esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Methods for Speaker Recognition
#### 2.1.5 Loss Function
The operation process of BLSTM is divided into forward LSTM and reverse LSTM, a combination of the bidirectional two-layer LSTM. In Figure 10, the x-sequence input to the BLSTM is passed into the forward LSTM and the reverse LSTM, and the predicted values of the two LSTMs are combined as the output \(y\)-sequence. If a single-direction LSTM architecture is used, the information volume of the LSTM can be increased only in a forward manner. The advantage of BLSTM is that it can obtain two-way information, thereby expanding the perception field of the model. Therefore, BLSTM can achieve better performance than general LSTM. Bidirectional Long Short-Term Memory with Mel-Frequency Cepstral Coefficients and Autoencoder Features for Triplet Loss (BLSTM-MFCCAE-TL) Method  
A pre-trained autoencoder was employed to encode the voices of speakers, which achieved better results in word recognition [16]. Based on a given success illustration, employing the same autoencoder architecture in speaker recognition can also improve the accuracy. We use a pre-trained autoencoder to encode MFCC features into AE features for speaker encoding. Therefore, in our proposed BLSTM-MFCCAE-TL method, we achieved better accuracy in speaker recognition by combining MFCC with AE features. We refer to the model architecture of [16] to construct the GMM-HMM-MFCCAE method for speaker recognition. Because the architecture in [16] is used for denoising word recognition, the correct rate for speaker recognition is 64.48%, which is low. The speaker recognition process of the proposed BLSTM-MFCCAE-TL method is shown in Figure 11. We divided the BLSTM-MFCCAE-TL method into six steps: MFCC feature extraction, feature preprocessing, AE modeling, cluster training, BLSTM modeling, and loss function. In the third step of the BLSTM-MFCCAE-TL method, we refer to [16] to construct a pretrained AE model and combine the AE features output by the AE model with the MFCC features and input them to the BLSTM model in the fifth step of training. The third and fifth steps are described in detail below. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Size** & **Information** \\ \hline Input & (512, 39) & \\ BLSTM & (512, 256) & Units: 128 \\ Dropout & (512, 256) & Drop: 0.3 \\ Flatten & (1, 131,072) & \\ Dense (Output) & (1, 128) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: BLSTM model parameter table in BLSTM-MFCC-TL method. Figure 10: Schematic diagram of the BLSTM architecture.
# Scaling of Rain Attenuation Models: A Survey
## 2 Taxonomy  
The current approaches of rain attenuation can be categorized into scaling microwave links in clear sky conditions and the scaling of rain attenuation techniques in rainy conditions. The scaling of the rain attenuation of the radio wave link operated within several gigahertz bands can also be categorized into four types of scaling techniques currently comprehended. These are the elevation angle scaling, frequency scaling, pathlength scaling, and polarization scaling. Frequency scaling can be classified into two divisions depending on the attenuation ratio of two different frequencies and nonratio techniques. The ratio-based methods can be further divided into three kinds, and two types can be divided into nonratio-based methods, as shown in Figure 1. Frequency-based techniques may also be categorized into the statistical attenuation ratio (SAR)-based model-generated attenuation and the instantaneous approach, and the nonratio frequency-based approach can be classified into the empirical formula and instantaneous scaling schemes. In addition, the third form of frequency scaling approach known as the \"composite model\" is conceivable, which is based on comparing the attenuation of individual parts to the overall attenuation.
