esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Description  
### Hardware  
Figure 6 represents the hardware block diagram of the proposed measurement system. The hardware includes a piezoresistive gauge pressure transducer [31] that is used in a wide range of applications, namely biomedical applications, such as NIBP measurement. One specific feature of the pressure transducer is its temperature compensation capability in the temperature range between \(-40\)\({}^{\circ}\)C and \(+125\)\({}^{\circ}\)C, this being the compensation important to minimize errors caused by temperature variations. The signal conditioning circuit (SC) of the oscillometric pulse signal has an automated gain control (AGC) feature implemented by a digital potentiometer, and this feature can be used to adjust the measurement's sensitivity. The two main electro-pneumatic components in the hardware part of the prototype include an electro-pneumatic pressure regulator (EPPR), which is used to generate pressure signals for static and dynamic calibration purposes [32], and a release valve [33], which is mainly used to depressurize the cuff after the deflation BP measurement phase. Data processing, control and transmission interface capabilities are provided by a microcontroller [34] that includes an 8-bit microprocessor and a wireless transmission interface that can give access to a Wi-Fi network. To perform the calibration of the pressure sensor and SC circuits, the following components are used: a pressure gauge [35]; a digital multimeter [36]; a data acquisition board [37] and a pressure calibrator [38]. Table 1 summarizes the specifications of the main electrical components and devices that were used. Figure 5: Parabolic pulse envelope to be used in the evaluation of the systolic and diastolic pressures when using the maximum slope algorithm. Figure 7 represents the block diagram of a multiple pressure sensor calibration platform that was developed to calibrate, simultaneously and in a comparative mode, a set of four different pressure sensors. It is also important to underline that the calibration platform includes a pneumatic line that can have different lengths and diameters, with it being possible to calibrate the entire pressure measuring chain and not only the pressure sensor. This feature can be very important since the accuracy of the ABPMD measurements is affected not only by the pneumatic tube cuff position but also by its length and diameter [39].
## 5 Evaluation of the Security Aspects of the Designed Protocol
### Selective Forwarding Attack  
This attack, which involves selectively capturing and resending packets in the RPL routing protocol, is a common threat. This type of attack can disrupt network communication and potentially compromise its integrity. To mitigate such attacks, a robust authentication mechanism is essential to thwart the intrusion of malicious nodes into the network. As stated previously, the recommended protocol incorporates an optimal authentication system into its process, leveraging parameters such as S\({}_{1}\), A, S\({}_{2}\), S\({}_{3}\), SA, N\({}_{\text{i}}\), and AC\({}_{\text{i}}\). However, there may be unique circumstances where the hostile person in the network chance to transmit messages through the network with the intention of executinga selective transmission attack. The presented protocol has several security measures in place to counteract this threat:  
1. **Message Freshness:** At each step of the protocol, there is a rigorous check for the freshness of messages. Verifying the currency of messages ensures that either the node or the server can detect messages from earlier, duplicated, or expired sessions, leading to the disconnection of the message recipient.
2. **Random Numbers Utilization:** In the context of implementing the security features of our protocols, the utilization of random numbers is considered as the basic component in the protocol process. Random numbers ensure the absolute uniqueness of generated values, making it challenging for network attackers to execute selective transmission attacks. Consequently, the presented protocol has improved its flexibility against selective forward attacks by taking advantage of random number features as well as the security feature of checking draft message freshness during the protocol process.
