esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### SM-TCNNET Model
#### 3.2.2 Training and Testing  
How well a model performs and trains depends on having a sufficient amount of data variation and choosing appropriate hyperparameters. The selection of hyperparameters implies choosing the number of epochs, batch size, activation function, and learning rate, among others. Hyperparameter selection is used in the training phase, while performance evaluation involves using the validation set. We empirically selected some possible ranges of hyperparameter values and used a grid search method to exhaust different hyperparameter combinations to evaluate their performance. Through methods such as cross-validation, we selected the best-performing hyperparameter combination as the final hyperparameter value. Therefore, we used the following hyperparameters for the self-picked dataset in the training phase: n_epochs = 200, batch size = 30, and learning rate \(\alpha\) = 1 \(\times\) 10\({}^{-4}\). The StanWiFi dataset uses the following hyperparameters: n_epochs = 1500, batch size = 200, and learning rate \(\alpha\) = 1 \(\times\) 10\({}^{-4}\). An important issue in the training phase  
Figure 8: Convolution comparison: (**a**) standard CNN convolution; (**b**) dilated convolution.  
was overfitting, where we applied the Adam optimizer to minimize the risk of overfitting and used a softmax-based cross-entropy loss function. It directly softmax transforms the input and then calculates the cross-entropy loss between the softmax-transformed probability distribution and the true labels, thus avoiding the problem of performing the softmax transformation manually. The mathematical relationship is shown in Equation (6). In addition, we used four-fold cross-validation on the self-picked dataset and ten-fold cross-validation on the StanWiFi dataset, using cross-validation to better evaluate the generalization ability and stability of the model.  
\[loss=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{C}y_{i,j}\log\ (\frac{exp\big{(}z_{i,j} \big{)}}{\sum_{k=1}^{C}exp\big{(}z_{i,k}\big{)}}) \tag{6}\]  
The training was followed by a test and evaluation phase. We use well-known performance evaluation criteria, namely accuracy, F1-score, precision, and recall [34], to measure the recognition performance of the proposed system.
# Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation
## 1 Introduction
By selecting a proper resampling frequency, the accuracy of EEMD has been further increased. The vibration data were decomposed into several IMFs by means of improved EEMD. Then, the fault-related signal was extracted by selecting the IMF with the largest kurtosis [11; 19]. After fault-related signal extraction, fault information should normally be identified to provide guidance for maintenance. However, in the diagnosis of a gearbox, the amplitude modulation occurs in a measured signal, which means the fault information cannot be obtained by spectrum analysis directly. The phenomenon of amplitude modulation occurs because a high-frequency carrier signal is varied by a low-frequency modulating signal. The modulating signal results from the impacts caused by defects of a bearing or gear impulses appearing every time the tooth or rolling element crosses the defected area, which leads to amplitude modulation [20; 21]. To deal with this phenomenon, Hilbert square demodulation (HSD) techniques are introduced. HSD can derive the low-frequency modulating signal from the modulated signal. Lastly, spectrum analysis is applied on the demodulated signal, and fault-related information is obtained. In this paper, a novel hybrid method based on EEMD and HSD is presented for wind turbine gearbox fault diagnosis. The paper is organized as follows. The review of EEMD and Hilbert square demodulation is presented in Section 2. The proposed method for gearbox fault diagnosis is discussed in Section 3. The experimental and practical validations are presented in Section 4. A discussion and a conclusion are given in Section 5.
# Social Media and Digital Inequity: Reducing Health Inequities by Closing the Digital Divide
## 1 Introduction  
Social media has become a prominent feature in the daily lives of billions of people across the globe. At the end of 2021, there were more than 4.26 billion social media users worldwide, with another 1.75 billion users expected by 2027 [1]. Leading social media platforms have billions of active monthly users: Facebook (2.9 billion), YouTube (2.6 billion), WhatsApp (2.0 billion), Instagram (1.5 billion), WeChat/Weixin (1.3 billion), and TikTok (1.0 billion) [2]. Social media use has significantly expanded in low- and middle-income nation in recent years, where regular use has increased from just 34% of people in 2013 to 53% in 2018 [3]. Over the last two years, Africa has experienced a 13% year-on-year growth in the number of people using the internet [4]. As social media's reach has grown globally, it has interacted profoundly with public health--both positively and negatively.  