###### Abstract  
Cryo-electron microscopy (cryo-EM) is a powerful tool for imaging biological samples but is typically limited by sample thickness, which is restricted to a few hundred nanometers depending on the electron energy. However, there is a growing need for imaging techniques capable of studying biological samples up to 10 \(\upmu\)m in thickness while maintaining nanoscale resolution. This need motivates the use of mega-electron-volt scanning transmission electron microscopy (MeV-STEM), which leverages the high penetration power of MeV electrons to generate high-resolution images of thicker samples. In this study, we employ Monte Carlo simulations to model electron-sample interactions and explore the signal decay of imaging electrons through thick specimens. By incorporating material properties, interaction cross-sections for energy loss, and experimental parameters, we investigate the relationship between the incident and transmitted beam intensities. Key factors such as detector collection angle, convergence semi-angle, and the material properties of samples were analyzed. Our results demonstrate that the relationship between incident and transmitted beam intensities follows the Beer-Lambert law over thicknesses ranging from a few microns to several tens of microns, depending on material composition, electron energy, and collection angles. The linear depth of silicon dioxide reaches 3.9 \(\upmu\)m at 3 MeV, about 6 times higher than that at 300 keV. Meanwhile, the linear depth of amorphous ice reaches 17.9 \(\upmu\)m at 3 MeV, approximately 11.5 times higher than that at 300 keV. These findings are crucial for advancing the study of thick biological and semiconductor samples using MeV-STEM.  
Monte Carlo simulation; MeV-STEM; Beer Lambert law; electron-specimen interaction; nanometer resolution; thick biological samples 2020 IEEE/MME
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Security Challenges in UIoT
#### 4.2.4 Methods to Prevent Sybil Attacks  
Message authentication and proper localization mechanisms are necessary to prevent the Sybil attack in the UIoT environment. The existing Sybil attack prevention methods applicable for UIoT networks are explained herewith. In [46], [PERSON] et al. proposed the received signal strength indicator (RSSI) based light-weight approach to detect the Sybil attack; this approach can be applicable in UIoT networks. In [47], [PERSON] et al. proposed a pairwise random key predistribution scheme to secure the communication link that can be used for UIoT networks. Resource-based testing is one of the solutions for Sybil attack prevention in UIoT networks. In [48], [PERSON] et al. provide an example of resource-based testing. This method can be used in UIoT.
# Research on a Mongolian Text to Speech Model Based on Ghost and ILPCnet
## 1 Introduction
As a typical low-resource language, Mongolian is also one of China's minority languages and is widely used in the Inner Mongolia Autonomous Region. It has a long history and rich cultural heritage. Due to the limited technical resources that can be used for reference in Mongolian information work at home and abroad, the development of its information processing technology lags. In order to promote intelligent life in areas where Mongolian is the main language, experts and scholars have performed many active explorations and practices. In 2017, [PERSON] et al. applied the acoustic model based on deep neural network technology to Mongolian speech synthesis and built a complete Mongolian speech synthesis system based on the characteristics of Mongolian [11], which provided Mongolian speech synthesis. In 2019, [PERSON] proposed an end-to-end Mongolian speech synthesis model [12], which was based on an encoder-decoder structure with an attention mechanism. It used a hybrid method that combined rules and statistics to deal with the problem of converting Mongolian letters to phonemes, which effectively reduced the word error rate and phoneme error rate. In 2022, [PERSON] et al. proposed a completely non-autoregressive, real-time, high-fidelity Mongolian speech synthesis model, MonTTS [13], based on the FastSpeech2 model to further improve the efficiency and quality of Mongolian speech synthesis. The abovementioned research shows that, with the continuous expansion of Mongolian speech synthesis research, the synthesized speech quality of the speech synthesis model has been greatly improved. However, due to the characteristics of the Mongolian language and the lack of an audio corpus, the Mongolian speech synthesis model still has problems, such as a poor real-time synthesis rate, large model parameters, complex structure, mispronunciation, and missing words in the synthesized speech. This paper continues to explore Mongolian speech synthesis technology and strives to improve the quality and speed of synthesized speech. This not only promotes the development of Mongolian intelligent information processing technology, but also creates a better, more efficient, more convenient, and faster user experience. Focusing on the abovementioned issues, this paper proposes to build a Mongolian phoneme pre-training model based on Bang and a speech synthesis model suitable for traditional Mongolian, named Ghost-ILPCnet, and conducts a large number of data experiments to verify its effectiveness.