# Optimizing Activation Function for Parameter Reduction in CNNs on CIFAR-10 and CINIC-10
## 1 Introduction  
Image recognition is an important mechanism for modern technology nowadays and plays an essential role in several industries. In medicine, it is used to diagnose diseases. In transportation, it is used to recognize road signs and pedestrians in autonomous vehicles--this technology has been the basis of many innovations. Furthermore, in security and surveillance, image recognition algorithms are critical for biometric systems such as facial and fingerprint recognition, as well as for real-time monitoring and identification of potential threats. It is important for these algorithms to be 'lightweight' to function efficiently on devices with limited resources. Although architectures such as AlexNet [1], Inception [2], and ResNet [3] demonstrate high accuracy and performance, this is often achieved at the cost of millions of hyperparameters and significant computational resources. These numbers can be reduced significantly. However, parameter reduction often requires more training cycles to achieve similar results, which presents a challenge for researchers. The goal of this study is to propose a novel approach to convolutional neural network (CNN) architecture that combines high performance with minimal resources. This research also presents a new activation function--Exponential Partial Unit (EPU). The EPU function not only improves accuracy, but also provides faster convergence of the loss function while reducing the risk of overfitting even after 100 or more iterations. Moreover, the runtime remains relatively uniform compared to traditional activations. By presenting an efficient network with a reduced number of parameters and using CIFAR-10 as a benchmark, this research aims to demonstrate how reliable performance can be achieved with limitedresources, opening new opportunities for deployment in low-power consumption devices and real-world applications.
## 3 Related Work
### Literature Review on Retina Disorder Classification Using OCT
The first is used to differentiate AMD class from normal class and the second CNN model is used to divide the AMD class into those with exudates and without exudates using transfer learning. The first CNN achieved an accuracy of 99% and the second one achieved an accuracy of 93.9%. Despite that the models attained high accuracies, the classification was limited to two classes which simplifies the problem. The work of [PERSON] et al. [4] established a diagnostic tool based on a deep learning framework for the screening of patients with common treatable blinding retinal diseases. They applied their approach on an OCT dataset [25] with four classes which are DME, CNV, DRUSEN, NORMAL. They used transfer learning inception model which achieved 96.1% average accuracy. Their approach demonstrated performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema. Using the same dataset, [PERSON] et al. [20] adopted a transfer learning VGG16 network that achieved an accuracy of 98.6%. Both studies applied deep learning approaches [4; 20] to classify four different classes including serious diseases (DME, CNV) and achieved promising results. However, the problem for deep learning models (CNNs) presented in the work of [PERSON] et al. and [PERSON] et al. [4; 20] is that its sensitivity to urgent referrals classes (CNV/DME) is unsatisfactory. This is the main argue point against the approaches in the work of [PERSON] et al. and [PERSON] et al. [4; 20]. Another common concern of deep learning architectures is the limited interpretability of the constructed models. The incorporation of hand-crafted features can provide an adequate solution to deep learning issues. Hand-crafted features can offer complementary information that increase the understandability of the classification decision [26; 27]. In addition, the use of hand-crafted features was shown to considerably improve the performance of pure deep learning approaches [28]. Recently, similar studies for image classification and recognition have shown that the integration of deep and traditional learning yield better performance. Examples of these studies include the work of [PERSON] et al. [29] for face recognition and [PERSON] at al. [30] for lung nodule classification. Moreover, deep learning architectures in return can provide automated localization of the retinal RoI, which overcomes the need for an expert to aid in the specification of the RoI, required for traditional feature extraction. In this paper, we propose a hybrid computer aided diagnosis (HyCAD) system based on OCT images using deep and traditional learning to attain high accuracy classifications and provide relative insight into the classification decision. In addition, HyCAD provides automated RoI localization which helps advance the diagnosis process.
## 3 Interference Mitigation Schemes
### Cognitive Radio Approach
#### 3.3.4 Interference-Aware Management Framework  
As in [40], interference within a hospital is considered in [41]. Cognitive radio is employed in devices attached to the human body, and medical devices in the hospital are designated as the primary users. The interference-aware management framework (IAMF) is introduced as a cognitive framework that consists of the database, measurement, evaluation and update function, strategy function, and internal and external dissemination. The first task detects regions of overlap between WBASNs and calculates the position of mobile cognitive radio nodes. Power control and frequency hopping methods are then implemented in the cognitive radio platform to avoid interference.