Social media has caused serious harm to mental health and perpetuated the rampant spread of misinformation. Widespread misinformation has fueled vaccine hesitancy among social media users and led to attacks on physicians and health officials [5]. These adverse effects have been exacerbated by social media's market-driven epidemic dynamic of denying evident harms, resisting mitigation efforts, and persisting in the use of addictive algorithms that amply these harms [6].  
Social media has also proven itself to be an invaluable public health tool, offering the rapid dissemination of public health information, expanded access to health resources, and other benefits for the public, patients, healthcare providers, and policy-makers. And recent work has focused on strategies for maximizing social media's benefits while mitigating the harms [5]. However, emerging evidence indicates that these benefits are being inequitably distributed, creating a \"digital divide\" of unequal access to and use of information and communication technologies [7].  
To address this new health challenge, we summarize the central role of social media for public health, discuss the emerging health benefits of digital access, define the magnitude of the digital divide, and present strategies for reducing potentially preventable health inequities arising from the existing measurable digital inequalities [8].
# Application of Laser-Ultrasonics for Evaluating Textures and Anisotropy
## 3 Results and Discussion
### Quenched Steel  
The cylindrical specimens of quenched martensitic steel were tempered for 1 h at temperatures up to 650 \({}^{\circ}\)C before measuring the wave velocities at room temperature. Figure 12 shows the resulting values through two complete revolutions of the samples. In general, the accuracy of ultrasonic wave velocities is mostly limited by the length of the wave path since time measurements are very exact. Due to the extremely constant path lengths in these lathe turned specimens, the precision of the values is very high and, from repeated measurements, can be judged to be significantly better than one part in ten thousand. The degree of anisotropy in individual specimens is only about 0.08%, and the repeatability is around 0.005%, as seen in Figure 12. In addition to these rotational measurements, P-velocities were also measured with high precision along the cylinder axis, i.e., along the plate's normal direction.  
Figure 11: Longitudinal wave velocity as a function of polar angle for azimuthal values of 0\({}^{\circ}\) (**above**) and 90\({}^{\circ}\) (**below**).  
A greater source of variation is actually the metallurgical condition since the wave velocity increases with tempering temperature and hence with strength level. From the as-quenched state to 650 \({}^{\circ}\)C, it increases by about 0.6%. This variation is in accord with other observations on the velocity of high strength steels [28] and also on variations of [PERSON]'s modulus [29]. The average wave velocity plotted against Vickers hardness, in Figure 13, agrees with measurements on other steels made in the authors' laboratory [28]. There still appears to be no adequate explanation of this relationship between elastic strength and plastic strength. Calculations have been published of the elasticity of martensite based on atomic interactions [30; 31], but these suffer from the assumption that the martensite has a tetragonal crystal structure, which is not the case for low carbon contents such as this (0.3% C), which are mainly cubic. Possibly, the high internal stresses in martensite are responsible in some way. Some new information is provided by the anisotropy data shown by the orange line in Figure 13. Due to the impossibility of making single crystals of martensite, the crystal elasticity coefficients have never been measured. Although the present results contain significant scatter, there appears to be a general decrease in the anisotropy as hardness reduces. The softest condition is expected to correspond most closely to pure iron when the steel's carbon is fully precipitated as iron carbide. It appears, therefore, that the crystal anisotropy of martensite is slightly greater than that of pure bcc iron. It should be emphasised again that no change in the crystallographic texture occurs with tempering so the changes observed here must originate from variation in the steel's fundamental elastic behaviour.  
Figure 12: The measured longitudinal wave velocity as a function of azimuthal angle for the as received and tempered martensite materials.