## 3 Test Results and Analysis
### Comparative Analysis of Experiment and Numerical Simulation  
During the experimental process, we applied a confining pressure of 5 MPa to weakly cemented sandstone samples and measured their permeability under various stress conditions using a permeability testing instrument. Concurrently, numerical simulations were conducted under the same stress conditions to fit the permeability of the samples.  
In the initial phase, as the confining pressure was applied, the permeability of weakly cemented sandstone exhibited a certain degree of reduction. This decrease occurred because, under the influence of the confining pressure, the pores within the rock were compressed, leading to a decrease in the effective cross-sectional area of pore channels, resulting in decreased permeability. The reduction in this stage is primarily influenced by pore structure and cementation degree.  
With further increases in stress, when a certain critical threshold is reached, the permeability of weakly cemented sandstone sharply increases. This sudden increase is attributed to the destruction of cementitious material at high stress levels, which causes  
Figure 6: Diagrammatic Representation of Joint Interstitial Flow.  
pore channels to reopen, thereby increasing the effective cross-sectional area and rapidly elevating the permeability. The permeability increase during this stage is mainly influenced by rock fracturing and damage behavior.  
Subsequently, as stress levels continue to rise, the permeability of weakly cemented sandstone tends to stabilize. In high-stress conditions, the internal pore channels of the rock have essentially reopened, and the changes in permeability reach a stable state. At this point, the rock's permeability is jointly influenced by cementitious material and pore structure.  
By comparing the experimental results under 5 MPa (As shown in Figure 7) confining pressure with the numerical simulation results, the feasibility of numerical simulations was validated. The experimental and numerical simulation results exhibit a good fit, displaying largely similar trends. This provides crucial reference and guidance for further research and the assessment of rock permeability in engineering practice.
# UAV Formation Trajectory Planning Algorithms: A Review
## 6 Problems of UAV Formation Trajectory Planning Algorithm
### Performance Problems of UAV Formation Carrying Equipment  
In the UAV formation trajectory planning algorithm, the performance of the UAV itself is not considered enough. An insufficient consideration of problems such as fuel consumption, load, and onboard sensor errors in practice makes it difficult to accurately detect complex environments, and the trajectory planning that can be achieved by simulation cannot be realized or has poor robustness in the actual environment.
## 2 Materials and Methods
### System Architecture  
For the provision of the user interface, a system architecture was proposed in which the user interacts through a dApp to access the described functionalities (see Figure 1).  
In the initial step, the user will utilize a browser to connect to the server that hosts the dApp. This connection provides a straightforward web interface, enabling interaction with both the system and the BC network. A database management system (DBMS) is included in the model to manage access to and modification of sensitive end-user data, such as personal data, that could uniquely identify the user. Additionally, the system includes support for the use of an open BC network. In particular, in this case, a pilot has been deployed using the Sepolia testnet [36]. This network, by its nature, is completely autonomous and is used for the anonymized recording of data related to user activities.  
Figure 2 illustrates a typical interaction sequence within the system. As can be observed, the sequence begins with a user logging into the system via their dApp and interacting with the web server (checkpoint 1 in Figure 2). The users input their access credentials, and the server logs them into the system. Subsequently, in this prototype, the user must select the challenge to make use of the platform (see point 2 in Figure 2). In the following interaction, the user records the actions related to the Planking challenge on the BC network through the dApp (refer to point 3 in Figure 2). This action can be repeated multiple times. Each time, the smart contract responsible is invoked to perform a series of tasks, such as issuing the event on the network, recording the operation on the BC, and checking the status of the challenge. Point 4 illustrates the process carried out; if the conditions for the challenge's success are met, a reward is issued. Finally, as depicted in point 5, the agent called \"SupportingMessageDispatcher\" is responsible for sending messages to users in order to promote user engagement, as described in [PERSON] et al. [37].  
