energy was considerably reduced, and the optimized light intensity distribution curve is presented in Figure 2a. As shown in Figure 2b,c, the spatial distribution of the beam energy with a flat top is ideal. The calculated light intensity of the flat-top is 0.80; the light intensity difference is within 12%, that is, the relative light intensity is 88% to 100%; and the light intensity error is lower than 6%. Furthermore, the highly consistent beam profile with a huge scale is generated at a very close distance.
###### Abstract  
Vs30, the average shear wave velocity in the uppermost 30 m, is a critical parameter in seismic hazard analysis. In the Philippines, the Refraction Microtremor (ReMi) survey is the standard method for Vs30 Estimation. This study evaluates the efficiency of using an elitist Genetic Algorithm (GA) to invert Horizontal-to-Vertical Spectral Ratio (HVSR) data as an alternative approach. Unlike ReMi surveys, which require geophone arrays, HVSR surveys use a single-unit three-component microtremor seismograph, enabling faster and broader data collection. Analysis of 174 HVSR and 52 ReMi datasets from the Greater Metro Manila Area (GMMA) and Leyte Province revealed strong correlations between estimated and measured Vs30 values. The overall match rates for soil profile classification under the National Structural Code of the Philippines (NSCP 2015) were 76% in GMMA and 81% in Leyte, with R-squared values of 0.885 and 0.806, respectively. Additionally, the relationship between the fundamental site period and estimated Vs30 values was explored. The R-squared values of 0.772 for GMMA and 0.707 for Leyte indicate a strong correlation and demonstrate the expected inverse relationship between the two variables. Given the Philippines' high seismic activity, this method provides an efficient means to enhance seismic hazard mapping, improving earthquake preparedness and mitigation.  
HVSR; Vs30; NSCP 2015; refraction microtremor; genetic algorithm; seismic hazard analysis Article
## 1 Introduction  
With the trend of ageing, the demand for medical devices continues to rise all over the world. The market value has reached 336.1 million USD and the demand for dental implants has increased significantly by the report of BMI in 2016 [1]. According to an estimation by Merrill Lynch, the market compound growth rate of artificial implants has been 17.7% since 2001. In Taiwan, dental technicians are responsible for making prostheses for patients. Therefore, how to reduce the problem of low production efficiency caused by human factors is always the goal of every dental laboratory [2]. In recent years, the Taiwan government has continuously promoted the integration of wireless networks, automated production, and process management intelligence through the policy of productivity 4.0 [3]. Much research has discussed the application of \"Productivity 4.0\" or \"Industry 4.0\" in Taiwan, but most of them have only discussed the potential benefits of and impacts on business management or production lines after assisting companies to apply this policy [4; 5; 6; 7].  
[PERSON] et al. combined two different wireless communication protocols, ZigBee and Wi-Fi, on the same system and used Arduino to integrate with them to achieve the effect. The system can remotely monitor the changes of temperature and humidity in home without performing it through a computer It can effectively reduce the cost of the existing remote monitoring equipment and select appropriate communication protocols for data transmissions based on different sensing signals [8]. [PERSON] et al. used RFID technology and cloud architecture to strengthen the management system of logistics service. The idea can effectively reduce costs and assist manager tracking [9]. [PERSON] et al. combined RFID and expert systems to develop the material management system and material forecasting mechanism for a dental laboratory [10]. With the maturity of AI technology, how to introduce intelligent management of denture manufacturing to improve the competitiveness of a dental laboratory is an important research direction in the future.  
In this study, an Industry 4.0 framework-based decision support system (DSS) has been developed using a combination of wireless network and RFID identification technology to manage a production line in a traditional dental manufacturing lab. DSS has been widely used for production line management, for example, [PERSON] and [PERSON] [11] applied the DSS model to manage the network traffic within the users and organizations using linguistic information, which recommends priorities for suppliers. Recommendations and decisions can be processed by rating a database through a decision tree in models [12]. Our primary motivation is to prevent mistakes from manual recording, to remotely monitor the working hours of employees in manufacturing processes via internet, and to evaluate the rationality of employee's working hours. Further, the system is able to evaluate the rationality of employees' working hours and recommend managers a more efficient process.