# A General Euler Angle Error Model of Strapdown Inertial Navigation Systems
## 2 The Proposed General Euler Angle Error Model
### Modeling of Relative Euler Angle Errors
\begin{array}{ccc}0&-\sin(\gamma^{c})\omega^{b}_{nbz}+ \cos(\gamma^{c})\omega^{b}_{nbz}&0\\ \sec^{2}(\theta^{c})\big{(}\sin(\gamma^{c})\omega^{b}_{nbz}-\cos(\gamma^{c}) \omega^{b}_{nbz}\big{)}&\tan(\theta^{c})\cos(\gamma^{c})\omega^{b}_{nbz}+\tan (\theta^{c})\sin(\gamma^{c})\omega^{b}_{nbz}&0\\ \sec^{2}(\theta^{c})sin(\theta^{c})\big{(}-\sin(\gamma^{c})\omega^{b}_{nbz}+ \cos(\gamma^{c})\omega^{b}_{nbz}\big{)}&-\sec(\theta^{c})\big{(}\cos(\gamma^{c })\omega^{b}_{nbz}+\sin(\gamma^{c})\omega^{b}_{nbz}\big{)}&0\end{array} \right] \left[ \begin{array}{c}\delta\theta\\ \delta\gamma\\ \delta\varphi\end{array} \right]+\] \[\left\{ \begin{array}{ccc}\cos(\theta)\cos(\gamma^{c})&0&\cos( \theta^{c})\sin(\gamma^{c})\\ \sin(\theta^{c})\sin(\gamma^{c})&\cos(\theta^{c})&-\sin(\theta^{c})\cos(\gamma^{c })\\ -\sin(\gamma^{c})&0&\cos(\gamma^{c})\end{array} \right] \tag{43}\]  
According to Equation (43), the general Euler angle errors can be calculated by introducing the integral operation:  
\[\left[ \begin{array}{c}\delta\theta\\ \delta\gamma\\ \delta\varphi\end{array} \right]=\left[ \begin{array}{c}\delta\theta_{0}+\int_{ \eta\phi}^{t}\delta\dot{\theta}dt\\ \delta\gamma_{0}+\int_{\eta\phi}^{t}\delta\gamma dt\\ \delta\varphi_{0}+\int_{\eta\phi}^{t}\delta\dot{\varphi}dt\end{array} \right] \tag{44}\]  
The singularity of the Euler angle error method can be avoided by different Euler angle rotation orders. Similarly, the differential equation of general Euler angle errors and conclusions can be obtained.
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
## 3 Results and Discussion
### Micro-Mechanism of EICP-Reinforced Sand
#### 3.2.5 Micro-Mechanism of EICP Reinforcement  
Based on the UCS test results and the microscale analysis, the micro-mechanisms underlying the macro-phenomena of strength improvement by EICP can be outlined. Firstly, the morphology of the crystals influences their cementation patterns. Small, discrete crystals (Figure 10) are insufficient for bonding soil particles but tend to fill smaller pores, encapsulate particle surfaces, and bond particles at contact points. In contrast, larger, aggregated crystals can bond soil particles, fill larger voids, and bridge separated particles. Secondly, the calcium carbonate aggregates formed in the large pores of the soil can subdivide the large pores into several smaller ones and split the connected pores into isolated pores. This reduces the formation of fine fracture surfaces during the loading process, thereby increasing the overall strength of the soil. Finally, from a fractal dimension perspective, the improvement in sand strength through EICP is attributed to the increased irregularity and toughness of the pore structure, induced by calcium carbonate precipitation. This pattern, characterized by the fractal dimension, shows a positive correlation within the scope of this study.