Due to its technical nature, the data stored in BC networks is immutable, traceable, and transparent. In this way, all participants can check their own status without the possibility of fraud by potential malicious third parties. Through the application, SCs responsible for resource allocation are invoked once the challenge under consideration is completed.  
Figure 1: Representation of the system architecture.
## 4 Results and Discussion
### Validation of Satellite-Derived Products  
The accuracy of turbidity products derived from remote sensing imagery was assessed using in situ data collected at the inlet, synchronously to satellite overpasses. In situ turbidity data include both CTD profiles, acquired during field activities from 2017 to 2021, and continuous data from the fixed stations SN and TRP available from January 2020. The scatter plot in Figure 3 shows the correlation found between satellite-derived turbidity versus in situ measurements for the 199 S2-match-ups and the 96 L8-match-ups with coefficients of determination R\({}^{2}\) of 0.926 and 0.959, for S2 and L8, respectively. This dataset extends the match-up presented in [43], confirming the accuracy assessed for ACOLITE-derived turbidity retrieved from L8 and S2 in the North Adriatic coastal waters [35; 50]. In situ turbidity is slightly underestimated by S2 and slightly overestimated by L8; this can be related to the different spatial resolutions of the satellite data, that is 10 m for S2 and 30 m for L8. Overall, these results emphasize the consistency of turbidity derived from L8 and S2 for a quite large quantity of match-ups, as demonstrated in [44]. As seen for data from fixed stations (Figure 2), most of the turbidity values in the lagoon-sea transect ranged in the 2-20 FNU interval.  
Figure 2: Scatter plot of the turbidity data retrieved from the fixed stations (OBS) compared with the data from CTD probe profiles (Seapoint) at TRP (**a**) and SN (**b**). The linear regression equation and the common descriptive statistical metrics are reported in the plots: number of data pairs (N); coefficient of determination (R\({}^{2}\)); root mean square error (RMSE); and mean absolute error (MAE). Dotted lines are the best-fit regression, forced through zero. Grey solid line is the 1:1 line.
## 5 Results
### Evaluation Using Daily Observations from 2000 to 2020
#### 5.1.3 Yearly Time Scale  
In completing the previous two subsections, daily observations of each year were also used to validate the GPPs based on CC, RMSE, MBE, and CSI (see Figure 9). This analysis could provide supportive information to understand the temporal characteristics of the selected GPPs, revealing whether they had stable performances in years over the last two decades. Figure 9a displays the yearly CC values for the GPPs, clearly indicating that before 2011, either GSMaP or ERA5 had the highest CC values, while after that, GSMaP proved to be the most accurate GPP. The results also suggested the better performance of IMERG in comparison to TMPA, CHIRPS, and PERSIANN, being the third most accurate dataset after ERA5 and GSMaP. Moreover, the CC trend lines of GPPs remained stable without a considerable and significant upward/downward trend throughout the time, with the lowest and highest SD of 0.026 (ERA5) and 0.055 (PERSIANN). Likewise, Figure 9b illustrates the RMSE dynamics for the GPPs, suggesting the higher RMSE values for IMERG. In comparison, other GPPs had lower RMSE values with relatively monotonous trends during the last two decades. The results also demonstrated that the GSMaP in almost all time intervals was the GPP with the least RMSE values, closely followed by ERA5. Additionally, the trends of MBE variations were also investigated (Figure 9c), and the results show that TMPA and IMERG had extreme consistent behaviors during the study period, having the highest underestimations and overestimations, respectively. The MBE values for the other three GPPs, i.e., CHIRPS, PERSIANN, and ERA5, were stable between 2000 and 2020, varying between 0.0 and 0.5, indicating the stable overestimations of these GPPs. However, the findings demonstrated that GSMaP MBE values had a significant trend (\(p\)-value \(<\) 0.05), which was statistically confirmed by checking the randomness test. This certified the improvement in the average bias of the GSMaP that roughly started from nearly -0.5 and reached 0.04 over the last two decades. Finally, the yearly precipitation occurrence detectability of GPPs was examined using the CSI metric. The results indicated stable trends for all GPPs with low variations and the superior performance of GSMaP and ERA5 as the two first accurate GPPs, respectively.