## 5 Experimentation and Results
### Comparative Study
#### 5.4.1 Comparative Analysis between Different Representations
On the other hand, flexible representation (using WrapperMI) entails that algorithms obtain better values for sensitivity (predicting students that pass the course) at the expense of obtaining worse values for specificity (predicting students that do not pass the course). The fact of this off-balance between these measures is traduced in worse predictions overall. Again, flexible representation (using SimpleMI) obtains the most balanced results for both measures, sensitivity and specificity. Thus, this representation gets the best value or a very close one, achieving the best accuracy. To analyze final results and show if there is significant differences between the behavior of algorithms using different representations, it is applied the test of Wilcoxon signed-ranks ([PERSON] and others, 2019). Thus, a pairwise comparison is carried out facing representation based on single instance (traditional) and the two MIL-based representations (SimpleMI, MIWrapper). Table 13 shows the results of the tests attending to accuracy measure. It is shown the \(R^{+}\), \(R^{-}\) and \(p\)-values. With a confidence level of 99%, SimpleMI shows an improvement over the other representations. With a confidence level of 95%, MIWrapper shows an improvement over traditional representation. Analyzing the differences in sensitivity, Table 14 shows a similar tendency: both flexible representations significantly outperform with a confidence level of 99% to traditional representation. However, attending to specificity results in Table 15, it can be appreciated that MIWrapper has difficulties to distinguish the negative class, which leads to a bad performance compared to SimpleMI and traditional representation. However, SimpleMI does not have this problem, reaching the best results in this metric too.
# Fast 3D Semantic Mapping in Road Scenes +
## 2 Related Work
[32] introduce a regularization framework to obtain spatially smooth semantic labeling of 3D point clouds from a point-wise classification, considering the uncertainty associated with each label. Gaussian Process (GP) is another popular method for map inference. [PERSON] et al. [33] exploit GP to learn the structural and semantic correlation between map points. This technique also incorporates OcotoMap to handle sparse measurements and missing labels. In order to improve the training and query time complexities of the GP-based semantic mapping, [PERSON] et al. [34] further introduce a Relevance Vector Machine (RVM) inference technique for efficient map query at any resolution. Our semi-dense approach is also inspired by dense 3D semantic mapping methods [8; 9; 35; 36] in both indoor and outdoor scenes. The major contributions from these work involve the 2D-3D transfer and the map regularization. Especially, [PERSON] et al. [9] propose an efficient 3D CRF to regularize 3D semantic mapping consistently considering influence between neighbors of 3D points (voxels). In this work, we adopt a similar strategy to improve the performance of the 3D semantic reconstruction in the road scenes. The key concepts are  
* a 3D semantic mapping system based on monocular vision,
* integration of monocular SLAM [21] and scene parsing [7] into 3D semantic representation,
* exploiting the correlation between semantic information and geometrical information to enforce spatial consistency,
* active sequence downsampling and sparse semantic segmentation so that to achieve a real-time performance and reduce the storage. Following the comparison in [30], we list the characteristics of our approach and some related works in Table 1.
# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 3 Materials and Methods
### Feature Extraction
#### 3.2.1 Prosodic Features  
Prosodic features are acoustic features prominently used in emotion recognition and speech signal processing because they carry essential paralinguistic information. This information complements a message with an intention that can paint a flawless picture about attitude or emotion [35; 56]. In addition, prosodic features are considered as suprasegmental information because they help in defining and structuring the flow of speech [35]. Prosodic continuous speech features such as pitch and energy convey much content of emotions in speech [57] and are important for delivering the emotional cues of the speakers. These features include formant, timing and articulation features and they characterize the perceptual properties of speech typically used by human beings to perform different speech tasks [57].  