# Design and Implementation of DSLMS Algorithm Based Photoelectric Detection of Weak Signals
## 2 Related Work
### Theoretical Analysis of Adaptive Filters
In a boisterous signal environment, too small a value of \(\mu\) slows down the system's adaptation to changes in the signal environment. It prolongs the time for the system to converge to the desired signal. In less noisy signal environments, larger values of \(\mu\) speed up the weight update and cause over-adjustment and oscillations in the system. Such oscillations not only lead to convergence problems of the filter near the global optimum solution but also degrade the system's performance, leading to signal distortion and increased steady-state error.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 3 Fundamentals of Different LAS Measurement Schemes
### Calibration-Free WMS-2f1f Method  
The calibration process needed for the WMS-2f method requires all environmental conditions to remain the same between the calibration stage and the actual measurement stage. Apparently, this luxury of having stable and known conditions is not always available, especially in practical applications where laser transmission fluctuations are much more difficult to predict or monitor. One solution is to normalize the 2f signal with the 1f component, referred to as the calibration-free WMS-2f/1f method.  
The normalization process cancels out the unknown or changing laser intensity. However, to directly associate the measured WMS-2f/1f value with absolute gas conditions, laser intensity and frequency tuning parameters need to be accurately characterized. Typically, this laser characterization process identifies the first- and second-order laser intensity modulation indices (\(i_{1}\), \(i_{2}\)) and intensity-frequency modulation phase shifts (\(\varphi_{1}\), \(\varphi_{2}\)) at a specific center frequency \(\
u_{0}\), which are sufficient to describe the intensity and frequency modulation behavior of a DFB semiconductor laser.  
[PERSON] et al. summarized potential sources of uncertainty in calibration-free WMS, with particular emphasis on the influence of pressure and optical depth in harsh environments [55]. Under such conditions, measurement uncertainties induced by pressure deviation between the simulation and the experimental conditions become more significant, and preference for stronger absorption features voids the optical-thin assumption so that larger measurement errors may result.  
In view of such problems, scanned-wavelength WMS with larger scan ranges and modulation depths may be used. A modified calibration-free WMS scheme with an entirely different laser characterization strategy is proposed to account for the time-variant and wavelength-dependent change of the laser characteristic parameters [16; 58; 59]. The flow chart of this method is shown in Figure 5. This analysis scheme differs from previous WMS strategies in two apparent ways: (1) the use of measured intensity in real time avoids the need for a pre-determined analytic model to describe laser intensity, and can at the same time account for the wavelength-dependent transmission of optical components in the beam path, and (2) using the same data processing procedure for both simulation and measurement introduces equal contributions from any potential non-ideal performance of the lock-in and low-pass filter.  
Figure 4: Flowchart of the 2f-WMS method.  
This method is applicable without constraints on optical depth or modulation index, so that pressure-broadened and blended absorption features can also be studied. However, nonlinear variations in laser characteristics due to calibration drift, temperature variations, and laser aging that add to the residual amplitude modulation (RAM) at different harmonic orders may introduce errors that mostly occur over longer terms.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Iterative Decoding Feedback-Based MP Detector
#### 4.4.2 From the LDPC Decoder to the MP Detector
0: Bit information;
1: When \(n_{out}\) is less than \(N_{out}\), start MP detection until the number of internal loops in the detector reaches \(N_{in,MP}\);
2: Calculate the symbol-level outer information \(M_{e,MP}^{n_{out}}(x_{i}=a_{j})\) of the detector output using Equation (26);
3: Convert \(M_{e,MP}^{n_{out}}(x_{i}=a_{j})\) to bit-level information \(L_{e,MP}^{n_{out}}(c_{i,k}=a_{j,k})\) using Equation (27);
4: After de-interleaving, the \(L_{pr,LDPC}^{n_{out}}(c_{i,k}=a_{j,k})\) is obtained as the prior information for the LDPC decode;