# A Magnetic-Balanced Inductive Link for the Simultaneous Uplink Data and Power Telemetry
## 2 Magnetic-Balanced Inductive Link System  
### System Overview  
Figure 2 shows the block diagram of the magnetic-balanced inductive link system. This system is composed of a WPT subsystem (pink blocks), an uplink subsystem (green blocks) and three coils (the external power coil \(L_{1}\), the external data coil \(L_{3}\) and the implant coil \(L_{2}\)). The external power coil (\(L_{1}\)) is used for feeding the wireless power, and the external data coil (\(L_{3}\)) is used for the uplink data receiving, while the implant coil (\(L_{2}\)) is used for both the wireless power harvesting and the uplink data transmitting. Dual carriers (i.e., power carrier and uplink data carrier) are used for the power and uplink data transmissions, respectively. The uplink data are modulated on the uplink carrier using binary phase shift keying (BPSK). The power carrier operates at 2 MHz (\(f_{\text{p}}\)) in consideration of less energy loss on biological tissues [20]. The uplink carrier operates at 500 kHz (\(f_{\text{ul}}\)). It is \(\frac{1}{4}\)-times lower than \(f_{\text{p}}\) to leave a sufficient transition band span for filtering out the power carrier interference before the uplink demodulation. By the way, the downlink data (which are transmitted from the external to the implant) could be modulated on the power carrier. As there is no interference problem between the power link and the downlink, the downlink is not discussed in this paper.  
As seen in Figure 2, the power feeding unit drives the external power coil (\(L_{1}\)) to generate the power carrier, and the power recovery unit receives the power carrier from the implant coil (\(L_{2}\)). At the same time, the implant transmitter drives the same implant coil to transmit the modulated uplink carrier through two isolation resistors (\(R_{\text{isolate+}}\) and \(R_{\text{isolate-}}\)). \(R_{\text{isolate+}}\) and \(R_{\text{isolate-}}\) are designed to be high impedance to isolate the implant transmitter from the power recovery unit circuits for minimizing the impacts of the uplink data transmission on the WPT. The external receiver acquires the uplink signal from the external data coil (\(L_{3}\)). The external receiving filter (blue block) further blocks the power carrier and enhances the uplink signal. Then, the clean uplink signal is fed to the uplink demodulator (blue block). Note that \(L_{1}\) and \(L_{2}\) both resonate at the power carrier frequency (\(f_{\text{p}}\)) with  
Figure 1: Application scenario of the intraocular sensor system for glaucoma treatment.  
their tuning capacitors \(C_{1}\) and \(C_{2}\), respectively, in order to derive high PTE. \(L_{3}\) resonates at the uplink carrier frequency (\(f_{\rm ul}\)) with its tuning capacitor \(C_{3}\) to increase the link gain of the uplink.
# Electrophysical Method for Identifying the Causes of Excessive Hydro Generator Vibration
## 4 Evaluation of Torsional and Tangential Vibrations
For the ULF current component with and amplitude of 100 A and frequency of 1.2-1.5 Hz, the linear tangential force \(T_{0}\) is 1340 N/m and the amplitude of the radial force \(F_{0}\) is 2080 N/m. When surge currents with amplitude of 500-750 A are superimposed, the force impacts increase significantly. The linear tangential force \(T_{0}\) reaches 5040 N/m and the amplitude of the radial force \(F_{0}\) reaches 7800 N/m. The radial and tangential displacements increase accordingly. The surge displacement values are presented in Table 2.  