The present authors have included three important prosodic features of energy, fundamental frequency and ZCR. Signal energy models the voice intensity, volume or loudness and reflects the pause and ascent of the voice signal. It is often associated with the human respiratory system and is one of the most important characteristics of human aural perception. The logarithm function is often used to reflect minor changes of energy because the energy of an audio signal is influenced by the recording conditions. Fundamental frequency provides tonal plus rhythmic characteristics of a speech and carries useful information about the speaker. ZCR determines the information about the number of times a signal waveform crosses the zero amplitude line because of a transition from a positive/negative value to a negative/positive value in a given time. It is suitable for detecting voice activity, end point, voiced sound segment, unvoiced sound segment, silent segment and approximating the measure of noisiness in speech [58]. ZCR is an acoustic feature that has been classified as a prosodic feature [49; 59; 60]. In particular, energy and pitch were declared prosody features with a low frequency domain while ZCR and formants are high frequency features [60].
## 2 Materials and Methods  
### Samples Collection and Preparation  
A post-mortem study was performed on autopsy samples of human CC retrieved at the Department of Biomedical Sciences and Public Health, Legal Medicine Unit, Universita Politecnica delle Marche. Human autopsy brains were obtained from 11 adult males who died of sudden death (myocardial infarction, traffic accident, etc.) in the period September 2021-February 2022. Subjects were divided into two groups according to their age at death: 6 donors with an average age of 30.7 \(\pm\) 5.7 years (young, named **Y**), and 5 donors with an average age of 69.6 \(\pm\) 8.5 years (old, named **O**). The study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 2013, and approved by the Ethics Committee of Universita Politecnica delle Marche (protocol code 2019-373, date of approval 1 October 2020). Furthermore, all samples were rigorously anonymous and identified only using a numeric code.  
CC samples were collected from both hemispheres, within a postmortem interval (PMI, the time between death and removal of the brain tissue from the cranial cavity) of 24-96 h, including the refrigeration of the corpses after their discovery. More in detail, the interhemispheric fissure was exposed and gently retracted. The CC was extracted and three cubic portions (ca. 0.5 \(\times\) 0.5 \(\times\) 0.5 cm) were collected from the areas corresponding to the genu (named **A**), body (named **B**), and splenium (named **C**) [37]. Samples with visible signs of decomposition were excluded. All samples were immediately frozen at \(-\)80 \({}^{\circ}\)C.  
From each cubic portion, six sections (\(\sim\)10 \(\upmu\)m thick) were cut at \(\sim\)150 \(\upmu\)m away from each other using a cryomicrotome. Sections were alternatively deposited onto CaF\({}_{2}\) optical windows (1 mm thick, 13 mm diameter) for FTIRI measurement, and onto glass supports for histological analysis [38].
# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
## 3 Background
### Optimization
This equation plays a central role in the iterative process of Bayesian optimization, where new points are selected based on the acquisition function to iteratively find the optimal configuration. On the other hand, ADAM is an optimization algorithm commonly used in DL to train ML models. It is particularly well suited for optimizing neural networks, which have become fundamental to many modern AI applications. The core idea behind ADAM is to adapt the learning rates for each parameter during training, which helps the algorithm converge faster and more efficiently. Traditional gradient-based optimization algorithms, such as Stochastic Gradient Descent (SGD), use a fixed learning rate for all parameters. ADAM, on the other hand, maintains a separate adaptive learning rate for each parameter. This adaptive learning rate is based on two key moving averages: the gradients' first moment (the mean) and the second moment (the uncentered variance) [31]. The process begins with a set of parameters, including the learning rate (\(\alpha\)), the exponential decay rates for moment estimates (\(\beta_{1}\) and \(\beta_{2}\)), the objective function to minimize (\(f(\theta)\)), and the initial parameter vector (\(\theta_{0}\)). Initialization: The process commences by initializing two-moment vectors: the first-moment vector, \(m_{0}\), and the second-moment vector, \(v_{0}\), both set to zero. The iteration counter, \(t\), is initialized to zero as well. Iteration: The algorithm iterates until the parameters converge. The algorithm progresses iteratively until the parameters converge. In each iteration, the counter increments (\(t\gets t+1\)), the objective function's gradient (\(\