5: Start LDPC decoding until the number of internal loops in the decoder reaches \(N_{in,LDPC}\);
6: Calculate the bit-level LLR information \(L_{e,LDPC}^{n_{out}}(c_{i,k}=a_{j,k})\) output from the LDPC decoder using formula Equation (28);
7: Obtain the bit-level input information \(L_{pr,MP}^{n_{out}}(c_{i,k}=a_{j,k})\) of the MP detector through an interleaver;
8: Using Equation (32), convert the bit level information \(L_{pr,MP}^{n_{out}}(c_{i,k}=a_{j,k})\) into the probability values of each modulation symbol, as the prior probability information \(M_{pr,MP}^{n_{out}}(x_{i}=a_{j})\) input to the MP detector;
9: \(n_{out}=n_{out}+1\), return to step 2 if \(n_{out}\) is less than \(N_{out}\), until \(n_{out}\) reaches \(N_{out}\);
10:return Make a decision to output bit information based on \(L_{e,LDPC}^{N_{out}}(c_{i,k}=a_{j,k})\);
```  
**Algorithm 1** Process of IDF-MP detection algorithm based on LDPC
## 2 Related Work and Methods
### Proposed Method  
This section presents the unsupervised feature selection approach proposed in this study, which integrates an autoencoder with dual-graph regularization. The method embeds dual-graph regularization into a single-layer autoencoder to enable effective data reconstruction while preserving the geometric structures of both the data space and the feature space. This dual preservation guides the process of feature selection. In addition, by incorporating regularization constraints into both the loss function and the weight matrices, the proposed method achieves improved performance and enhanced robustness against noise and outliers.
###### Abstract  
For years, apical microleakage has been considered the main factor in endodontic failure therapy. Sealing abilities and antibacterial properties of root canal sealers and intracanal medicaments between appointments have been recognized as important factors for the success of endodontic treatment. Background: Apical periodontitis (AP) is an inflammatory disease around the apex of a tooth root. The microorganisms reach the pub by dental tubules especially when there is an open cavity after a coronal fracture and the pub is in contact with the septic oral environment. The aim of the study was to evaluate the dynamics of healing by recording periapical index (PAI), after two appointment endodontic procedure with commercial or experimental intracanal medicament. Methods: A total of 40 patients with primary chronic apical periodontitis requiring root canal treatment were assigned randomly into four groups according to the teeth medicated with dehydrated plant extract, calcium hydroxide, calcium hydroxide mixed with chlorohexidine (CHX) gel 2%, Walkhoff paste and obturated on a second visit, 7 days later. Patients were recalled at intervals of 3, 6, and 12 months to evaluate the treated teeth both clinically and radiographically for periapical healing. A 5-score scale PAI was used to evaluate stages of the periapical healing on a periapical radiography using a Kodak Dental imaging software provided by the radio-imaggistic center. Results: Radiological evaluation revealed that the experimental intracanal medicament had a cumulative positive healing capacity by reducing the PAI as well as all resorbable pastes used in endodontic conventional therapy. Conclusions: The results suggest that the vegetable dry extract obtained from _Epilobium parviflorum Schirbel_ can be used as an inter-appointment medication among with the root canal filling for the positive effect on apical healing quantified by reducing the PAI.  
dry vegetable extract; periapical index score; endodontic treatment
## 4 The Components of FAGD-Net
### Feature Fusion Pyramidal Module  
The encoder progressively deepens the network, extracting higher-level semantic information. However, this intensification accompanies a reduction in feature resolution, potentially neglecting and gradually losing certain spatial features. Consequently, crucial grasp-related information may be compromised during the up-sampling stage, limiting the overall quality of grasp detection. To tackle this challenge, [PERSON] et al. [29] effectively reduced feature information loss by employing spatial pyramid pooling. Nevertheless, a large dilation rate (rate = 6, 12, 18) in this context may result in suboptimal information utilization. To overcome these concerns, we introduce the Feature-Augmented Pyramid Module (FAPM), strategically designed with a small dilation rate. The FAPM acts as a crucial bridge between the encoder and decoder, effectively mitigating the aforementioned issues and contributing to improved grasp detection accuracy.  