## 2 Materials and Methods
### QCT-Based BMD Measurements  
Once the examinations were retrieved on the QCT software, an experienced operator (more than ten years in osteoporosis imaging) performed the exams using the dedicated Mindways software (QCT PRO version 6.1). The evaluation can be conducted on up to three lumbar vertebrae, ranging from L1 to L4. The software automatically identifies the vertebral region and place the regions of interest (ROIs) on the axial lumbar CT, but these ROIs can always be corrected by the operator, whenever necessary.  
The values of vBMD (mg/cm\({}^{3}\)) obtained through QCT were expressed as equivalent calibrated densities of aqueous potassium phosphate. For QCT, lumbar spine T-scores were calculated using the manufacturer's CTX reference database; these values are not equivalent to DNA T-scores and cannot be used for the World Health Organization diagnosis of osteoporosis. Therefore, the American College of Radiology defines trabecular spine BMD categories as follows: values above 120 mg/cm\({}^{3}\) are considered normal, values between 80 and 120 mg/cm\({}^{3}\) indicate osteopenia, and values below 80 mg/cm\({}^{3}\) are classified as osteoporosis [16].  
Figure 1: Mindways Software Inc. QCT Pro phantom (**left side, A**), alongside QA/calibration results (**right side, B**).
## 2 Description of the Shipboard Microgrid under Study
### Energy Storage System  
In order to keep the hybrid power system stable, energy storage devices are crucial for quickly supplying power generation subsystems that are low on energy. In this study, a battery energy storage system (BESS) and a flywheel energy storage system (FESS) were used. The BESS's time constant is limited to a few seconds because it takes some time to charge the battery cells. A voltage-controlled source, \(E\), plus an internal resistor make up the BESS model [30]. The following equations give the battery voltage and state of charge (SOC):  
\[\begin{split}& E=E_{o}-\frac{KO}{Q-\int idt}+A\exp(-B\int idt)\\ & SOC=100(1-\frac{1}{Q}\int idt)\end{split} \tag{11}\]  
where \(E\) represents the no load voltage, \(E_{0}\) is the battery constant voltage, \(Q\) is the battery's total capacity, \(A\) denotes the exponential zone amplitude, \(K\) is the polarization voltage, \(B\) is the exponential zone time constant inverse, and \(i\) represents the battery's current.  
In contrast to other ESS, such as batteries or capacitors, the FESS stores energy as kinetic energy. This unique feature enables the FESS to accumulate excess energy during low-demand periods and deliver it instantly during peak periods when there is high energy demand. The FESS uses the stored kinetic energy to quickly spin up a rotor, which generates electricity that can be supplied to the grid. Under normal conditions, the motor is employed to convert electrical energy from the power system into kinetic energy, which is then stored in the rotating flywheel. When the electrical load demand increases or an emergency arises, the stored kinetic energy is converted back into electrical energy by the generator and supplied to the connected electrical load. As shown next, the transfer functions of the BESS and FESS can each be represented as a first-order lag [46], i.e.,  
\[G_{FESS}(s)=\Big{(}\frac{1}{1+T_{FESS}s}\Big{)}\quad,\quad G_{BESS}(s)=\Big{(} \frac{1}{1+T_{BESS}s}\Big{)} \tag{12}\]  
Figure 2: Electrical equivalent circuit of a PV cell [45].  
Based on the above, and as Figure 1 illustrates, the whole transfer function model of an SMG, the frequency deviation can now be expressed as  
\[\Delta f=(\Delta P_{\text{DG}}+\Delta P_{\text{FC}}+\Delta P_{\text{SW}}+\Delta P_ {\text{PV}}-\Delta P_{\text{FESS}}-\Delta P_{\text{BESS}}-\Delta P_{\text{load} }-D\cdot\Delta f)\big{/}2H \tag{13}\]