abla_{\theta}f_{t}(\theta_{t-1})\)) computes based on the current parameter vector, and the first-moment estimate (\(m_{t}\)) updates through an exponential moving average with \(\beta_{1}\). Similarly, the second-moment estimate (\(v_{t}\)) updates by using \(\beta_{2}\), incorporating the square of the gradient. Bias-corrected estimates, \(\hat{m}_{t}\) and \(\hat{v}_{t}\), adjust for the exponential moving average and iteration number for \(m_{t}\) and \(v_{t}\), respectively. The parameter vector \(\theta_{t}\) updates by using these moments to determine step sizes, scaled by \(\alpha\) and adjusted for numerical stability with a small constant \(\epsilon\). Convergence: The algorithm continues these steps until the parameters converge, returning the optimal parameter vector \(\theta_{t}\). The mathematical equations for the ADAM optimizer can be summarized as follows:  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{8}\]  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{9}\]  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{10}\]  
\[\hat{v}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}} \tag{11}\]  
\[\theta_{t}=\theta_{t-1}-\alpha\frac{\hat{m}_{t}}{\sqrt{\hat{v}_{t}+\epsilon}} \tag{12}\]  
These equations capture the essence of the ADAM optimizer's behavior and its ability to efficiently update the parameters during the training of deep neural networks.
## 4 Results  
### 118 Ghz  
The temperature retrieval errors induced by instrument uncertainties are shown in Figure 5. The error induced by local oscillator offset (LO) is around 5 K above 60 km, since the line width is quite narrow in the upper atmosphere. Sideband weight causes an error of 1 K in the stratosphere. This is because of the large brightness temperature difference between the lower sideband (18 K at 30 km) and the upper sideband (250 K at 30 km). NEDT is the mainly considered error in normal retrieval which cannot be alleviated through calibration. It leads to an error of less than 2 K. The effect of pointing error (LOS) is less than 0.5 K above 15 km, because it can be retrieved simultaneously with temperature.  
The antenna pattern plays an important role in the 118 GHz observation. The brightness temperature difference \(T_{A}-T_{B}\) at various tangent heights is shown in Figure 6. The impact of the 118 GHz antenna pattern on the measurement can be as large as 15 K at the tangent height of 20 km in the spectral line wings, since the temperature profile varies rapidly in this region. Although the difference will decrease with the tangent height increasing, the offset is still larger than 2.5 K at 90 km. This is due to the large FOV (5.5 km) of the 118 GHz antenna. The impact of the antenna pattern width error on the retrieval errors is presented below.  
Figure 7 shows the temperature retrieval errors induced by different calibration uncertainties. The temperature retrieval error induced by the antenna pattern can be as large as 20 K below 25 km and less than 1 K in the middle atmosphere. The error due to the R-J approximation is about 1-2 K in this frequency band. The hot target offset causes an error of 0.7 K above 15 km.  
The most nonlinear effects of calibration can be corrected in the processing of the radiometer calibration by using Equation (4). Although there exists a residual of about 0.3 K (STD), its impact on temperature retrieval is relatively small.  
The temperature retrieval errors induced by the a priori errors are shown in Figure 8. The effect of pressure uncertainty is less than 0.5 K, since it can be retrieved with temperature simultaneously through the hydrostatic equilibrium equation. The a priori profile mainly affects the region where the measurement information lacked, such as the troposphere and upper mesosphere. The error is larger than 1 K above 85 km.  
Figure 5: The temperature retrieval errors induced by different instrument uncertainty sources: (**a**) absolute and (**b**) relative retrieval errors. The uncertainties assumed are summarized in Table 3.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Technologies
#### 3.1.1 Ethernet  
Ethernet is a type of wire used in computer networking. It enables the sensor devices to transfer data via cable wires. It is also used in IoMT for connecting IoT devices and monitoring patient data in real time. Ethernet connection is reliable, high-speed, and secure. The normal speed of Ethernet is 10 Mbps.