As illustrated in Figure 4, the Feature Fusion Pyramid Module (FFPM) incorporates a multi-scale feature extraction process. Initially, the features are input into four parallel feature pyramids, each utilizing atrous convolution with different dilation rates of 1, 3, 3, and 6, respectively. This unique structure enhances information utilization while maintaining a broad receptive field. To enable comprehensive feature interaction, the extracted features from the pyramids are concatenated and added together. This process ensures a holistic representation of features across different scales. Subsequently, feature extraction is finalized through a convolution module coupled with a skip connection. This convolution module comprises a 3 \(\times\) 3 convolution operation, batch processing, and Rectified Linear Unit (ReLU) nonlinear activation. The intricate design of the FFPM promotes effective multi-scale feature extraction, fostering enhanced information integration within the grasping network.
## 4 Optimization of the IWOLA + FFT Algorithm
### Performance Comparison between the IWOLA + WOLA and IWOLA + FFT Algorithms  
To demonstrate the advantages of the IWOLA+WOLA algorithm, a signal with a frequency of 45 MHz was inputted into the IWOLA + WOLA structure, IWOLA + FFT structure, where \(K\) = 1024, \(K_{1}\) = 2048, \(f_{\rm s}\) = 800 MHz, \(D\) = 8, and the frequency resolution was approximately 763 Hz. The spectral leakage characteristics were then plotted, as shown in Figure 14. As can be observed from the figure, the IWOLA + WOLA algorithm  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Bandwidth = \(f_{\rm s}\)/(D * \(D_{1}\))**} \\ \hline  & & & \multicolumn{6}{c}{**Each Sub-Band (Complex Signal)**} \\ \hline Before re-order & 0 & 1 & \(\ldots\) & \(K_{1}/2-1\) & \(K_{1}/2\) & \(K_{1}/2+1\) & \(\ldots\) & \(K_{1}-1\) \\ \hline After re-order & 0 & \(K_{1}-1\) & \(\ldots\) & \(K_{1}/2+1\) & \(K_{1}/2\) & \(K_{1}/2-1\) & \(\ldots\) & \(1\) \\ \hline \hline \end{tabularfeaturered a spectral leakage less than that of the IWOLA + FFT algorithm by a factor of  
To analyse the SNRs of the IWOLA + WOLA and IWOLA + FFT algorithms, an input LFM signal with a frequency of 160 MHz, bandwidth of 80 MHz, and SNR of 30 dB was inputted to both algorithms, where \(K\) = 1024, \(K_{1}\) = 2048, \(f_{\rm s}\) = 800 MHz, \(D\) = 8, and the frequency resolution was approximately 763 Hz. The SNR performance was plotted as shown in Figure 15. As can be observed from the figure, the IWOLA + WOLA structure demonstrated an SNR of 31.27 dB, which was superior to that of the IWOLA + FFT structure (24.65 dB).  
Furthermore, we analysed the channelised gain flatness characteristics of the IWOLA + WOLA and the IWOLA + FFT algorithms by scanning the spectrum from one sub-channel centre to an adjacent sub-channel centre using 31 sinusoidal signals. The channelised gain flatness characteristics are shown in Figure 16. The IWOLA + WOLA algorithm demonstrated a fluctuation range of approximately 0.12, whereas the IWOLA + FFT algorithm demonstrated a fluctuation range of approximately 0.32. Thus, the IWOLA + WOLA algorithm featured a fluctuation range that was smaller than that of the IWOLA + FFT algorithm.  
Figure 14: Comparison of the spectral leakage.  
Figure 15: The SNR comparison. The SNR for the IWOLA + WOLA algorithm was 6.62 dB higher than that of the IWOLA + FFT algorithm.  
Based on the analysis results above, although the IWOLA + WOLA algorithm uses a smaller two-stage FFT with high computation efficiency instead of an ultra-large FFT, which is similar to the IWOLA + FFT algorithm; the IWOLA + WOLA demonstrated a superior performance to the IWOLA + FFT algorithm and direct FFT algorithm.