The impact considered has the following features. The superimposition of the ULF component results in a slight vibration increase. However, there is a frequency shift in mechanical impacts that create unwanted frequencies and danger of their falling into the near-resonance zone. The current frequency shift is \(\pm\)1.2-1.5 Hz; resulting in 48.5-49 Hz and 51.2-51 Hz magnetic fields being generated in the machine. The interaction with the field of fundamental frequency causes mechanical force emergence in the 98-102 Hz frequency range. Under near-resonance conditions, the vibration and surge displacement values \(2W_{1}\), \(2U_{1}\) increase significantly. If the natural frequency of the basket-type end-winding parts is beyond the 86-113 Hz range, the relative tangential vibration of the end-winding parts should not exceed 15-30 \(\upmu\)m under imbalanced conditions with the ULF components of current loads. The surge current superimposition has a short-term nature. However, the displacement value reaches 35 \(\upmu\)m in the radial direction and 117 \(\upmu\)m in the tangential one, i.e., both exceed the acceptable level. As for hydro generators with a composite core, the core vibration should not exceed 30 \(\upmu\)m under balanced load conditions according to [7]. In addition, the recordings have shown such impacts to be of intense nature and able to repeat several times a minute. This contributes to the reduction in the hydraulic turbines' service life and confirms the cause of increased vibration noted during inspection and repair tests performed at the hydropower plant.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### The Re-Encryption Processes
#### 4.3.5 Data Sharing to a Third Party  
The EHR may be requested by a third party, such as other healthcare institutes, pharmaceutical companies, and insurance institutes. During this process, re-encryption is performed because the encrypted symmetric key should be delivered to the third party. The processes involved are as follows:  
1. When the server receives a request to access the stored EHR from the third party \(usr_{t}\), the server verifies \(usr_{t}\)'s accessibility from the blockchain. If \(usr_{t}\) does not have the right to access the EHR, then the server rejects \(usr_{t}\);
2. The server obtains the requested pair of the encrypted data \(\mathcal{C}\) and symmetric key \(\mathcal{E}=(D,\ E,\ F,\ s)\), and sends \(\mathcal{C}\) to the third party and \(\mathcal{E}\) to the proxy server;
3. When the proxy server receives \(\mathcal{E}\), it finds \(\mathtt{rk}_{M\to t}=(rk_{M\to t}^{<1>},\ V,\ W)\) from its storage;
4. The proxy server calculates \(E^{\prime}=E^{rk_{M\to t}^{<1>}}\) and sends \((E^{\prime},\ F,\ V,\ W)\) to the third party.  
After this process, the third party receives the ciphertext of the EHR and the symmetric key that is encrypted with the first level of the PRE.
The sum of these issues makes the use of biomass for energy production often difficult to implement, since the costs associated with all the ancillary operations make the process unfeasible, making it imperative to study variables associated with the logistics process, in addition to variables associated with energy recovery processes [16]. There are several forms of biomass resulting from commercial agroforestry businesses, such as the production of coconut, sugarcane, cashew nuts, or palm oil, which deliver a steady supply of waste that, in the absence of use, can turn into environmental problems through the emission of greenhouse gases (GHGs), either by their combustion to eliminate the materials, or by rotting due to lack of destination or form of recovery [17]. Thus, the possibility of transporting these materials to a destination where they can be valorized should be a very interesting possibility, were it not for the previously mentioned problems related to the logistical costs associated with these materials [18]. The scale of food processing facilities is generally not large enough to serve as an \"end use point\" for utility scale production, but it is often of sufficient scale to serve as a location for preprocessing prior to shipment to a larger, centralized conversion facility. The possibility of materials being subjected to energy densification processes, such as thermochemical conversion processes, eliminating moisture and volatile compounds with low heating value, is associated with a densification process, such as pelletization or briquetting, which can transform waste with little value into readily transportable energy products, capable of functioning as an alternative to coal. This is mainly of the most common types with widespread commercial use, such as sub-bituminous and bituminous coals [19]. Another advantage is that those energy value-added products can be feedstock used directly in coal-fired power plants, without the need for major changes, since they have similar physico-chemical properties with regard to heating value and grindability [20]. In the above context, the aim of the present study was to characterize the properties of four widely used residual biomass feedstocks of agroforestry origin, namely coconut shells, sugarcane bagasse, palm kernel shells (PKS), and cashew nutshells, which are abundant in tropical and subtropical regions, and constitute a reserve with the potential to supply countries that are currently replacing coal as a way to reduce greenhouse gases (GHGs) emissions. Based on the results obtained, it was intended to demonstrate that the energy densification of these materials allows the reduction of logistical costs, and, thus, their transport to places of final use away from the points of production.
## 6 Experiment Results
### Simulation Settings
In our simulation environment, the task processing system is implemented as a discrete event simulation (DES) model. Each edge server maintains its own task queue and processes tasks using the First-Come-First-Served (FCFS) rule. While we do not strictly assume Poisson arrivals or exponential service times, the queuing dynamics of each server share similarities with the M/M/1 queuing model. Therefore, our system can be viewed as a parallel set of DES-modeled single-server queues, reflecting the task offloading and scheduling process in edge computing for vehicular networks.